Finels frm Microalcme: 

Technobgy %tug Btmtial, and 

Research Requirements 
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SERIISP-231-2550 

DE86OlO739 

UC Category: 61c 

Fuels from Microalgae: 

Technology Status, 

Potential, and Research 

Requirements 

Bernie Neenan 

Daniel Feinberg 

Andrew Hill 

Robins Mclntosh 

Ken Terry 

August 1986 

Prepared under Task No. 4513.20 

FTP No. 513 

Solar Energy Research Institute 

A Division of Midwest Research Institute 

161 7 Cole Boulevard 

Golden, Colorado 80401 -3393 

Prepared for the 

U.S. Department of Energy 

Contract No. DE-AC02-83CH10093 
sew 

PREFACE 

This study was conducted at the request of the Department of Energy, Biomass 

Technology Division, to provide a comprehensive overview of the concept of fuels from 

microalgae. The scope of the study is limited to evaluating alternative fuels from mass 

cultured microalgae in the southwestern United States, the basis for current program 

activities. This approach focuses on the feasibility of producing fuel products from 

microalgae and does not specifically address application of the technology to other ends. 

The authors acknowledge the contributions of Sid Browne and Art Power to the 

development and execution of this study. The strong and enlightened guidance of 

Sid Browne through the most demanding and formative stages of this project contributed 

immeasurably to the final report. Art Power provided insight and support to the 

engineering appraisals and resource evaluations critical to the overall technology 

evaluation. We also extend our appreciation to Stan Bull for his guidance, unwavering 

support, and considerable patience, and to Bill garclay for timely review and technical 

guidance. Editorial and administrative assktance were generously provided by 

JoAnn Smith and Shirley Valdez. 

Finally, an integral part of this assessment was the contribution of a dedicated and 

knowledgeable team of consultants who reviewed the input parameters, provided insight 

on the complexities of algal culture, and pointed out issues that otherwise might have 

gone unnoticed. The authors, while retaining responsibility for the content of the report, 

acknowledge the contributions of D. Brune, J. Dodd, G. Eskamani, K. Foster, C. Hinman, 

J. Huguenin, R. Krauss, T. Mumford, E. Pryde, and R. Young. 

Approved for 

Solar Energy Research Institute 

Solar Fuels Research Division 
EXECUTIVE SUMMAH Y 

This technology asscssrrwn t dcr~lonstrnlrs that gas01 lrw md (lir*u~l ftcrls could be produced 

from mass-cultured microalgac .t t pric-es that will Iw t ol~~l)r* I kt ivr with conventional 

fuels. Aggressive research is nrrdcxd to f~~lfill 1l1c 1,t.r !ort~~.rll( tb t quircnlents defined by 

the analysis, but the required irllprovct~tcmt\ .ire. WI~~IIII tlw I~t\r~t~ds of attainability and 

have been closely approached undtv cot) trul lvd t.r)~d~ t lollr. Irrq)rovrtr~cnf s need to be 

made in the productivity, lipid yield, and seltr~ity .u,d trrt\pc.riiture tolerance of 

microalgae species. Engineering improvements are also needed in t tw cu i t iva t ion system 

design and in harvesting. Two critical resource parameters are the availability of saline 

waters in the Southwest deserts and the cost of carbon dioxide. Based on the 

achievement of these research goals and economic availability of the critical resources, 

liquid fuels that are potential direct substitutes for conventional hydrocarbon fuels can 

be produced from rnicroalgae for $1.60-$2.00/gallon by the year 2010. 

In 1979, DOE and SERI initiated a research program to pursue opportunities for producing 

liquid fuels from microalgae. Microalgae are unique photosynthetic organisms in that 

they are known to accumulate storage lipids in great quantities and will thrive in high 

salinity water. The DOE/SERI program emphasizes the development of microalgae 

systems in the desert Southwest because this area offers flat land, high incident solar 

radiation, few competing land uses, and large reservoirs of saline water. Thus, locating 

the mass culture facility in this region minimizes land costs, while the use of saline 

water, not suitable for agricultural, domestic, or industrial purposes, minimizes competition with other uses for the limited supplies of fresh water in the Southwest. Finally, 

the program is focused on the production of lipids from microalgae because plant storage 

lipids could be among the best biomass feedstocks for producing renewable, high-energy 

liquid fuels such as gasoline and diesel fuel. 

To assess the feasibility of producing fuels from microalgae, four major questions were 

answered in this evaluation. 

1. Which fuel products are the best candidates for production from microalgae? 

2. Given those fuel products, what improvements in the microalgae cultivation 

systems are required to achieve greater cost effectiveness? 

3. Given the microalgae feedstock characteristics, what improvements in fuel 

processing are required to achieve cost effectiveness? 

4. Given the required improvements in the mass culture system and fuel processing, what are the implications for a research program designed to meet the 

objective? 

Analysis of a number of fuel conversion options for microalgal biomass has demonstrated 

that the promise of microalgae for fuel production is best realized through the utilization 

of conversion processes based on cellular lipids, an energy-r ich hydrocarbon. The ability 

to produce lipids that can constitute 60% or more of the total biomass is a distinguishing 

characteristic of microalgae and makes them uniquely attractive candidates for conversion to liquid fuels. The two most promising fuel conversion options are transesterification to produce fuels similar to diesel fuels and catalytic conversion to produce gasoline. While microalgal lipids represent the premium energy product, the energy trapped 

in the other biomass constituents can also be utilized; e.g., the cell residue after lipid 

extraction can be anaerobically digested for the production of methane and carbon 

dioxide. 
The availability of saline water will be an important factor in determining the ultimate 

scale of fuel production technology based on this resource. Due to high evaporation 

rates, water demands for uncovered cultures in this region will be extremely high. While 

saline aquifers are found throughout the Southwest, the total volume these aquifers can 

supply on a sustained basis has not been determined. Since very little information is 

available on the saline water resources of the desert Southwest, the quantification of this 

resource is a high priority activity. 

Carbon dioxide supply is the largest single contributor to the cost of liquid fuels derived 

from microalgae. However, the acquisition of sufficient quantities of carbon dioxide 

should not impose constraints on the ultimate scale of the microalgal fuels technology. 

Existing and proposed coal-fired power plants will produce carbon dioxide in excess of 

the quantity required for microalgal production. Competing demands from enhanced oil 

recovery (EOR) are not anticipated, since most oil fields in the a r e a a r e expected to be 

depleted by the year 2010, which is the expected date of emergence of an extensive 

microalgal mass culture technology for liquid fuel production. If methods were 

developed for the recovery of carbon dioxide previously injected into oil wells, these 

large reservoirs could supply additional abundant quantities of low-cost carbon dioxide. 

The major issues to be resolved in the mass culture technology are biological. For this 

technology to become cost competitive, the biological productivity of these systems 

must be improved. The production analysis indicates that photosynthetic efficiencies of 

12%-16% must be attained, and that 50%-60% by weight of the biomass produced must 

be in the form of lipids. If higher lipid contents were achieved, lower photosynthetic 

efficiency would be acceptable, and vice versa. 

In addition to displaying improved productivity, species must demonstrate environmental 

tolerance characteristics that make them suitable for outdoor culture in arid regions. 

The productivity analysis has identified species salinity tolerance as a particularly signif - icant aspect of environmental tolerance, since operation of cultures at high salinities is 

necessary to control water requirements through minimization of water consumption for 

blowdown. 

The basic engineering needs for microalgal culture have been identified and form the 

basis of a significant portion of the production analysis. Considerable additional 

engineering research and development will be required. Important engineering issues are: 

efficient and inexpensive harvesting methods, mechanisms to reduce evaporation, production system design, brine disposal, and carbon dioxide input systems. 
TABLE OF CONTEN'I'S 

Page 

1.0 Introduction ......................................................... 1 

1.1 Progress to Date ................................................ 1 

1.2 Technology Overview ............................................ 4 

1.3 Study Purpose and Approach ...................................... 4 

2.0 Principles of Microalgal Fuels Technology ............................... 8 

2.1 Mass Culture Technology ......................................... 8 

2.1.1 Introduction ............................................. 8 

2.1.2 Culture Water Demand and Salt Balance ..................... 8 

2.1.3 Carbon Dioxide Supply ..................................... 11 

2.1.4 Nutrient Supply .......................................... 12 

2.1.5 Algal Species for Mass Culture ............................. 14 

2.2 Design of Mass Culture Production Systems ......................... 16 

2.2.1 Growth Conditions ....................................... 16 

2.2.2 Light Utilization in Mass Cultures .......................... 19 

2.2.3 Engineering and Construction .............................. 20 

2.2.4 Harvesting .............................................. 21 

2.2.5 Summary ................................................ 23 

2.3 Resource Requirements .......................................... 23 

2.3.1 Land Requirements ....................................... 23 

2.3.2 Climate Requirements .................................... 24 

2.3.3 Water Requirements ...................................... 24 

2.3.4 Carbon Dioxide Supply and Demand ......................... 25 

2.4 Product Alternatives ............................................ 33 

2.4.1 Functional Properties ..................................... 33 

2.4.2 Overview of Fuel Product Network ......................... 35 

2.4.3 General Characteristics of Crude Microalgae Feedstock ....... 35 

2.4.4 Fuel Products from Lipids ................................. 41 

2.4.5 Other Products and Processes .............................. 45 

2.4.6 Summary ... .. ........................................... 46 

3.0 Mass Culture Economics .............................................. 48 

3.1 Process Design Economics ........................................ 48 

3.1.1 Pond Configurations and Cost Factors ....................... 48 

3.1.2 Harvesting Options ....................................... 49 

3.1.3 Production Labor ......................................... 49 

3.1.4 Energy Requirements ..................................... 49 

3.1.5 Resource Supplies ........................................ 50 
TABLE OF CONTENTS (continued) 

3.2 Analytical Method for Evaluation .................................. 51 

3.2.1 ModelDescription ........................................ 51 

3.2.2 Overview of Reference Values and Ranges for Model 

Parameters .............................................. 51 

3.2.3 Resource Parameters ..................................... 55 

3.2.4 Facility Design Parameters ................................ 59 

3.2.5 Biological Parameters .................................... 62 

3.2.6 Financial Parameters ..................................... 64 

3.2.7 Summary ................................................ 64 

3.3 Reference Case Economics ....................................... 64 

3.3.1 Cost Contribution to Reference Case ....................... 65 

3.3.2 Summary ................................................ 69 

4.0 Economics of Producing Fuels from Microalgae ........................... 71 

4.1 Estimating Algal Processing Costs ................................. 71 

4.2 Lipid-Based Conversion Routes .................................... 73 

4.2.1 Pseudo Vegetable Oil ..................................... 73 

4.2.2 Transesterification ....................................... 74 

4.2.3 Catalytic Conversion ...................*.*........... ... 74 

4.3 Nonlipid-Based Processes ......................................... 74 

4.3.1 Anaerobic Digestion ...................................... 74 

4.3.2 Ethanol ................................................. 74 

4.4 Summary of Fuel Processing Costs ................................. 75 

4.5 Fuel By-product Values .......................................... 76 

4.6 Reference Fuel Production Costs .................................. 76 

5.0 Initial Screening of Cost Contributions .................................. 86 

5.1 Resource Parameter Sensitivity ................................... 86 

5.2 Facility Design Parameter Sensitivity .............................. 87 

5.3 Biology Parameter Sensitivity ..................................... 90 

5.4 Financial Parameter Sensitivity ................................... 90 

5.5 Important Parameters for Further Evaluations ...................... 93 

6.0 Evaluation of Important Technology Issues ............................... 95 

6.1 Important Linkages and Critical Issues in Microalgae 

Feedstock Production ............................................ 96 

6.1.1 Water Demand ........................................... 98 

6.1.2 CarbonDioxide .......................................... 103 

6.1.3 Algal Productivity and Products ............................ 107 

6.1.4 Harvesting .............................................. 112 

6.2 Assessment of Fuel Product Options ............................... 113 

6.2.1 ProcessOptions .......................................... 113 

6.2.2 Screening of Fuel Option Feasibility ........................ 113 

vii 
TABLE OF CONTENTS (concluded) 

Page 

6.3 Attainability Targets for Microalgae Feedstock Production 

andFuel Processing ........................................... 116 

6.3.1 An Attainable Production Technology Configuration .......... 117 

6.3.2 Fuel Processing .......................................... 119 

6.3.3 Analysis of Feedstock Production and Fuel Processing 

Requirements ............................................ 123 

6.4 Sensitivity Analysis .............................................. 130 

6.4.1 Sensitivity of the Attainability Production Cost 

to Constraints .......................... ..... ..,, ... 130 

6.4.2 Special Attainability Cases with Low Production Costs ........ 135 

6.4.3 Lipid Sensitivity Cases .................................... 136 

6.5 Summary ....................................................... 137 

7.0 Conclusions and Recommendations .......................... ... .... ... 140 

7.1 Fuel Products from Microalgal Feedstocks .......................... 140 

7.2 Resources and Environmental Considerations ........................ 141 

7.3 Biological and Engineering Aspects of Feedstock Production .......... 142 

8.0 References ........................................................ 144 

Appendix A Microalgae Production Model .................................... 149 
LIST OF FIGURES 

Page 

Major C02 Productive Areas and Oil Reservoirs Amenable 

toC02Flooding .................................................... Generalized Overall Refinery from Crude Oil to Salable Products .......... Network of Fuel Products from Natural Gas ............................ Network of Products from Coal ....................................... Major Lipid Fractions as Recovered via Sequential 

Solvent Extraction .................................................. Product Distribution from Catalytic Conversion of Triglycerides ........... Overview of Algal Production and Economic Model (APEM) ............... Average Annual Net Evaporation from Pan A Data ....................... Contribution to Product Cost by General Cost Category .................. Cost Contribution Detail for Direct Operating and 

Maintenance Expenses ............................................... General Cost Contribution of Capital Cost Categories .................... Cost Contribution Detail for Nondepreciable Port ion of 

CapitalInvestment .................................................. Cost Contribution Detail for Depreciable Capital Investment .............. Overview of Microalgae Fuel Refining ................................. Pseudo-Vegetable Oil Production Process Generalized Flow Diagram ....... Schematic of Transesterification Process ............................... Mobil M-gas Process Adapted for Lipid Oils ............................. Anaerobic Digestion of Microalgae .................................... Ethanol Production Process Generalized Flow Diagram ................... Cost Contribution Detail for Lipid-Based Processes ...................... Cost Contribution Detail for Nonlipid-Based Processes ................... Resource Parameter Sensitivity ..................................... .. Facility Design Parameter Sensitivities ................................ Biological Parameter Sensitivities ..................................... Sensitivity of Algal Production Cost to Detention Time. Depth. 

and Density ........................................................ Financial Parameter Sensitivities ...................................... Linkages among Water Source Salinity. Water Use. and Algal 

Species Requirements ................................................ Sensitivity of Reference Production Cost to Variations in the 

Evaporation Rate and Algal Salinity Tolerance .......................... Carbon Dioxide Costs and Transportation Distance for Reference .......... Carbon Dioxide Scale Economies for Reference Transportation ............ Experimental Design for Evaluation of Algal Species Improvements ........ Biomass Production Cost Sensitivity to Photosynthetic Efficiency 

andLipidContent ................................................... Reference. Research Opportunity. and Fuel Option Cases and 

Parameter Values ................................................... Capital Costs for Attainability Fuel Processing Plants .................... Total Operating Costs for Attainability Fuel Processing Plants ............ Operating Costs (Excluding Feedstock) for Attainability Fuel 

Processing Plants ................................................... Photosynthetic Efficiencies Required to Maintain Fuel Product Costs 

Equal to the Cost Goals: Uncertainties that Increase Costs ............... 
LIST OF FIGURES (concluded) 

Page 

6-12 Photosynthetic Efficiencies Required to Maintain Fuel Product Costs 

Equal to the Cost Goals: Uncertainties that Decrease Costs . . . . . . . . . . . . . . 138 

6-13 Combinations of Lipid Content and Photosynthetic Efficiency that Will 

Lead to the Production of Cost-Competitive Liquid Fueis . . . . . . . . . . . . . . . . . 139 
LIST OF TABLES 

Page 

2-1 Potential C02 Demand by Petroleum Basin .............................. 28 

2-2 Total Potential C02 Supply ............................................ 29 

2-3 Carbon Dioxide Supplies in the Permian Basin ............................ 31 

2-4 Energy Content of Some Liquid Fuel Products ............................ 34 

2-5 Composition and Energy Content of a Representative Sample of 

Crude Algae Feedstock .............................................. 41 

2-6 Suggested Standards for Vegetable Oil and Ester Fuels 

vs . Diesel Oil Specifications ........................................... 42 

2-7 Summary of Reference Processing Product Yields ........................ 47 

3-1 Parameter Ranges Identified for Microalgae Systems ...................... 53 

3-2 Summary of Reference Production Facility Cost Contributions 

for Annual Direct Cost and Capital Cost ................................ 70 

4-1 Reference Production Summary for Microalgae Processing Options .......... 75 

4-2 Summary of Reference Capital. Operating. and Allowable Feedstock 

Costs for Fuel Processing Options ...................................... 80 

4-3 Estimated Year 2000 Fuel Cost Goals for Microalgae Products ............. 83 

4-4 Algal Fuel Processing By-products and Values ............................ 83 

4-5 Comparison of Feedstock Production Cost and Allowable 

Processing Cost for the Reference Case ................................. 85 

5-1 Summary of Most Critical Resource. Facility Design. and 

Biology Parameters ................................................... 94 

6-1 Major R&D Issues for Fuels from Microalgae ............................. 97 

6-2 Tradeoffs between Salinity Tolerance and Water Costs .................... 102 

6-3 Summary of Alternative Scenarios to Reduce C02 Costs 

to a Mass Culture Facility ............................................. 106 

6-4 Parameter Assumptions for Evaluating Photosynthesis on 

Lipidcontent ........................................................ 108 

6-5 Comparison of Reference Algal Feedstock Values and Attainable Levels 

forProductivityVariations ............................................ 110 

6-6 Productivity and Fuel Type Cases for Algal Processing Evaluation .......... 116 

6-7 Parameter Levels for Attainability Microalgae Production Cases: 

Changes from Referencecase ........................................ 119 

6-8 Summary Output for Attainability Microalgae Production Cases ............ 120 

6-9 Reference Case and Attainability Values for Lipid Processing Plant 

Scale and Conversion Efficiency ........................................ 121 

6-1 0 Summary of Attainability Product Yields ................................ 122 

6-1 1 Production Summary of Attainability Microalgae Refinery Options .......... 122 

6-12 Summary of Attainability Capital and Operating Costs for Fuel 

Processing Options ................................................... 125 

6-13 Comparison of Integrated Allowable Costs and Production Costs for 

Microalgae-Derived Fuels ............................................. 127 

6- 14 Derived Microalgae Fuel Production Costs for Integrated 

Attainability Concept ................................................. 128 

6- 15 Parameter Values for Attainability Microalgae Production: Variances 

from Reference Case Values ........................................... 129 
LIST OF TABLES (concluded) 

Page 

6-16 Input and Output Material Flows for the Integrated Attainability 

Microalgae Production Concept ........................................ 130 

6- 17 Summary of Integrated Attainability Production Facility 

Cost Contributions ..............e.................................... 131 

6-18 Sensitivity Cases for Attainable Microalgae Production 

Cost: Uncertainties that Increase Costs ................................. 132 

6-19 Mitigating PSE Levels for Attainability Production 

Case Sensitivities .................................................... 133 

6-20 Sensitivity Cases for Attainable Microalgae Production Cost: 

Uncertainties that Decrease Costs.. .................................... 136 

xii 
1.0 INTRODUCTION 

Microalgae are single-celled aquatic plants that have many potential uses including irrigation, waste-water treatment, and the production of food, fuels, and commodity chemicals. The SERI/DOE microalgae program focuses on the production of fuels from microalgae feedstocks. This study presents the results of a comprehensive evaluation that 

assesses the technology of fuels from rnicroalgae and its potential for becoming a costeffective energy supply opt ion. 

1.1 Progress to Date 

The concept of using mass-cultured algae for fuel production grew out of earlier efforts 

to use algae for food production and waste treatment (Burlew 1953). Some of these 

efforts have produced algae with a high market value. For example, the blue-green alga 

Spirulina produces high-quality protein and is sold in health food stores for up to $10/lb. 

Researchers at the Carnegie Institute (Burlew 1953) experimented with mass culture of 

algae for food in the early 1950s, and this work led to expanded efforts by American, 

German, Israeli, and other researchers to mass-produce various species of microalgae for 

both waste water treatment and animal feed protein (Shelef and Soeder 1980). 

The use of microalgae as a fuel was first proposed 30 years ago for the production of 

methane gas (Meier 1955). In the 1970s, the cost of conventional fuels began rising 

rapidly, and the possibility of using algae as a fuel source received renewed attention. 

In 1979, DOE and SERI initiated a research program to pursue opportunities for producing 

fuels from microalgae. Although previous research on algae grown for other uses provided a basic foundation for the program, the unique challenges of fuel production 

required a directed, goal-oriented research and development program. 

Past research and assessment efforts have resulted in a concept for using microalgae 

that has four main features: (1) a mass culture system for producing microalgae feedstocks, (2) location of mass culture facilities in the U.S. desert Southwest, (3) the use of 

saline waters, and (4) an emphasis on the production of lipids. Mass culture of microalgae is important to the viability of the technology because it would produce feedstocks 

in sufficient quantities to take advantage of economies of scale in fuel processing. 

The DOE/SERI program emphasizes the development of microalgae systems in the desert 

Southwest because this area offers flat land, high incident solar radiation, and few competing land uses; locating the mass culture facility in this region would minimize land 

costs. The use of saline water, which is a suitable medium for the growth of many 

microalgae, minimizes both water costs and competition with other uses for limited supplies of fresh water in the Southwest. Finally, the program emphasizes the production of 

lipids from microalgae because plant storage lipids could be among the best biomass 

feedstocks for producing renewable, high-energy liquid fuels. Microalgae are among the 

few photosynthetic organisms that directly produce and are known to accumulate storage 

lipids in great quantities. 
Microalgae Mass Cuiture Technology 

Mass culture technology 

requires several components 

that must be integrated into a 

system for eff~cient production 

and harvesting. 

Purnphouse - 

Ij 

Control Center 

'%%'I Harvesl Tanks Drying Basins 

- 11 Media Recycle Preprocessing 

Influent Line fimm; 

T T T TT T iT T T 1 

conceptualized M~croalgal Culture 

LLl Facility for Energy d - 

Microalgae are grown in the 

culture unit, a shallow 

(10-20 cm deep) pond or 

raceway. In these ponds, water 

circulation is necessary to hold 

the microalgae in suspension 

for optimal productivity. 

Paddlewheels are an 

economical mixing mechanism 

and can be adapted to facilitate 

nutrient additions such as 

carbon dioxide, nitrogen, and 

phosphorus. 

High productivity requires 0 

0 

uniform mixing of the culture to 0 rassure that the algae circulate 

systematically between the 

dark bottom zones and the 

lighted surface zones. Foils 

have been proposed to provide 

this uniform mixing. 
Microalgae Mass Culture Technology 

Production Concentration Processing 

0.02-0.15% 

Total 

Suspended 

Solids 

(TSS) 

2-7% TSS - Drying 1 Extraction 

A 

Microalgae must be harvested 

from the culture medium, 

concentrated, and dried before 

they can be processed. The 

opt~rnal harvesting technique 

depends on the characteristics 

of the species. Some algae can 

be induced to form clumps 

(autoflocculate) and float or 

settle to facilitate harvesting. 

Otherwise filtering must be 

employed to harvest the small 

algal cells. 

Harvesting by autoflocculation 

requires pumping the medium 

from the culture unit to a 

separate pond where 

flocculated cells can be 

separated from the medium. 

Microstrainers or ultrafiltration 

techniques can be used to filter 

the algal cells continuously or 

in batches directly from the 

culture. 
In research sponsored by the SERI/DOE microalgae program and other, independent 

researchers, sustained photosynthetic efficiency * in the laboratory and controlled outdoor experiments has achieved over 12%, and lipid concentrations, normally about 10%- 

20%, have reached 60% using nitrogen starvation techniques. These are significant 

achievements that represent a valuable beginning to an emerging technology. However, 

progress must be measured against the ultimate goal of producing cost-competitive fuels. 

The objective of this study is to define research goals consistent with costcompetitiveness. 

1.2 Technology Overview 

Two basic steps are involved in producing fuels from microalgae: feedstock production 

and fuel processing. As the technology is envisioned today, microalgae would be grown in 

large, shallow ponds of saline water in the desert Southwest, harvested, and processed to 

make a feedstock that can be used in the production of liquid or gaseous fuels. This 

specific concept is the subject of this evaluation.** 

Two characteristics of microalgal fuels technologies make them particularly difficult to 

assess. First, a wide variety of fueis can be produced, including ethanol, triglyceridebased diesel fuel, ester fuel, methane, and gasoline. Each fuel has a different market 

value as well as different feedstock and fuel processing requirements. It is not clear 

which fuels are the best candidates for production from microalgae. 

The technology's second major feature is that it contains interrelationships so complex 

that no single parameter--either within or between phases of production--can be evaluated or optimized in isolation. The magnitude of the assessment problem is illustrated by 

the following example. 

To improve the cost effectiveness of microalgae-based fuels, the productivity of the 

mass culture system must be improved from current levels. But specific improvements-- 

in photosynthetic efficiency, nutrient supplies, and the production of different algal products--cannot be evaluated completely without first specifying the fuel product to be 

produced and its processing requirements. Similarly, the required improvements in fuel 

processing cannot be determined without first specifying the feedstock to be processed 

and the fuel product to be produced. Finally, the most promising fuel products cannot be 

identified without an understanding of feedstock characteristics and fuel processing 

requirements. These complex technology interdependencies are described in detail in 

Section 2.0 and largely determined this study's assessment approach. 

1.3 Study Purpose and Approach 

The broad purpose of this study is to assess the feasibility of producing fuels from microalgae. Specifically , achieving this goal involves answering four major questions. First, 

which fuel products are the best candidates for production from microalgae? Second, 

*In this document photosynthetic efficiency is defined in terms of photosynthetically 

active radiation (PAR), those wavelengths of sunlight between 400 and 700 nm. 

**A comprehensive overview of algal biomass technologies can be found in Barclay and 

McIntosh (1986). 
given those fuel products, what improvements in the feedstock production process are 

required to achieve greater cost effectiveness? Third, given the microalgae feedstock 

characteristics, what improvements in fuel processing are required to achieve cost effectiveness? Finally, given the required improvementsin the mass culture system and fuel 

processing, what are the implications for a research program designed to meet the object ive? 

Because of the complex interrelationships within and between feedstock production and 

fuel processing, it is not possible to answer these questions in sequential order. Instead, 

the nature of the technology requires an iterative approach that allows for the sequential 

screening of major technical issues. Generally, in our assessment process we established 

a reference case for mass culture and fuel processing, conducted sensitivity analyses to 

identify major cost drivers and optimal fuel products, and then established cost and performance requirements for a viable microalgal fuels technology. Specifically, the assessment approach involves four basic steps. 

Estimate current costs for feedstock production. This step is necessary to provide a starting point from which the analysis could proceed. Section 3.0 provides 

an overview of the processes involved in mass culture production of microalgae 

feedstocks, derives representative characteristics for each step in the production 

process, and then estimates the cost of producing feedstock from the reference 

system. The reference case is based on the best available information from 

small- and large-scale experiments and engineering studies. Thus, it represents a 

first approximation of the expected costs if a production facility were constructed and operated today and identifies ranges for all variables that represent 

the potential for improvement. 

Estimate current costs for fuel processing. This step provides a basis for further 

evaluation. In Section 4.0 we use the feedstock characteristics derived in Section 2.0 to establish a reference fuel conversion process and then estimate the 

cost of fuel processing. As in the feedstock reference system, the fuel processing reference case is based on best available information and represents a first 

approximation of processing technology. 

Determine the major parameters affecting the cost of microalgae feedstocks. 

More than 100 parameters were specified in the development of the reference 

mass culture system. This step was necessary to screen the most important 

parameters so that later analyses could be reduced to manageable proportions. 

Section 5.0 describes the sensitivity analyses that were conducted to identify the 

major cost drivers. 

Assess R&D requirements for both feedstock production and fuel processing. 

This step simultaneously permits an assessment of the viability of the technology 

and helps to def ine R&D priorities. Section 6.0 begins by assessing important 

linkages among related parameters in mass culture technology. These linkages 

reduce the major cost drivers to their lowest common denominator and facilitate 

the analysis of R&D goals. In Section 6.0 we estimate allowable feedstock costs 

to establish attainability targets for mass culture production and to identify the 

best fuel products for production from microalgae. Then we determine the 

required improvements in fuel processing, first in reference to a single, subjective technology configuration that is judged to be attainable and then relative to 

uncertainties inherent in the attainability case. 
Microalgae - The Simplest Plants 

Microalgae are the most 

primitive, and the most simply 

organized, members of the 

plant kingdom. The vast 

majority of microalgae exist as 

single cells in aqueous 

habitats, while a few are 

organized into simple colonies. 

Macroalgae-the seaweedshave achieved a further level of 

organization, with cells that are 

grouped into structures which 

resemble the leaves, stems, and 

roots of higher plants. 

Almost twenty thousand 

species of algae have been 

identified. These species have 

been grouped into a number of 

divisions, three of which 

conta~n algae that are 

frequently employed for largescale cultivation. The members 

of the first group, the bluegreen algae, are organized 

similarly to bacteria in that their 

cells do not possess a nucleus. 

In some cases, the blue-green 

algae can utilize atmospheric 

nitrogen gas to fulfill their 

nitrogen requirements and thus 

serve an important ecological 

role as producers of fixed 

nitrogen. The remainder of the 

algae are true plants 

possessing nuclei and 

chtoroplasts, which are the 

su bcellular organelles in which 

photosynthesis takes place. 

The green algae (Chlorophyta) 

are among the most common, 

especially in fresh water, and 

can occur as single-celled or 

colonial forms. The golden 

algae (Chrysophyta) have developed more complex pigment 

systems and can appear yellow, 

brown, or orange in color. 

Golden algae can form oils 

(lipids) as their major energy 

storage product, in contrast to 

the majority of the green algae, 

Ankistrodesmus falcatus - A single-cell green algae 

Colony of Botryococcus braunni 
Microalgae - The Simplest Plants 

Hantzsch~a sp. A diatom (a golden algae) showing hpld accumulation 

which produce starches. These 

oils can constitute 60% or more 

of the cell biomass under some 

conditions. Diatoms, a 

particularly common group of 

golden algae, normally form 

silica shells surrounding their 

cell mass. 

Microalgae, as the~r name 

implies, are very small, typically 

about one one-thousandth of 

an inch in size. Their size varies 

widely, however; single-celled 

species larger than 1/4 inch 

can be found, as can species 

which are as small as some of 

the bacteria, less than one tenthousandths of an inch. 

While the biochemical 

mechanism of photosynthesis 

in algae is similar to that found 

in all plants, algae can be 

part~cularly efficient converters 

of solar energy to biomass by 

virtue of their "low overhead." 

Free of the need to generate 

elaborate support and 

reproductive structures, the 

microalgae can devote Ihe 

majority of the energy 

they trap to biomass growth. In 

addition, single cells 

suspended in an aqueous 

'environment can directly 

acquire the water, carbon 

dioxide, and nutrients 

they require for growth. The 

energetic efficiency of the 

microalgae, along with their 

ability to produce large 

portions of their total biomass 

in the form of oils, has caused 

them to be regarded as one of 

the most promising crops 

for liquid fuels production from 

plant biomass. 
This section is a 

reauirements and 

PRINCIPLES OF MICROALGAL FUELS TECHNOLOGY 

primer on the principles of microalgae mass culture, engineering 

options, basic resource requirements, and fuel product alternatives. 

~eitions 2.1 through 2.3 provide a review of basic concepts and establish the foundations 

for estimating production costs in Section 3.0. Section 2.4 describes the basic chemical 

components of algal biomass and develops the fundamental concepts for fuel process 

design and economics developed in Section 4.0. 

2.1 Mass Culture Technology 

2.1.1 Introduction 

The fundamental components of a microalgal mass production technology (illustrated in 

the sidebar in Section 1.0) include water, nutrient, and carbon dioxide supply systems; 

culture ponds; harvesters; and processing equipment. In this section we describe the biological and engineering principles governing the design and performance of the components related to biomass production and harvesting. Processing components will be 

examined in Section 2.4. 

2.1.2 Culture Water Demand and Salt Balance 

Water demand and salt balance are governed by the evaporation rate, a function of climate and culture design, and by the salinity of the source water. Interactions among 

these variables are discussed first for uncovered and then for covered systems since this 

facility design variable affects the basic relationships. Then the effect of salinity on 

culture operation is discussed. 

Uncovered Systems 

One major advantage of microalgal production technologies is their ability to utilize 

saline waters, which are unsuitable for conventional agriculture or for higher plant production. Uncovered algal ponds lose significant amounts of water through evaporation, 

thus becoming solar concentrators of dissolved salts. Since many algal species can tolerate salinity well in excess of that of saline groundwater [4-25 g total dissolved solids 

(TDS)/L], and some species tolerate salinity in excess of that of seawater (35 g TDS/L), 

this concentration process is not viewed as detrimental within expected operating limits. 

However, constraints are placed on system operations by the upper limits of the algae's 

salt tolerance and by the solubility of various salts, and it is necessary to remove salts 

from the systems before concentrations reach these limits. Salt removal is achieved by a 

process termed llblowdown," in which a portion of the water in the culture is removed, 

carrying the appropriate amount of salt with it. Over the long term, the salt equilibrium 

in the system must be held within tolerable limits to maintain algal productivity. 

The water voiume consumed for blowdown is small relative to the source water input 

volume as long as the operating salinity of the culture is significantly greater than the 

source water and evaporation rates are not excessive. The remainder of the source 

water input volume replaces evaporation losses. Water for blowdown is ideally removed 

downstream of the harvester to minimize the loss of algal biomass with the blowdown 

stream: blowdown is achieved in the water recycle loop by replacement of part of the 

recycle with fresh water. 
Covered Svstems 

Transparent coverings over microalgal mass culture systems can reduce evaporative 

water losses, protect cultures f rorn heavy rains and contamination, and permit improved 

temperature control. Such coverings, however, will add significantly to the cost of production systems. In addition to their cost, covers cause the following logistic problems: 

All covering materials will absorb and reflect portions (about 10% minimum) of the 

incident light, reducing the light utilization efficiency of the system. Water condensation on the inner surface of the covering and the accumulation of dust and 

dirt on the outer surface could reduce further the amount of light reaching the culture. 

While coverings can permit the maintenance of warmer cultures in the winter, they 

can cause severe overheating in the summer. This effect can be partially offset by 

the use of covering materials that reflect or absorb infrared radiation, but only at 

additional cost and with some loss of photosynthetically active radiation (PAR). 

Passive ventilation systems can be added to the covered systems to prevent overheating, but evaporation control, the main purpose of covers, would then be lost. 

Although culture systems are likely to be established in areas where there will be a 

net loss of water due to evaporation, severe rainstorms can be expected occasionally. If large areas are covered and protected from this rainfall, a significant problem of runoff and water disposal will be created unless mitigating actions are 

taken. 

A comprehensive analysis of the trade-offs between the problems and the advantages of 

covered systems has not been performed. Engineering designs proposing covered or 

enclosed systems cannot be rejected out of hand, but their designers must thoroughly 

address these issues. In Section 6.0, we address minimum design requirements and tradeof fs between cost and productivity. 

Salinitv Effects on Culture Performance 

Two aspects of the concentrations of salts in an algal culture medium influence growth: 

the total salinity or ionic strength of the solution, and the concentrations of the individual ions that make up the total salt content. 

Microalgal species vary widely in their ability to tolerate salinity: while some species 

survive only in fresh water, other species can tolerate saturated brines (Bonin, Maestr ini, 

and Leftly 1981). In uncovered cultures in arid regions, average operating salinities can 

be expected to be high due to high evaporation rates. However, since periodic rainstorms 

can be expected to lower the salinity temporarily, the most desirable species will be 

those that tolerate large fluctuations in salinity as well as high average salinity. 

The concentrations of ions in solution have significant impacts on the growth of algal 

species. The major ions in saline waters (sodium, potassium, calcium, magnesium, 

chloride, and sulfate) are biologically active, but very few studies have been done on the 

effects of ionic concentrations on algal growth. Ions present in lower concentrations, 

which have been identified as limiting to algal growth in natural waters, have been 

studied intensively. These ions include nitrate, nitrite, ammonium, phosphate, carbonate, 

bicarbonate, and iron. Algal requirements for these low concentration nutrients and the 

ability of algae to assimilate them from the environment are well understood. Water 

added to algal cultures after evaporation or blowdown will carry nutrient ions that in 
some cases can help to meet nutrient demand. The significance of such contributions can 

be assessed only after more intensive study of specific saline water resources. 

Salts in natural waters also include trace elements that, while vital to the survival and 

growth of microalgae, can become toxic to the algae in higher concentrations. These 

trace elements include copper, cobalt, zinc, manganese, molybdenum, and vanadium. 

Since algal cultures are expected to concentrate the groundwater salts, the effects of 

concentrating these ions must also be considered, especially with respect to the requirements of the algae and to the potential toxicity of these metals. The potential toxicity 

of other elements such as mercury, cadmium, and selenium must also be considered. 

Quantitative requirements of microalgae for trace elements have not been determined, 

and in most cases the levels at which the ions become toxic have not been well documented. It is known, however, that the presence of chelating agents in the water substantially reduces the activity of metal ions and hence their toxicity (Huntsman and 

Sunda 1980). It may be necessary to add chelating agents to culture systems, but some 

evidence shows that natural chelating substances produced by the algae may contribute 

to this detoxification function. The following aspects of water supply are of particular 

interest: 

Operating salinities. To minimize blowdown requirements, it may be desirable to 

operate systems at the highest possible salinities. High operating salinities can also 

reduce the possibility of contamination and may aid in predator control; however, 

the cells may require more energy to grow in a high salinity medium, which may be 

detrimental to productivity. Microalgal species that will perform well as fuel producers in high-salinity outdoor cultures must be identified and characterized. 

These microalgae must tolerate both the total ionic strength and the ionic composition of the culture source water. 

Requirements for nutrients. Most nutrients required by the algal cultures will be 

supplied through the addition of commercial fertilizers or other compounds, 

although some source waters can make significant contributions to these requirements (Section 2.1.4). Source waters can, in many cases, meet some of the 

requirements for major ions, including potassium, and can contribute to the 

requirements for nitrogen and phosphorus. If harvesting and processing mechanisms 

can be developed that permit significant nutrient recycling, the requirement for 

externally supplied nutrients can be further reduced. However, significant nutrient 

requirements will remain for startup or for facility expansion. 

Ionic toxicity. The concentrating effects of algal systems on potentially toxic 

trace elements and other toxic metals cannot be ignored. Since many of these 

elements are incorporated into the algal biomass, nutrient recycling systems will 

return these elements to the culture and further increase the concentration effects 

of the system. Thus, if the quantity of trace elements supplied by the source water 

exceeds the quantity removed in the product stream, elevated concentrations of 

these ions will develop. An understanding of the ion concentrations in the source 

water, the quantitative requirements of the algae, the toxicity responses of the 

algae, and the effects of natural and artificial chelation is necessary before this 

e f f e c t c a n be evaluated fully. 
2.1.3 Carbon Dioxide Supply 

Algae utilize carbon dioxide (C02) as the source of carbon for the synthesis of organic 

compounds such as lipids. Since carbon demand increases in proportion to productivity, 

intensive mass culture of microalgae requires a large, reliable source of C02. 

The Necessity for Carbon Dioxide Supply 

Carbon dioxide is present in the atmosphere at very low concentrations (0.032% by 

volume in dry air). Passive flux of atmospheric C02 through the surface of algal mass 

cultures is inadequate to supply the amount of carbon dioxide required to support intensive productivity. If a culture producing 25 grams of dry algal biomass per square meter 

per day was solely dependent on atmospheric CO as a carbon source, it would require all 

of the carbon dioxide in the air 50 meters directTy above the surface of the culture each 

day. If this amount of air was bubbled through the cultures, the efficiency of transfer of 

CO from the air to the water would be extremely low, reflecting the low concentration 

of Zo2 in the air. It would be necessary to bubble hundreds of cubic meters of air per 

square meter of culture each day to supply the necessary COZ. Moreover, it would be 

necessary to do this over the few hours of the day when the culture carbon demand was 

highest. The costs and logistics of such a system would be prohibitive. Thus, outdoor 

systems for the photosynthetic culture of microalgae have consistently required additions 

of C02 to achieve high productivity. 

Carbon Dioxide Additions 

For the reasons outlined above, concentrated carbon dioxide must be added to systems to 

produce biomass fuels from microalgae. The sources of this carbon dioxide are discussed 

in Section 2.3.4. If C02 additions to the culture raise concentrations above the equilibrium concentration with the atmosphere, significant quantities of C02 will be lost (outgassed). Stringent automated control of C02 additions will be required to minimize 

these losses, as will careful selection of the criteria governing C02 addition so that a 

balance is achieved between possible CQ2 limitation of the algae and C02 loss to the 

atmosphere. The only major prospect for reduction of the total CO requirement is the 

utilization of the dissolved forms of carbon dioxide (primarily bicarbonate) that can be 

present in source waters. As with the nutrients discussed above, the large volumes of 

water added to the culture system to compensate for evaporation and blowdown may 

carry significant quantities of bicarbonate. Depending on the evaporation rate and the 

bicarbonate content of the source water, this water can supply from a few percent to 

more than 20% of the total carbon dioxide requirement. 

Carbon Dioxide Supply Systems 

Two factors determine the efficiency with which externally supplied carbon dioxide is 

converted into fixed organic carbon in microalgal systems. The first factor, carbon 

dioxide loss from the culture surface to the atmosphere, has been described. The second 

factor is the efficiency with which the carbon dioxide can be dissolved in the culture 

medium before it escapes to the atmosphere. If carbon dioxide is added as bubbles, large 

quantities can be lost if the bubbles reach the surface and burst. Alternatively, carbon 

dioxide may be added as small bubbles in counterflow columns, where the downward 

movement of the water offsets the upward movement of the bubbles until they are 

consumed. A third alternative supply mechanism holds the carbon dioxide in a containment vessel below the surface of the culture, and the free surface of the gas contacts 

the flowing water. Hybrid systems can also be envisioned. Since the addition of bicar- 
bonate salts to algal systems will lead to increased alkalinity, it seems unlikely that the 

bulk of C02 will be added in this manner, and one of the above concepts must be 

developed to meet carbon requirements. 

Little attention has been given to determining the efficiency with which carbon dioxide 

can be added to outdoor algal systems. Rough estimates from SERI subcontractors indicate that 50%-80% of the CO added to their systems is incorporated into the product 

algal biomass (Laws et al. 1983). Very little engineering effort has been devoted to the 

design of the COZ addition systems used in these cultures, and significant improvements 

can be expected with improved engineering. If cultures are carefully monitored, if C02 

concentrat ions are maintained slightly below saturation with respect to the atmosphere, 

and if contained systems for C02 addition are employed, highly efficient carbon dioxide 

utilization can probably be achleved without detrimental effects on algal production 

rates. 

2.1.4 Nutrient Supply 

Nutrient requirements are determined by basic biological requirements, the quality of 

the nutrients, and the design of the culture system. 

Algal Nutrient Requirements 

In the present context, nutrients can be defined as inorganic or organic compounds, other 

than carbon dioxide and water, that are incorporated into algal biomass during the 

process of growth, or whose presence in the cell is necessary to cellular function. 

Various compounds of over twenty elements are known to function as inorganic nutrients 

by this definition (Ukeles 1976). In addition, some algae require specific organic compounds synthesized by other organisms. However, many algae require only inorganic 

nutrients, and it is likely that these algae will be utilized for biomass fuel production. 

Many inorganic nutrients required by algae are required in such low concentrations, or 

are present in natural waters in such relatively high concentrations, that they have not 

been observed to limit algal growth in nature (Huntsman and Sunda 1980). Nutrients that 

normally become limiting in outdoor mass cultures, as well as in natural populations, are 

nitrogen, phosphorus, silica (for diatoms), and iron. Algae can store nutrients in substantial excess of their requirements, presumably as a reserve against nutrient depletion; 

algal cells may contain 2-4 times the amount of nitrogen and more than 10 times the 

amount of phosphorus they require. 

A major concern in algal mass culture is the regulation of nutrient consumption. The 

requirements for nutrients must be regarded as absolute, as these nutrients are components of the cellular "machinery" through which biomass is produced. Nevertheless, the 

consumption of these nutrients can be reduced if cellular processes can be directed away 

from the replication of cell machinery and toward the production of energy-rich storage 

products, which typically contain much smaller amounts of inorganic nutrients. 

A two-stage system can be employed to induce algae to utilize their internal nutrient 

reserves for growth and product formation before harvest. Such a system may have 

important implications for the determination of product type as well (Section 2.1.5). 

Thus, the total nutrient content of the biomass product could be reduced, but it has yet 

to be determined whether such a reduction in algal nutrient content, and thus in the 

amount of nutrients required to produce a certain amount of biomass, can be achieved 

without detrimental effects on photosynthetic efficiency. However, if an efficient 
system of nutrient recycle can be developed, the nutrient content of the algal cells need 

not be a major concern, although the portion of the nutrients organically incorporated 

into the final product must always be replaced. In later sections we explore tradeoffs in 

production cost and recycle efficiency. 

Nutrient Sources 

The nutrients required by algae cultures c a n b e supplied in a variety of forms. Many can 

be found in solution as a variety of ions or bound in organic compounds. Ionic forms can 

be supplied to the cultures as salts, both inorganic and organic. Nitrogen, for example, 

can be supplied as nitrate, nitrite, or ammonium ions combined with each other or with 

other ions in various salts; as urea; as ammonia gas or liquid; or as a wide variety of 

organic nitrogen compounds. The form in which nutrients are supplied is a major issue 

only for nutrients acquired in large quantities. The selection of nutrient source is most 

significant for nitrogen, because possible sources are numerous and the requirement is 

large. 

Nitrogen source. Of the available nitrogen sources, ammonia (NH3) and urea [CO(NHZ) ] 

are the least expensive. Ammonia can be supplied as a liquid under pressure, wh&~ 

reverts to the gaseous phase at atmospheric pressure. Nutrient handling costs are low 

with a pumpable fluid such as ammonia. In addition, ammonia is 83% nitrogen by weight. 

Urea is another attractive source of nitrogen. The cost of urea per nitrogen atom is 

similar to that of ammonia; however, handling can add to this cost, since urea is supplied 

as a crystal and cannot be inexpensively pumped. In addition, only 42% of the weight of 

urea is nitrogen; thus, more material by weight must be handled with urea than with 

ammonia. However, the urea molecule contains a carbon atom as well as two nitrogen 

atoms; this carbon atom is released as C02 when urea is utilized, and presumably it is 

available for photosynthetic assimilation. Thus, urea can supply both the nitrogen 

requirement and 1.5%-10% of the carbon requirement. A more complete analysis of 

these trade-offs is required to determine the optimum nitrogen source. 

Nutrient sources other than commercial fertilizers. Municipal wastewaters contain 

nutrients in forms that are readily available to algae or become available to the algae 

after bacterial decomposition. Bacterial-algal interactions for wastewater nutrient utilization have been studied extensively and are the basis of existing sewage treatment 

technology. Warm temperatures are a prerequisite for the operation of these systems, 

which are called high-rate oxidation ponds. Several obstacles restrict the use of sewage 

nutrients in biomass fuels production. Probably the greatest obstacles are the absence of 

sizable municipalities in the vicinity of anticipated production sites and alternate wastewater utilization technologies that are being developed and applied. In addition, municipal wastewaters do not provide a nutrient source concentrated enough to support intensive cultures of microalgae with a high population density in shallow water. 

Since saline groundwaters contain significant quantities of certain nutrients, groundwater 

can contribute to the overall nutrient supply. However, the groundwater supplies 

nutrients in direct proportion to the volume of water utilized; thus, engineering strategies that minimize water use, such as covered systems, will also minimize the supply of 

nutrients from this source. Also, saline groundwaters vary widely in nutrient and nonnutrient ion content. There are insufficient data available to determine whether highnutrient (and in particular high-bicarbonate) groundwaters are available in sufficient 

quantities to support a microalgal fuels technology. Moreover, the long-term effects of 

pumping on the composition of these groundwaters is not known. 
Nutrient SUDD~Y Strategies 

Careful regulation of the quantities of nutrients added to culture systems is extremely 

important. Two goals must be achieved: (I) minimization of the amount of nutrient lost 

with the water in the blowdown stream or to ammonia degassing, and (2) minimization of 

the nutrient content of the harvested algae, to the extent that this minimization does not 

significantly reduce product yield. Most mass culture systems operated to date have 

used nutrients in excess of the algae's requirement, constantly sustaining a high nutrient 

concentration in the culture medium. Thus, water removal from the system for blowdown and harvesting has led to significant nutrient loss and reduced efficiency. 

Since the water expelled during blowdown is identical in composition to the culture 

medium, minimization of the nutrient loss to blowdown requires minimization of the 

standing concentrations of nutrients in the culture. Algae can grow rapidly at ambient 

nutrient concentrations that are nearly undetectable. In very dense outdoor cultures, 

nutrients are consumed quickly and nutrients must be added at the same rate at which 

they are consumed to maintain low concentrtions without nutrient limitation. This 

balance between addition and consumption can be achieved by continuous monitoring of 

nutrient concentrations in the culture combined with continuous addit ions designed to 

maintain low but satisfactory concentrat ions. However, reliable, easy-to-use instrumentation to monitor nutrient concentrations is not available. 

2.1.5 Algal Species for Mass Culture 

The design of microalgal mass culture systems is a synergistic process: the design must 

be tailored to the characteristics of the culture organism while species must be selected 

that contribute to economic construction and operation of the facility. Thus, a primary 

task in mass culture development is the "engineering" of the organism itself. In practice, 

these organisms are not amenable to direct engineering; rather, species with the most 

desirable characteristics must be selected from the thousands of natural species. 

Through breeding and molecular genetics, selected species can be altered to enhance 

these characteristics, and to suppress detrimental attributes. 

Character istics of Suitable Species 

The first priority in selecting species for mass culture applications is to identify the 

desirable characteristics. This in turn requires some assumptions about the types of 

systems that will be employed and in particular about their potential location and the 

types of water that will be used. Many species characteristics considered desirable by 

the SERI microalgae program reflect the decision to locate the system in temperate latitudes in arid regions and to use saline source waters. These characteristics include 

(I) tolerance of high temperatures, (2) tolerance of a wide range of temperatures, 

(3) tolerance of high salinity, (4) tolerance of a wide range of salinity, and (5) high productivity at high incident-light intensity (resistance to photoinhibition). In addition, 

certain characteristics of species are desirable regardless of the location of the cuiture 

facility. These include (I) high photosynthetic efficiency, (2) size, buoyancy, or behavioral characteristics that enhance harvestability, (3) resistance to predators and contaminants, (4) absence of autotoxicity, (5) suspendability (absence of sticking or settling), (6) 

the ability to tolerate high nutrient concentrations, and (7) a life cycle that permits continuous culturing. Ideally, a species will also have a readily inducible sexual reproductive 

phase to permit genetic modification through classical breeding and selection. 
The final consideration, and one of the most important, is the slate of substances produced by the organism. In the Department of Energy's program, interest is focused on 

biomass products that can be converted to fuels. The characteristic of greatest interest 

is the fuel production rate, which is the biomass production rate multiplied by the fractional fuel product content. A species that exhibits a high biomass production rate under 

one set of conditions and a high fuel product content under another set of conditions cannot be considered desirable for fuel production systems, although it may be a good candidate for species development activities. 

Species that fulfill all of the above criteria may not be available in the natural environment. The environment in a mass culture system differs significantly from most natural 

environments, and the performance criteria in mass culture (fuels product ion) are not 

equivalent to the performance criteria in nature (species survival and reproduction). 

Species must be collected from natural environments that reflect desired culture conditions as closely as possible, and these species must be screened for their fuels production 

ability early in the selection process. When species with the best possible combinations 

of the desired characteristics have been identified, development of these species through 

classical and molecular genetics can be undertaken. 

Since the natural environment does not select for algal species that will be optimum performers in mass cultures, it may be necessary to genetically alter these organisms 

through selective breeding (classical genetics) and genetic engineering (molecular genetics). In t he classical genetic approach, cells that have desirable characteristics are 

mated, and their off spring are screened for attractive combinations of these characteristics. This process can be repeated many times, leading to significantly improved strains. 

Genetic engineering techniques permit the introduction of genetic material from other 

organisms or artificially synthesized genetic material into algae with good production 

potential, or direct modification of the algal genome. The following discussion presents 

basic issues relating to each of these approaches. 

Selective breeding. In order for selective breeding to be successful, a genetic base must 

be developed. It is necessary to identify species that are suitable candidates for this 

process and to collect a large enough number of strains of each species to permit crossbreeding. The use of selective breeding is complicated because some algal species do not 

have known mechanisms of sexual reproduction, while others that have been observed to 

reproduce sexually cannot be induced to do so when required. Thus, improved knowledge 

of algal life cycles is required before sexual genetic recombination can be achieved. 

An additional genetic technique is the creation of mutants through the use of radiation or 

chemical mutagens, and the subsequent selection of those mutants that offer improved 

characteristics for mass culture for fuels. The major drawback to mutagenic techniques 

is that the majority of mutations result in deletions of characteristics. Thus, mutagenesis is primarily applicable when characteristics are to be eliminated, for example 

regulatory mechanisms that prevent excessive accumulation of storage product. Mutagenic techniques can be utilized in combination with selective breeding techniques to 

suppress undesirable characteristics while adding or improving those that are desirable. 

Genetic engineering. While genetic engineering permits direct manipulation of the 

physiological processes of the cell, many obstacles remain to the application of this 

technique to microalgae. To introduce genetic material into a host organism, it is necessary to have both a knowledge of the genetic material that governs the process to be 
altered and of a mechanism for introducing this material into the host cell (vector). 

Also, the physiology of the organism that receives an introduced gene will not always 

permit it to be expressed (to perform its physiological function). Genetic manipulation 

of the process of fuel (lipid) production in microalgae, for example, requires knowledge 

of the pathway by which lipids are produced, the mechanisms by which this pathway is 

regulated, and the discovery of a vector to introduce the necessary genetic alterations 

into viable cells of suitable species. 

An additional technique that may hold some promise for species development is protoplast fusion. Some success has been achieved in generating protoplasts of macroaigae, 

but only limited success has been reported in regenerating viable plants from the protoplasts, or in obtaining fusion and regeneration (Cheney 1986; Saga, Bolne-Fuller, and 

Gibor 1986). Protoplasts are cells that lack a cell wall and are only surrounded by the 

plasma membrane. In some organisms, it has been possible to induce these protopiasts to 

merge and thus share their genetic material. This technique has been particularly successful in transferring characteristics genetically coded in the cytoplasmic organelles. 

Protoplast fusion techniques offer a form of genetic manipulation between classical 

breeding and genetic engineering. The application of this technique will require the 

development of techniques for protoplast formation and regeneration, and also the 

development of a genetic base of species and strain material. 

2.2 Design of Mass Culture Production Systems 

2.2.1 Growth Conditions 

Growth conditions in microalgal mass cultures can be divided into two categories: those 

dictated by the location of the culture and those based on culture management strategy. 

Location-related variables include insolation, evaporation, rainfall, temperature, and 

wind velocity. Variables that can be managed include salinity, nutrient concentration, 

carbon dioxide concentrat ion, culture mixing, culture aeration, and residence time of the 

population. 

Growth conditions dictated by the culture's location will be among the prime considerations in siting the production facility. The SERI program has been based on the assumption that it will be necessary to locate a production facility in a n a r e a that receives large 

amounts of sunlight and has relatively warm temperatures. 

Many other growth conditions in outdoor mass cultures are dictated by the management 

strategy selected. For example, through the blowdown technique mentioned earlier, the 

salinity of the culture can be maintained between the salinity of the source water and 

the algal salinity tolerance. The maintenance of selected nutrient concentrations and 

carbon dioxide concentrations has also been discussed. The rates of culture mixing, culture aeration, and culture turnover (detention time) can be selected. Culture management techniques also include the selection of strategies for the addition of medium, the 

supply of nutrients, the removal of product, and the selection of species for cultivation 

during various seasons. The major factors in the design and operation of mass culture 

systems are discussed below. 

Sunlight Requirements 

Since sunlight drives the production of biomass, ideal production systems will be located 

in areas that receive high insolation. Nevertheless, it is necessary to consider the tradeoffs involved in achieving high insolation. If the costs of land, raw materials, or 
operation are significantly increased at a location with high insolation, siting solely by 

the solar input may be disadvantageous. In addition, the search for areas of high 

insolation implicitly assumes that all solar radiation can be used by algal systems, 

regardless of its intensity. This assumption can be supported for well-mixed, energyintensive systems, but in less well mixed systems it is not clear that high-intensity light 

c a n b e efficiently utilized. 

Culture Tem~eratures 

Algal growth rates have been positively correlated with culture temperature, at least 

until temperatures become high enough to be detrimental. However, algae in outdoor 

mass cultures do not normally grow near the maximum growth rate dictated by the temperature, but are limited in their growth by the availability of light or other factors. 

Properly selected species may continue growing with good photosynthetic efficiency even 

at low winter temperatures. However, low temperatures are normally correlated with 

low light intensities, and cultures at high or mid latitudes have become unproductive 

during the winter months (Benemann et al. 1977). While some limited level of production 

could be maintained at these times, productivity might be insufficient to support operational costs. While mechanisms to maintain higher culture temperatures during winter 

months might be suggested, these mechanisms will not be practical if light intensity falls 

below a critical level. Calibration of culture economics is needed to determine strong 

requirements. 

Culture Aeration 

The introduction of carbon dioxide into culture systems is necessary to achieve high rates 

of production, but other functions of aeration should not be overlooked. Among these is 

the reduction of culture oxygen concentrations during the highly productive midday 

period. During this period, culture concentrations can reach more than five times the 

equilibrium oxygen saturation concentration if no mechanism of vigorous gas exchange is 

provided. Oxygen, which is an end product of photosynthesis, inhibits the photosynthetic 

process when it is present at high concentrations through a process referred to as photorespiration. 

The rate of photorespiration is determined by the relative concentrations of carbon 1 

dioxide and oxygen in the cell; when oxygen concentrations become too high, the photosynthetic process slows down--photorespiration becomes the limiting growth factor. 

Thus, to maintain productivity it is necessary either to provide a mechanism for oxygen 

removal from the culture during the highly productive midday period, or to select species 

that are particularly resistant to photorespiration. Culture aeration can remove excess 

oxygen--highly aerated systems have been shown not to become severely oversaturated-- 

but may be excessively costly. Significant interspecific variability in resistance to 

photorespiration may exist among algae; it has been demonstrated that algae vary in 

their ability to concentrate C02 within the cell (Miyachi, Tsuzuki, and Yagawa 1985). 

Since photorespiratory activity is determined by the relative concentrations of carbon 

dioxide and oxygen, this activity can be reduced by intracellular accumulation of C02 as 

well as by the elimination of oxygen. 

Continuous and Batch Cultures 

There are three fundamental management strategies for cultures of suspended microorganisms: batch culture, continuous culture, and semicontinuous culture. In batch culture, a small inoculum of cells is added to a large volume of culture medium, and growth 

is allowed to continue until the desired population for harvest is obtained. In continuous 
culture, once the desired population is attained (usually through a batch startup procedure), fresh medium is continuously added and the biomass product is removed from the 

system at the same rate. Semicontinuous cultures are very similar to continuous cultures, except that portions of the population are removed and medium is added at discrete intervals, frequently on a daily basis, 

Both continuous and semicontinuous cultures offer significant advantages over batch cultures since population size and growth conditions can be maintained at or near a steadystate. If these conditions are near the optimum for growth and product formation, 

system productivity can be maximized. In batch culture, the population size is not 

optimal for most of the growth cycle. Recent culture management experiments (Laws 

1984) have shown that semicontinuous strategies are particularly conducive to high production rates, although the physiological mechanism is not understood. 

Species Utilization and Rotation 

The operation of outdoor mass culture systems with harvesting and processing facilities 

can be simplified where a single algal species is maintained year round. However, the 

anticipated seasonal variations in culture conditions are wide enough that it is unlikely 

that a single species can be found which will perform optimally at all times. Thus, it 

seems more likely that mixed cultures will be employed, in which different species are 

dominant at different times. 

The harvesting and processing characteristics of these different species must be similar 

enough that capital equipment can be used all year without significant modifications, 

particularly since multispecies populations may occur during transition periods. Maintaining startup populations of seasonally adapted species can also be a significant problem. It is desirable, but far from assured, that seasonal crops will remain at low levels in 

the culture at all times, and will reappear as conditions become suitable. Careful culture 

management would be required to avoid significant drops in system productivity during 

transitions between species or strains. 

Product Manipulation - 

As noted in Section 2.1.4, the metabolism of microalgal cultures can be shifted from the 

production of cellular machinery toward the generation of storage products by nutrient 

deprivation. Lipid production can be induced by nitrogen deprivation in most algae, and 

by silicon deprivation in diatoms (Shifrin 1980). Through this mechanism, cells can be 

induced to accumulate a large proportion of their cell mass in the form of storage products. Chemical additions affecting cell biochemistry might also be utilized to induce 

storage product formation, although the economics of such mechanisms are questionable, It has not been determined whether the formation of storage products can be 

induced without significant reductions in photosynthetic efficiency. If a product induction phase is to be included in the culture system, it can be achieved by a number of 

mechanisms. 

Most simply, cultures can be grown to a certain density with excess nutrients, and then 

nutrient additions discontinued until the desired degree of product accumulation is 

realized. For a single reactor, this implies batch culture operation. It may be more 

desirable, therefore, to achieve product induction through a system of linked continuous 

cultures. In such a system, cells would be produced in the f irst-stage continuous culture 

with ample nutrients available. The outflow from the first-stage culture would become 

the input to the second-stage continuous culture, which would receive little or no 
nutrient addition. Harvested material would be drawn from the second culture after a 

residence time that would be determined on the basis of maximum product formation 

rate. The advantages of continuous culture would apply for both stages of the linked 

system. 

2.2.2 Light Utilization in Mass Cultures 

Light utilization by algal mass cultures is determined by two factors: (1) the amount of 

the incident light that actually reaches the algal cells, and (2) the efficiency with which 

the light received is utilized. Losses of incident light that are not related to the photosynthetic efficiency of the algae themselves, but that inf hence the photosynthetic 

efficiency of the culture system as a whole, include losses of light to covering materials, 

reflection by the water surface, nonproductive areas of the facility such as roads and 

earthworks, and suspended particles other than algae in the culture medium. The loss of 

light as a result of the presence of covering materials has been discussed in Section 2.1.2. 

Loss of light to reflection by the water surface cannot be avoided and eliminates about 

10% of the available radiation, although this loss is decreased somewhat by wind-induced 

ripples on the surface of the culture. 

Once all light losses are accounted for, the utilization efficiency for the remainder of 

the light is determined by the photosynthetic efficiency of the algae themselves. The 

maximum efficiency with which light energy can be converted to the stored chemical 

energy of plant biomass is the theoretical efficiency of the photosynthetic process. 

When the energy contents of the quanta received from sunlight and the thermodynamic 

efficiencies of the photochemical conversion processes are considered, maximum theoretical photosynthetic efficiencies for sunlight utilization are estimated to be about 23% 

(Radmer and Kok 1979). Experimental observations that have shown efficiencies in 

excess of this maximum have been the subject of heated debate (Aiba and Ogawa 1983). 

The practical limits of photosynthetic efficiency are below the theoretical efficiency, It 

is likely that the short-term utilization of light of low intensities can be very near this 

theoretical value, but plants typically utilize high-intensity light with much lower 

efficiency. Normally, increased light intensity beyond about 20% of full sunlight does 

not lead to a proportionate increase in the growth rates of cells. Thus, outdoor cultures 

with high light intensities have inherently low efficiencies. However, when outdoor populations are sufficiently dense, light is attenuated with depth in the cultures and only 

those cells at the surface receive light of very high intensity. In addition, algal cells can 

utilize high-intensity light at high efficiency if they are exposed to this light only for 

short periods of time. Thus, if cells in the culture are moved from the surface of the 

culture to the darker areas at depth, they can sustain a higher photosynthetic efficiency. 

Nonproductive Areas of the Culture Facility 

While the presence of nonproductive areas in the facility will reduce the overall light 

utilization efficiency, this effect is significant only to the extent that land costs contribute to the total production costs. Operating costs will be determined mainly on the 

basis of the actual area under cultivation. An exception is when barriers or earthworks 

shade parts of the cultures. This suggests that barriers between culture channels should 

be located as far apart as the hydraulics of the system will permit, and that their height 

should be minimized. 
Control of Nonalgal Turbidity in Cultures 

While care must be exercised to prevent the accumulation of dust, dirt, and chemical 

precipitates in the culture medium, with reasonable care it seems likely that the only 

major potential source of nonalgal turbidity will be bacterial biomass. In cultures 

supplied with inorganic fertilizers, bacterial populations are normally small when culture 

growth remains vigorous. Where municipal wastewater or some forms of nutrient 

recycling are employed, bacterial stocks may be much higher, due to the presence of 

organic substrates. 

Culture Mixing 

It has been demonstrated that the application of vertical mixing to outdoor cultures can 

significantly enhance their photosynthetic efficiency and hence their productivity [e.g., 

Laws et al. (1983)]. It is less clear, however, whether this sort of enhancement can be 

cost-effective, since mixing can be extremely costly. This trade-off can only be 

quantified through the resolution of several key issues, including the amount of mixing 

required to achieve a given degree of enhancement and the energy consumption of the 

hydraulic systems employed to generate the required mixing. However, mixing designed 

to enhance photosynthetic efficiency need only be applied during the few hours of the 

day when the incident light intensity is high, Mixing effects other than the direct 

enhancement of photosynthetic efficiency must also be considered in analyzing this 

trade-off; these effects include enhanced nutrient transfer across the cell boundary layer 

and the maintenance of a suspended cell population. 

2.2.3 Engineering and Construction 

Although many pond designs have been employed during the forty-year history of microalgal mass culture, a single design is now used almost exclusively. This design employs a 

shallow pond, 15-50 cm deep, configured in a racetrack or meandering style, through 

which water is circulated at a moderate rate to retain culture suspension. A motorized 

paddlewheel is commonly employed to circulate the water. The materials utilized for 

the construction of such ponds vary widely. 

Paddlewheels offer advantages over other pumping systems in the energetic efficiency of 

water movement and in the avoidance of mechanical damage to the algal populations. 

Other circulation systems have been tested as well. Air-driven systems have been a particularly popular alternative, whether in the form of airlift pumps or direct culture bubbling, but their energetic efficiency is- much lower than that of paddlewheel systems. 

There are limits to the temperature tolerance of algal species, and in many cases the 

fluctuation of temperatures in outdoor cultures of microalgae exceeds these limits, 

causing culture failure or contamination. The identification of temperature-tolerant 

algae has been a major objective of the SERI program, but absolute temperature control 

in outdoor cultures may be possible. 

Pond Configuration 

The pond design described above has been widely accepted for several reasons. Construction is inexpensive relative to most of the other designs proposed, and culture circulation is achieved efficiently. However, this design has not been the most productive 
of all designs tested (Terry and Raymond 1985); its selection is based on productivity/ 

cost trade-offs, rather than on the optimization of production. While the merits of the 

presently accepted design should be recognized, it should not be considered to be either 

optimum or final; innovation should be encouraged. 

Pond Materials 

Ponds have been constructed with a wide variety of bottom materials, including compacted soils, a thin blanket of crushed rock or other erosion-resistant material over 

impervious soil, soil cement, concrete, and plastic liners. These materials vary in roughness, stability, permeability, and resistance to failure. Surface roughness determines 

both the resistance to water movement and the potential for cell aggregation. Unstable 

bottom materials are subject to shifting and settling, which can destroy the original 

hydraulic design of the facility. Permeability or leakage (particularly in the case of 

plastics) can lead to the loss of both water and algae. Failures, including cracking of the 

bottom and the breakdown of the culture sides, lead to high maintenance cost and lost 

production. The construction of very large culture facilities for products such as fuels, 

in view of the potential scale of the technology, could support the development of 

specialized techniques for lining once design requirements and costlperformance tradeoffs have been identified. 

Culture Circulation 

The paddlewheel circulation system described above is regarded as the best design currently available. Again, it cannot be accepted as the optimal design, and research in 

alternative circulation systems is ongoing (Terry and Raymond 1985). 

Artificial Temperature Control 

Culture heating and cooling by conventional means can almost certainly be eliminated 

for energy production systems, since the necessary energy input would outweigh the 

energy production of the system and lead to an unacceptable negative energy balance. 

Alternative techniques, including passive solar and geothermal heating deserve consideration, although they pose significant logistic barriers. Alternative techniques for culture 

cooling will be particularly difficult to develop, and the selection of species that tolerate 

high temperatures appears to be a desirable objective since it offers a means of rnitigating the deleterious effects of temperature. 

2.2.4 Harvesting 

General Harvestine: Considerations 

Techniques for harvesting microalgae include settling or flotation, centrifugation, and 

filtration. These processes are aided by cell flocculation, either through the addition of 

chemical f locculants or through culture autof locculat ion. Flocculation causes the cells 

to become aggregated into larger clumps; these clumps are more easily filtered and settle more rapidly, either through gravity settling or through accelerated settling by centrifugation. Flotation is achieved through the introduction of small bubbles into the culture medium; algae are attracted to these bubbles and lifted to the surface for removal. 

Alternative harvesting techniques. The present slate of harvesting techniques for microalgae is costly both in terms of capital investment and in terms of energy input. It is not 

clear whether these techniques can be significantly improved, but the -total engineering 
investment in developing these techniques to date is small relative to the potential scale 

of the technology. Continued innovation and research in the area of microalgal harvesting may offer significant improvements in harvesting costs. 

Salinity effects on harvesting procedures. Many of the procedures developed for the harvesting of algae from freshwater systems or from wastewater systems have been based 

on the interaction between cells and various charged particles employed as flocculants. 

Since the effectiveness of these flocculants is significantly reduced in waters of high 

ionic strength, greatly increased doses of f locculant are required to achieve the desired 

effect at high salinities. 

Chemical flocculation. Chemical flocculation substantially increases cell harvestability, 

particularly for small unicellular species. However, the addition of flocculants introduces chemicals into the product stream and into the culture itself if the medium is to be 

recycled after harvesting. Autoflocculation processes can take advantage of pH 

increases caused by the growth of the algae to stimulate flocculation, but these processes probably depend on-the formation of precipitates such as calcium phosphate in the 

medium, and these ions must subsequently be replaced. In addition, autoflocculation processes are less effective at high salinities. 

Biological As~ects of Harvesting 

The harvestability of microalgae depends primarily on the organism's size, which determines the settleability and filterability of the species. Cell motility may also have a signif icant effect on harvesting processes that depend on settling. Cell surface charge, 

aggregation properties of the cells in culture, and behavioral characteristics of the cells 

may enhance or decrease their harvestability. 

Species harvestability. The most rapidly growing algal species are frequently very small, 

and often motile, unicells, which are among the most difficult to harvest. It is necessary 

to maintain an effective interaction between the development of harvesting technologies 

and the isolation and select ion of algal species for possible mass culture applications to 

ensure that the species selected can be harvested by techniques presently in practice or 

under development. While harvestability cannot be an absolute requirement for species 

selection, species readily harvestable by known techniques should receive preferential 

attention in species development activities. 

Environmental manipulation of harvestability. Many of the harvesting processes currently in use depend on the electrical charge at the surface of microalgal cells. There is 

some evidence that the strength of this charge varies with the growthconditions of the 

algae (Sherbet 1978). If so, the growth conditions might be manipulated to improve cell 

harvestability. Characteristics such as cell size and colony formation can also be manipulated through variation of growth conditions and can affect harvestability. Where 

growth conditions are manipulated to improve harvestability, however, the effects of 

these manipulations on production must be taken into account. 

Biological activity in harvested material. For harvesting techniques such as settling, 

which takes place relatively slowly and may require a long residence time, consideration 

must be given to the biological activity within the concentrated material. Metabolic 

activity of the cells can lead to the development of anaerobic conditions, cell death, and 

decomposition by autolytic and bacterial processes. Storage of algal cells in the dark 

will lead to storage product utilization and loss of the fuel product. Where timedependent harvesting processes such as long-term settling are to be utilized, such potential losses rnust be considered. 
2.2.5 Summary 

The wide variety of unresolved issues in mass culture technology is strongly indicative of 

the state of the art. As an emerging biotechnology, microalgal mass culture remains in 

its infancy. Although present microalgal technologies, based on high-value products, can 

survive without the complete resolution of these issues, systems for the production of 

high-volume, low-value products such as fuels will require resolution of key issues. 

These issues form the basis for the identification of research needs. The broad range of 

questions that must be answered, however, indicates that priorities for research activities must be established, and that the questions most crucial to the development of process viability must receive priority, The identification of the most crucial questions, 

however, is not always straightforward. Our economic analysis that begins in Section 3.0 

examines the response of product cost to various systems parameters; the relative sensitivity of product cost to these parameters can help to suggest priorities for research. 

2.3 Resource Requirements 

As part of the development of the mass microalgal culture concept, SERI has conducted 

and sponsored studies to determine the process requirements for major resources. A 

study by Vigon et al. (1982) described the resources of the desert in the southwestern 

United States and factors affecting their availability for production of microalgal or 

emergent aquatic plant species. Resource availabilities are outlined in this study along 

with recommendations for measuring the quantity of each resource available at a particular site. 

A subsequent resource evaluation by Maxwell et al. (1984) suggested screening procedures for resource evaluation. Land, water, and climatic criteria were established, quant itative and qualitative data were assembled, and through the use of quantitative 

weights, multivariate criteria were combined into a single measure of "suitability" for 

the Southwest. 

These studies discuss relevant resource requirements and related issues that are too 

extensive to reiterate here. Instead, we summarize the major resource issues that relate 

to land, climate, and water. For carbon dioxide, we provide a more extensive synthesis 

of several evaluations of supply options and competing demands for this critical resource. 

2.3.1 Land Requirements 

The mass culture of microalgae can be envisioned as a series of ponds connected by 

hydraulic and utility grids. Several physical characteristics of the land affect the construction and maintenance of the ponds. We group these characteristics into two general 

categories, physical requirements and location requirements, and briefly review concepts 

that are discussed in detail elsewhere (Benemann et al. 1982; Vigon et al. 1982; Maxwell 

et al. 1984). 

Phvsical Reauirements 

To maintain culture hydraulics in a flowing culture 10-20 cm deep, ponds must be graded 

to acceptable tolerances and maintained within these tolerances. Land of uneven 

topography or severe slope presents difficulties in the grading and preparation of ponds; 
the flatter the land, the less grading is required to achieve the basic pond gradients. Soil 

type also affects site preparation costs through grading requirements and pond design. 

Soils of low porosity and permeability may provide sufficient barriers to water loss to 

preclude the use of liners (Dodd 1984). 

Locat ion Requirements 

The physical requirements of land for sustaining algal culture place no strict contraints 

on their location. Factors such as ownership can exclude certain tracts under existing 

social or political arrangements, but these factors relate to prevailing political, social, 

and economic conditions and do not reflect the requirements of the algae. Climatic conditions, which are joint properties of land resources, are important factors in algal culturing and are discussed next. 

2.3.2 Climate Requirements 

Since microalgae rely on sunlight to energize their metabolism, the availability of sunlight is a major factor in determining the production rate. Average daily, monthly, and 

annual insolation determines the total energy input to the culture. Factors such as cloud 

cover and atmospheric turbidity also limit the amount of sunlight that reaches the culture and reduce the net energy available for production (Maxwell et al. 1984). 

Other climatic conditions do not necessarily preclude mass culture of microalgae but 

may limit production. Rain or snow dilutes open cultures and may temporarily disrupt 

culture salinity balance and reduce productivity. Storms can deposit dust or debris in the 

culture and inhibit algal growth. Violent climatic events can damage the facility or its 

components, but these risks are undertaken in any agricultural enterprise and are not 

absolute barriers to feasibility. 

2.3.3 Water Requirements 

A major factor in determining water needs is evaporation, the rate at which water is lost 

to the atmosphere. Evaporation rates are characteristic of the environment and, unless 

reduced by some protective device, are fixed and calculable by site. 

Water demand is further influenced by the source water characteristics and the algae 

themselves. Algae are endowed with a specific salinity tolerance. A closed system can 

maintain salinity at or near the salinity of the source water. Open systems suffer losses 

through evaporation; thus salinity rises and eventually the tolerance limits can be 

exceeded. The blowdown process expels water from the culture, replacing it with the 

source water and lowering salinity to within tolerance. However, water demand is 

increased through blowdown, which is required more often for more saline source water 

and for greater evaporation rates. Harvesting can also increase water demand in an open 

system lacking complete recycling. Precipitation could reduce water demand but is 

beneficial only if it does not endanger culture maintenance. 

Water demands for mass culture of microalgae depend on complex, interrelated issues 

and factors, some dictated by natural conditions and some determined by management 

strategies and therefore by system economics and objectives. At a minimum exact calculations of culture water demands require sufficient site specificity to determine 

evaporation rates and source water salinity. With these dimensions fixed, trade-offs in 

water costs becomes important determinants in optimizing depth, blowdown, and harvesting strategies. 
2.3.4 Carbon Dioxide Supply and Demand 

This section discusses the potential supply and demand of carbon dioxide in the southwestern United States. The importance of carbon dioxide as an inorganic nutrient for 

mass cultivation of microalgae is described in Section 2.1.3. Previous studies have suggested that the lack of adequate supplies of carbon dioxide might be a severe constraint 

to optimal production (Renemann et al. 1982; Nelson 1982; Vigon et al. 1982). 

A multimillion dollar industry supplies carbon dioxide for industrial uses such as chemical 

manufacturing (e.g., urea and methanol), refrigeration, and beverage carbonation. 

During the 1970s industrial demand for carbon dioxide increased at an average annual 

rate of 7.3%. In addition, use of CO in enhanced oil recovery (EOR) projects has 

created a new, potentially large dernanj that could significantly impact the price and 

supply of C02 (Meegan 1977). 

National Demand for Carbon Dioxide for Enhanced Oil Recovery 

The demand for carbon dioxide in enhanced oil recovery (EOR) projects will depend on 

the future price for crude petroleum and government tax policy. At lower crude petroleum prices, the value of carbon dioxide flooding declines dramatically because EOR projects require heavy front-end investments and the purchase of carbon dioxide represents 

about 47% of the production cost. Carbon dioxide-based EOR projects currently account 

for over 6.5% of all EOR-produced oil in the United States. 

In a recent study on enhanced oil recovery, the National Petroleum Council (1984) 

estimated that an additional 14.5 billion barrels (bbl) of oil could be produced nationally 

using current EOR technology with stable ($30-$35/bbl) petroleum crude prices. Carbon 

dioxide miscible flooding is anticipated to contribute over 38% (or 5.5 billion bbl) to this 

incremental increase in recoverable oil reserves. Using NPC projections, C02-based oil 

production is expected to increase from the current 31,300 bbl/day to a peak of 

500,000 bbl/day by the year 2000. After 2010, this production rate is predicted to decline due to reservoir depletion. 

Total carbon dioxide demand for the EOR proj cts in the United States has been esti- 5 mated to range from 850 billion to f .7 trillion m . This estimate is based on a C02 utilization rate between 140 and 280 m per barrel of recovered oil. Daily demand estimates 

for carbon dioxide for EOR projects depend on assumptions regarding extended lifetimes 

for oil fields using COZ recovery. Lifetimes for carbon dioxide fields range from 10 to 

20 years, depending on the characteristics of the reservoir and price of C02. Through 

the lifetime of an EOR project, the yield of oil diminishes to a point where it is no longer 

economical to inject CO for additional recovery. However, over the next forty ears, 

the national average dai ? y demand for COZ fs estimated to be over 100 million m Y /day, 

assuming net CO requiremyts of 280 m /bbl. The peak daily demand for C02 is 

estimated to be l& million rn for an oil production rate of 500,000 bbl/day. 

Demand for CO? in the Southwestern United States 

The demand for carbon dioxide for EOR is not uniformly distributed throughout the 

United States. In fact, the NPC study (1984) indicates that over 56% of the oil reservoirs 

most amenable to CO recovery are located in the Permian Basin of West Texas and 

eastern New Mexico. $he Permian Basin is estimated to contain 3.1 billion bbl of oil 

recoverable using COZ. Of the 63 C02 projects under production or construction, 35 are 
Saline Groundwater 

In its usual sense, groundwater 

is defined as subsurface water 

below the water table in fully 

saturated geologic and soil 

formations. A broader concept 

includes very deep, saturated 

geologic formations and nearsurface, unsaturated soil 

moisture regimes. Inflow to the 

hydrologic system occurs as 

precipitation in the form of 

rainfall and snowmelt. Water 

may then move by surface flow, 

subsurface flow, and 

evapotranspiration from land to 

the ocean or atmosphere. 

Water arriving at the soil 

surface as rain or snowmelt can 

be described chemically as an 

extremely dilute, slightly to 

moderately acidic, oxidizing 

solution containing low 

quantities of dissolved solids 

[several to tens of milligrams 

per liter (mg/L)]. As it infiltrates 

the soil zone, water usually 

becomes considerably more 

acidic due to soluble C02- 

from root respiration and 

oxidation of organic matterwith which it reacts to form 

carbonic acid (H2C03). The 

H,CO,-enriched water 

encounters minerals that are 

dissolvable in numerous 

sequences of reactions within a 

system that is more or less 

continuously recharged with 

additional inputs. As 

groundwater moves along its 

flow path, the content of 

dissolved solids and major 

ions increases, ultimately 

becoming similar in 

composition to seawater. With 

increasing depth below the 

surface, residence time, and 

sluggishness of flow, the 

dominant anions tend to evolve 

from bicarbonate (HCO;) to 

sulfate (Sod2-) to chloride (CI-). 

Solubility sequences among 

the major cations-potassium 

(K+), sodium.(Na+), calcium 

(Ca2+), and magnesium (Mg2+) 

-are more variable due to 

cation exchange reactions. The 

species and concentration of 

both cations and anions are 

affected by the geochemical 

characteristics of the lithology 

r. 

Hydrologic Effects of Pumping Saline Groundwater 

Evapotranspiration 

Recharge 

A Groundwater Classification 

Based 

-- 

on Total Dissolved Solids 

Description g TDS/L 

Fresh 0-1 

Brackish 1-10 

Saline 10 - 100 

Brine >I00 

through which the groundwater 

moves. Chloride content, for 

example, may be substantially 

increased if the flow 

encounters strata of evaporated 

marine basins. 

The tendency of groundwater 

to become mineralized is most 

pronounced in arid regions 

such as the U.S. Southwest, 

where the potential evaporation 

exceeds the precipitation. 

Near-surface strata and soils 

frequently become saline or 

alkaline due to deposition of 

dissolved minerals during 

evaporation of soil moisture, 

including that carried upward 

by capillary flow. In confined 

topographic basins, which are 

common in the Southwest, the 

groundwater may gain 

dissolved minerals as water 

movement alternates between 

downward infiltration and 

upward migration in response 

to discontinuous recharge and 

the evaporative pull. At greater 

depths, the mineralization 

sequence may be enhanced by 

the occurrence of salts in ancient 

marine deposits. 

Groundwater can be classified 

according to the amount of 

dissolved inorganic chemical 

constituents, or concentration 

of total dissolved solids (TDS), 

it contains. One commonly 

used scheme is presented in 

the table above. Water 

containing up to 2 to 3 gTDS/L 

may be tolerated for human 

consumption provided toxic 
Saline Groundwater 

The Hydrologic Cycle 03 FA Evapotranspiration \ Overland f f 

\ Flow Evaporation 

'. Groundwater Flow ,f , 

constituents such as selenium, 

lead, and arsenic are virtually 

absent. Livestock may tolerate 

from 4 to 10 g TDS/L. Most 

commercially useful plants 

have lower tolerances than 

humans and livestock. 

Seawater has a TDS 

concentration of about 35 g/L. 

The TDS concentrations of 

confined basins such as Great 

Salt Lake substantially exceed 

that of seawater. 

The occurrence and 

characteristics of saline 

groundwater in the U.S. 

Southwest are poorly known 

due to lack of interest in it as a 

resource. The principal source 

of avaitable information is the 

Geological Survey of the U.S. 

Department of Interior. Existing 

data can be viewed as data of 

opportunity-that is, they have 

resulted largely from other 

purposes, such as exploration 

for petroleum or fresh water, 

rather than from deliberate 

saline groundwater 

investigations. 

If three major phases of 

groundwater development are 

identified as exploration, 

evaluation, and exploitation. 

the status of our understanding 

of saline groundwater in the U.S. 

Southwest lies somewhere 

between exploration and 

evaluation. The occurrence of 

major aquifer systems is 

probably known, but their 

characteristics of recharge, 

discharge, and confinement are 

not well understood. Even more 

sparse is information on 

hydrologic features such as 

lithology, chemical quality, 

prospective yields, and 

potential undesirable side 

effects. Information developed 

with models and field data 

collection, such as electric logs 

and pumping tests, are needed 

to determine the location, 

number, spacing, yield, and 

effects of wells. Development 

of saline groundwater for 

microalgae culture should, in 

particular, consider effects 

such as land subsidence, fresh 

water contamination, and drawdown of the water table. 

References 

1. Davis, S.N., 1974, 

Hydrogeology of Arid Regions, 

in Brown, Jr., G.W. (ed.) Desert 

Biology, Volume II, Academic 

Press, New York. 

2. Feth, J.H. et al., 1965, 

Preliminary Map of the 

Conterminous United States 

Showing Depth to and Quality 

of Shallowest Ground- Water 

Containing more than 1,000 

Parts Per Million Dissolved 

Solids, Hydrologic 

Investigations Atlas HA-1 99, 

Geological Survey, U.S. 

Department of the Interior. 

3. Freeze, R.A. and J.A. Cherry, 

1979, Groundwater, PrenticeHall, Inc., Englewood Cliffs, NJ. 

4. Summary Appraisals of the 

Nation's Ground- Water 

Resources, Professional Paper 

81 3 series, Geological Survey, 

U.S. Department of the Interior. 

5. Maxwell et al., 1984, 

Resource Evaluation and Site 

Selection for Microalgae 

Production Systems, SERI/TR21 5-2484, NTIS, Springfield, 

VA. 
in the Per ian Basin. T tal carbon 

880 billion m (120 million 3 m 9 /day) over 

Permian Basin is based on a shorter time 

oil fields within the Permian Basin are 

depleted first. 

dioxide demand in this basin could reach 

the next twenty years. This estimate for the 

frame than the national estimate because most 

expected to be developed first and therefore 

Comparable estimates for carbon dioxide demand were developed in a study conducted 

jointly by Science Applications, Inc. (SAI) and several large petroleum production companies. The demand data developed in this report, presented in Table 2-1, indicate a 

similar, albeit higher, total demand estimate for C02 in the Permian Basin. This inconsistency in demand forecast is due to different assumptions in net C02 utilization, reservoir lifetime, and crude oil prices between study periods. The geographic distribution in 

Table 2-1 presents a higher percentay of C02 utilizat'on in the Permian Basin, where 1 total demand for C02 is 1.2 trillion m or 230 million m /day. If reservoir lifetime were 

assumed to b twenty years, the adjusted C02 demand from the SAI study is about 5 180 million rn /day. This daily d mand estimate is more consistent with NPC GO 5 demand projections (120 million rn /day) given anticipated real oil price increases an2 

new EOR technologies in the future. 

In summary, the demand for C02 fpr enhanced oil recovery in the Southwest could vary 

from 120 million to 180 million m /day over the next twenty years if crude oil prices 

remain relatively stable (in real terms) between $30 and $4O/bbl. Lower crude prices 

would reduce C02 demand because EOR would be less profitable. Higher prices might 

accelerate EOR profits and C02 demand, or stimulate drilling and result in the opposite 

effect as a result of tax benefits for new sources of oil. 

Carbon Dioxide SUDD~V - National Estimates 

Carbon dioxide is obtained from three major sources: 

Naturally occurring C02 deposits or GO2 located in conjunction with natural gas 

deposits; 

Table 2-1. Potential C02 Demand by Petroleum Basin 

Daily Daily 

Total Demand over Demand over 

Basin Demand a 15-yr a 20-yr 

m3) Period 6 3 (10 m ) 

Period 

(lo6 m3) 

Permian 1.28 233.1 174.82 

W illiston 0.03 5.51 4.1 1 

Appalachian 0.0 1 1.93 1.42 

Los Angeles 0.05 8.75 6.54 

Total 1.37 249.30 186.88 

3 3 To convert cubic meters (m ) to cubic feet, divide m by 35.31. 

Source: Anada et al. 1983. 
By-product C02 from the manufacture of chemicals, fertilizers, alcohol, and coal 

gasification plants; and 

C02 recovered from the flue gas of fossil fuel combustion plants. 

National reserves and by-product sources of COZ, developed by SAI, are pre ented in 3 Table 2-2. The national supply of carbon dioxide is estimated at 1.7 billion m /day, of 

which natural deposits are 2.2% and power plants are almost 90% of the total supply. 

Natural deposits of carbon dioxide have been discovered in many parts of the United 

States. These known deposits, shown in Figure 2-1, are generally located in the southwestern United States and Appalachia, in close proximity to the petroleum basins amenable to EOR techniques. These natural deposits are typically capable of producing 

83%-97% pure carbon dioxide, with reservoir pressures ranging from 600 to 3500 psig. 

These unique characteristics Led to recent interest in known deposits by petroleum companies pursui3g carbon dioxide EOR. Clearly, estimated natural deposits of 

38.7 million nj /day cannot meet estimated of peak EOR demands of over 

120 million m /day, 

Power plants are the single largest potential source for carbon dioxide. In contrast to 

natural deposits of carbon dioxide, power plants are widely distributed geographically, 

and the flue gas, at atmospheric pressure, contains up to 16% carbon dioxide. These 

impure, low-pressure carbon dioxide sources are distributed over wide regions and currently are not widely used by EOR operators. Utilization of flue gas in appreciable quantities at locations distant from the power plant requires collection, purification, compression, and transport of the carbon dioxide. 

These additional requirements to obtain pure, high-pressure carbon dioxide result in large 

capital investments and operating cost. Most EOR project analyses limit estimates of 

carbon dioxide supply from flue gas power plants and by-product plants to within an 

800-krn radius of the oil field (Anada et al. 1983). This constraint on the maximum 

economic transp rt distance lowers the national potential carbon dioxide supply for EOR 9 to 654 million m /day or 38% of the total available supply. 

Table 2-2. Total Potential C02 Supply 

Source 

Natural C02 deposits 

Power plants 

Cement plants 

Ammonia plants 

Ethylene oxide plants 

Ethanol plants 

Natural gas processing 

Hydrogen plants 

Fluid catalytic cracker unit 

regenerator 

Total -- 

Source: Anada et al. 1983. 

29 

Natural deposits and flue gas sources represent the most feasible supply of carbon 

dioxide for enhanced oil recovery and also for microalgae production. Natural deposits, 

although a low percentage of the total supply, are capable of producing C02 at required 

purity and pressure levels for both EOR and microalgae projects. 

Carbon Dioxide Supply in the Southwestern United States 

3 In their supply study, SAI estimates that over 61 7.6 million m /day of carbon dioxide are 

potentially available within 800 km of the geographical boundary of the Permian Basin 

(~nada et al. 1983). This supply estimate includes natural deposits of C02, by-product 

C02, and £lye gas streams from present and proposed plants. Presently about 

360 million m /day of C02 are available from existing plants in the region. Table 2-3 

lists these supply sources along with additional supplies that would become available as a 

result of completion of proposed C02 by-product plants in the region, about 80% of 

which is from power plant flue gas. 

The SAI study concluded that low-cost carbon dioxide will be available only from large 

point sources such as coal- or gas-fired power plants. Carbon dioxide recovery technology based on extraction processes such as aqueous monoethanolamine (MEA) or 

Table 2-3. Carbon Dioxide Supplies in 

the Permian Basin 

Source 

Natural deposits 

Existing by-product plants 

Power plants 

Cement plants 

Ammonia plants 

Ethylene oxide plants 

Ethanol plants 

Natural gas processing 

Hydrogen plants 

Refineries 

Total existing by-product 

Proposed by-product plants 

SNG plants 

Power plants 

Ethanol plants 

Ethylene oxide plants 

Refineries 

Hydrogen plants 

Total proposed 

a~ncludes natural C02 from Jackson Dome area. 

Source: Anada et al. 1983. 
potassium carbonate K 0 ) roduces carbon dioxide from flue gases in a price range (2% 3 from $2.95 to $8.82/10 m (1984 $) depending on actual recovery rate, fuel type, and 

volume. Additional costs include compression, drying, and transport of C02 from the 

point source to the user. 

Therefore, the actual supply of C02 from natural deposits and flue gas for EOR projects 

in the Southwest will depend on whether individual algae projects can secure sufficient 

quantities of low-cost C02 and transport the carbon dioxide to the algae fuel farm at 

costs that are less than its value in EOR projects. Natural deposits cannot meet the 

entire projected EOR demand for CO but sources of by-product carbon dioxide may be 

required to sustain extensive EOR pro 5' uction through CO flooding methods. This potential shortage of carbon dioxide has stimulated a great dea of research on developing new 

technologies to extract carbon dioxide from flue gases. 

z 

Im~lications for Microalgae Develo~ment and Utilization 

The preceding discussion of C02 supply and demand in the southwestern United States 

indicates that while sufficient quantities of carbon dioxide exist to meet expected future 

EOR demand, there may be spot shortages due to high development costs at certain locations. Because of these costs, extensive research in further development of C02 extraction from flue gas is being actively pursued by a number of companies. 

Research in improving flue gas utilization for potential EOR projects also will aid in the 

economical recovery of C02 for microalgae production. However, microaigae technology is expected to compete with these EOR projects for available sources of carbon 

dioxide. Within the Southwest, natural deposits will probably be developed just because 

of the desirable characteristics of the carbon dioxide from these sources. At present, 

three major natural C02 deposits in the Southwest--McElmo Dome, Sheep Mountain, and 

Bravo Dome--have ijlready been developed by petroleum companies. Their potential 

yield is 25 million rn /day delivered to West Texas. 

Economics of EOR dictate that beyond 800 km, the cost of transporting C02 is prohibitive. In addition, there is a question of scale in supplying C02 to EOR fields: the large 

quantities of C02 required for EOR must come from large power plants. While microalgae production requires more C02 per barrel of oil produced, the total C02 supply 

requirement is typically less than for large oil projects, and smaller power plants may be 

viable sources of COZ. Microalgae culture, which is more dispersed than concentrated 

oil projects, makes utilization of power plant C02 feasible, especially if algal facilities 

are located near existing power plants. 

A final consideration is the time frame for C02 demand. EOR projects within the 

Permian Basin are being developed now for C02 flooding. With an expected project lifetime of 20 to 30 years, many reservoirs will be depleted in the next 30 to 50 years. This 

depletion will weaken the demand by EOR projects for C02 at the time microalgae 

development is expected to grow. As one C02 consultant to the oil and gas industry 

reported, in about 30 years the Permian Basin is expected to be the world's largest 

underground source of C02 (Petzet 1983). 

In summary, there is good reason to believe that sufficient quantities of carbon dioxide 

will be available for microalgae production for liquid fuels. The supply sources for 

microalgae production are expected to be existing and planned power plants in the 

Southwest. In Arizona and New Mexico, existing power plant capacity is 13,939.3 MW, 
3 producing 122 million m /day of COZ. An additional 14% increase in capacity is planned 

over the next five to ten years (Energy Information Agency 1983). While EOR demands 

may utilize part of this supply between now and the year 2000, depletion of oil reserves 

after 2000 should free large supplies of C02 for microalgae production. 

2.4 Product Alternatives 

The major objective of the fuel products portion of this analysis is to determine how best 

to exploit the chemical composition of the algae to produce fuel products. The issue of 

comparing species product composition with fuel chemical requirements has been discussed in Feinberg (1984). Before examining the variety of potential fuel products, we 

first examine the properties that make an acceptable fuel product, both to producers and 

consumers. An assessment of the commercial fuel product network, both present and 

future, is presented since any promising fuel product will ultimately have to fit into that 

network. A brief characterization of the microalgae feedstock (as produced at the culture facility and fed to the fuel production facility) is presented to establish the basis for 

determining appropriate process requirements for converting algal chemical constituents 

into fuels. 

2.4.1 Functional Properties 

The first step in the evaluation of alternative fuel products is comparison with the physical, chemical, and functional properties of conventional fuels. Physical properties 

include the state of the fuel (solid, liquid, or gas), the density or specific gravity, the viscosity, various thermal properties such as boiling point and freezing point, and other 

complex properties such as cloud point, pour point, and flash point. The state of the fuel 

must be suited to its application: liquid fuels make the best fuels for transportation 

because they flow well, are stored easily, and provide a large amount of energy per unit 

volume. Gaseous fuels work well when no storage is available or necessary (e.g., pipeline 

systems), and solid fuels are best for stationary or semistationary applications (e.g., 

locomotives, power plants). 

The density and viscosity of liquids and gases primarily affect the design of pumping and 

storage systems, Viscosity is a critical parameter for oxygenated diesel fuel substitutes 

(Ryan et al. 1984; Hill and Feinberg 1984). The thermal properties must be matched by 

the conditions of usage. For fuel products that are mixtures of components rather than 

pure substances, the mixture boils (or freezes) at a range of temperatures rather than at 

a discrete point but standards are usually set relative to some critical point in the range. 

In the case of diesel fuels, the point below which the fuel cannot be poured is the pour 

point, which is an indicator of performance; the fuel will not flow and burn efficiently 

below this point. Engine manufacturers generally do not recommend diesel fuel use 

below the cloud point--the point at which the fuel becomes cloudy and waxes start to 

form (Lane 1980). Cloud points are typically 6°-~~o~ above the pour point. The flash 

point is the lowest temperature at which the fuel will ignite in the presence of an open 

flame. Among liquid fuels, gasoline has the lowest (best) flash point, about -50'~ 

(-45'~)~ while methanol and ethanol have flash points that are often above ambient ternperatures, 52'~ (1 1'~) and 70'~ (21°c), respectively. 

In terms of chemical properties, the first and most important distinction between nonrenewable (fossil) fuels and those typically derived from biomass is that the constituents of 

petroleum, natural gas, and coal are all hydrocarbons--compounds composed entirely of 

carbon and hydrogen. In contrast, most biomass-derived fuels contain oxygen as well, 
which changes many of the inherent physical and chemical properties of the fuel in 

combustion. For the purposes of this discussion, the most important property change is 

the decrease in heating value of the oxygenates compared to the hydrocarbons. 

Oxygenates are in a partially oxidized state; complete oxidation (combustion) of 

oxygenates liberates less energy than hydrocarbons, which are in a completely reduced 

state. Table 2-4 compares the typical lower heating value* of gasoline with those of 

ethanol and methanol. The volumetric heating value of ethanol is two-thirds, and 

methanol one-half, that of gasoline. 

Another important property to be considered in evaluating fuels for internal combustion 

(gasoline) engines is the octane number. Primarily a measure of the fuel's resistance to 

engine knock, a fuel's octane number is enhanced by the addition of aromatics such as 

benzene and toluene, as well as olefins, but is diminished by paraffins and naphthenes 

(cycloparaff ins such as cyclohexane). The least expensive octane-enhancing measure has 

historically been the addition of small quantities of tetraethyl lead (TEL). But, as lead 

emissions have continued to increase, allowable limits are being progressively decreased 

by the U.S. Environmental Protection Agency (EPA). Refiners are looking more aggressively for alternative, unleaded octane enhancers. 

For petroleum-derived diesel fuel, t h e c e t a n e number is a measure of the fuel's ability to 

start at low air temperatures, provide engine warrnup without misf iring or producing 

white smoke, and run smoothly with a minimum of varnish or carbon deposits (Lane 

Table 2-4. Energy Content of Some 

Liquid Fuel Products 

Products ~tu/lb~ Btulgalb 

~asoline~ 18,900 1 15,400 

Ethanol 11 ,500 75,700 

Methanol 8,600 56,600 

Dies 1 No. 2 8 18,500 129,500 

PVO 17,000 123,000 

Methyl ester fuel 15,900 1 16,600 

'TO obtain MJ/kg (or GJ/t) divide Btu/lb by 

430.9. 

b~o obtain MJ/L divide Btu/gal by 3,588. 

C~ypical of unleaded regular gasoline. 

d~seudo vegetable oil, an oleaginous product 

similar in composition to vegetable oils such as 

corn oil. 

Sources: McCallum et al. 1982; Tahir 

et a!. 1982. 

*The lower heating value of a fuel is the energy that can be recovered from it when the 

water of combustion is released as a vapor, i.e., when no additional energy is recovered 

by condensing the water to liquid in a heat-exchanger. When this additional step is taken, 

the total energy recovered from the fuel is known as the higher heating value. 
1980). The validity of the cetane number for nonpetroleum-derived fuels is in question 

and is the subject of current research (Pryde 1984; Ryan et al. 1984). Diesel fuels 

marketed in the United States have cetane numbers ranging from 35 to 65. Most manufacturers of truck and bus engines and of diesel locomotives specify use of fuels with a 

cetane number of 45 or above. Manufacturers of tractors and heavy construction equipment generally recommend a cetane number of 40 or above. The types of hydrocarbons 

that contribute to an increased cetane number are paraffins and naphthenes, the same 

compounds that would lower the octane number of a gasoline. 

A detailed examination of fuels from biomass product is required to verify, if not identify, the valuable properties present. Octane enhancement is a good example of how a 

renewable fuel (ethanol) can improve a particular property of the nonrenewable fuel 

(gasoline) to which it is added. It has been hypothesized that a microalgae-derived fuel 

additive would improve the cetane number or the viscosity of diesel fuel. However, 

establishing if a microalgae-derived fuel is compatible with diesel fuel or could substitute for it requires rigorous evaluation of processing options and the product fuel. 

2.4.2 Overview of Fuel Product Network 

Of the three primary fuel sources in use today, petroleum provides the greatest variety 

of fuel products. Figure 2-2 shows schematically the fuel products produced from petroleum. Transportation by highway, railroad, and air are all represented, a s a r e industrial 

and residential boilers and furnaces. As the feedstock characteristics or the product 

requirements change, the refinery can be adjusted to produce new product slates, such as 

more heating oil in the winter. Note that this figure includes refinery products only, not 

the network of petrochemicals. 

Figures 2-3 and 2-4 illustrate the corresponding networks for natural gas and coal, the 

other two primary fuel product sources. Natural gas is a precursor of fewer products 

than petroleum, which is not surprising since it contains fewer components. Coal has a 

much greater variety of possible products, but only a few of them, notably benzene, 

toluene, and xylene (known collectively as BTX), plus coke and fuel gas, are utilized 

directly as fuels. The few entries under coal and natural gas contrast with the large 

variety of products derived from petroleum. The petroleum network is so tightly integrated that it is extremely hard to introduce new products into that market. If the ultimate objective is to displace an entire barrel of crude oil, then a whole set of substitutes, virtually an entire set of new products, must eventually be found. This makes the 

problem considerably more complex than simply developing new fuel products one at a 

time. However, alternative fuels with one attractive feature, for example ethanol and 

methanol with high octane values, have an opportunity for market entry, especially when 

they complement fuels derived from conventional sources and processes. 

2.4.3 General Characteristics of Crude Microalgae Feedstock 

To bridge the gap between the products produced by the microalgal culture facility and 

the various fuel production options, we must consider in detail the physical and chemical 

nature of the products produced by the culture system. Crude algae biomass is a thick 

slurry that consists of about 10% solids by weight and the components lipid, carbohydrate, protein, intermediates, and ash. Intermediates are low-molecular-weight substances that do not belong to the first three categories. Ash is the inorganic fraction, which 

does not contribute to the fuel value of the algal feedstock. 
Microalgal Lipids 

Q) 

, Microag; Lipids , a I. 

z 

Polar, membrane bound lipids Nonpolar, storage lipids 

Glycolipids Phospholipids Triglycerides lsoprenoids Hydrocarbons 

Lipid Composition of Microalgae 

C 

A 

Storage 

Membrane bound polar lipids 

Micrograph of Algal Cell 

Microalgae are composed of three 

classes of organic,compounds, 

proteins, carbohydrates and 

lipids. Lipids are high-energy 

compounds (about 39 MJ/kg). 

Those required for cell membrane 

integrity are called polar lipids 

and those stored within the cell 

for energy reserves are called 

nonpolar lipids. 

feedstocks for fuel production 

due to their fundamental 

structural and chemical 

characteristics. Microalgae 

form vesicles (globules) of 

storage lipids within the cell 

that are more easily extracted 

chemically or mechanically 

than membrane lipids. 

Furthermore, storage lipids can 

be used, as extracted, as a 

diesel fuel substitute 

(triglycerides) or as a refinery 

blendstock (hydrocarbons). 

Additional processing can 

convert these storage lipids, 

especially triglycerides, into 

higher-value fuels such as ester 

fuel (a diesel fuel substitute) or 

gasoline. 

Lipids from growing cells 

typically are 50-70% membrane 

lipids but under stress, such as 

nitrogen deprivation, microalgae 

cells synthesize and accumulate 

storage lipids, up to 60% of total 

cell weight. The majority of 

these lipids are triglycerides and 

hydrocarbons, the most 

desirable compounds for the 

production of high-value fuels. 

v 

lipids are promising 

25% Lipid 50% Lipid 

40% Glycolipid -+ 5% Glycolipid 

5% Triglyceride --------, 40% Triglyceride 

Storage Lipid Accumulation via Nitrogen Deprivation Stress 
Microalgal Lipids 

- Feedstock 

Crude Oil Hydrocarbons 

I Trigl ycerides I 

Microalgal 

Feedstock typical 

of' stressed cells 

Product Distribution N 

0 

I Gasoline I 

Atmospheric 

Distillation 

and Subsequent 

B Methane 

Gasoline 

- Average Product 

Distillate Yield from 

Other 

Jet Fuel U.S. Refineries 

Residual 

Other 

\ I=- Ester Fuel 

I Other (nonfuel) 

Feedstock Composition and Product Distribution: Petroleum and Lipid Oils 

A 

Microalgal lipid oils have been The chemical composition of 

compared to crude petroleum lipids is species specific and 

but differ in their basic varies by the technique used to 

composition, required stimulate accumulation of 

processing and final product storage lipids. Thus, 

yields. Proposed processing processing yields also can be 

schemes yield gasoline and expected to vary. Better 

distillate fractions similar to definition of final product 

average yields from petroleum. yields from microalgal lipid 

However, microalgae catalytic components is needed to 

conversions are quite different define the desirable microalgae 

from those used in oil refining, species characteristics and to 

Transesterification of algal oils optimize processing for the 

could yield up to 75% ester desired (highest value) fuel 

fuel. product distribution. 



Table 2-5 shows the composition, specific energy, and gross energy content of a representative sample of algal biomass. The gross energy content is simply the product of the 

weight fraction and specific energy for each component. Lipids, while constituting only 

30% of the total weight of the representative sample, contain over 46% of gross total 

energy content. Carbohydrates constitute 20% of the total weight but contain only 14% 

of the total energy. The calculated total gross energy content of the algae, 24.9 GJ/t, is 

equivalent to a heat of combustion of 10,730 Btu/lb. This compares favorably with coal 

whose heat of combustion varies from 7,000 Btu/lb for lignitic types to 14,000 Btu/lb for 

bituminous types (Perry and Green 1984). Additional characteristic types of the microalgal feedstocks are discussed below by specific conversion processes and final fuel products. 

2.4.4 Fuel Products from Lipids 

To examine fuel products from microalgae, we first must determine if crude algae feedstocks can be utilized directly in refineries, either alone or blended with crude petroleum. In the absence of detailed research to the contrary, the answer at the present 

time has to be no, primarily because of the presence of large amounts of oxygen. Algal 

biomass would be a poor blendstock because the oxygenates could react with the unsaturated hydrocarbons at the high temperatures used in crude distillation, causing polymerization or other undesirable reactions. Crude petroleum contains essentially no oxygen. 

The carbohydrate fraction of algae contains about 50% oxygen and the lipid fraction 

about 10%; thus, a feedstock with the chemical composition shown in Table 2-5 would 

contain about 13% oxygen. Now we will look at the utilization of the individual components of the algae and emphasize in this study the use of the highest energy fraction--the 

lipids. We will also look at alternative processes that utilize the carbohydrate fraction 

and the whole algae product. 

Pseudo Vegetable Oil (PVO) 

In recent years a significant amount of research and development has been directed 

toward the use of diesel oils refined from seed-bearing crops. These refined oils consist 

of the glycerol esters (triglycerides) of a variety of fatty acids. Table 2-6 compares 

physical and chemical properties of triglycerides with those of diesel fuel. The fuel 

Table 2-5. Composition and Energy Content of a Representative 

Sample of Crude Algae Feedstock 

Weight Specific Gross Energy Percentage 

Component Fraction Energy Content of Total 

(G J/t) (GJ/t) Energy 

Lipid 0.300 38.930 11.679 46 360 

Carbohydrate 0 .ZOO 17.580 3.516. 14 .I07 

Protein 0.320 23.860 7.635 30.635 

Intermediates 0 .lo0 20.930 2.093 8.398 

Ash 0.080 0 0 0 

Total 1.000 24.923 100 .OOO 
Table 2-6. Suggested Standards for Vegetable Oil and 

Ester Fuels versus Diesel Oil Specifications 

Property 

Suggested Standards ASTM Specifications for 

No. 2 Diesel Oil (D975) Vegetable Oil Ester Fuels 

Cetane number (minimum) 

Cloud point (maximum) 

Distillation temperatures, 90% point 

minimum 

maximum 

Flash point {minimum) 

Pour point (maximum) 

Specific graviy, 15/ 15'~ 

Viscosity, mm /s at 38'~ 

Compositional 

Ash (maximum) 

Free fatty acid (maximum) 

Insolubles (maximum) 

Iodine value 

Moisture (Karl Fischer) (maximum) 

Phosphorus (maximum) 

Sulfur (maximum) 

Volatile matter (maximum) 

Water and sediment (maximum) 

Wax (maximum) 

40 

6'~ above 1 / 10 percen- - ' 

tile minimum ambient 

temperaturea 

a For example, the 10th percentile minimum temperature for Northern Illinois is 16'~ for November 

and 12'~ for March, bracketing the winter period when there would be minimum use of tractors on 

farms. 

Source: Pryde 1982. 

value of refined vegetable oil is typically 5% lower and the boiling range is higher overall 

than diesel; the cetane number (subject to the uncertainties discussed earlier) is 35-40. 

The greatest difference between diesel fuel and vegetable oils occurs in viscosity, especially at low temperatures. Fuel system modifications (e.g., a fuel preheater or precombustion chamber) are required for reliable performance from vegetable oils. 

The questions that need to be answered concern the similarities and differences between 

seed-oil lipids and algal lipids (so-called pseudo vegetable oil or PVO). First, algal lipids 

are much more heterogeneous than the seed-oil lipids. Figure 2-5 shows the major components and typical amounts of five lipid fractions as recovered by a sequential solvent 

extraction technique. The chloroform fraction of the algal lipids is roughly equivalent to 

the seed oil, containing mostly triglycerides plus free fatty acid. The individual fatty 

acid components are quite different from those in the seed-oil lipids (Tornabene et al. 

1983; Hill and Feinberg 1984), and the variation in chain length and degree of unsaturation are much greater for algal lipids. These factors make it difficult to character ize confidently algae-derived fuel products without more detailed investigations. At this time it is possible only to treat algal lipids as analogous to 
Solvent 

Hexane 

Benzene 

Chloroform 

Acetone 

Methanol 

Major components 

of extract 

Paraffins 

Olefins 

Aromatics 

lsoprenoids 

Mono-, di-, triglycerides 

Fatty acids 

Glycolipids 

Phospholipids 

Range of 5 

Typical content a 

Figure 2-5. Major Lipid Fractions as Recovered via Sequential Solvent Extraction 

(Source: Tornabene et al. 1983) 

vegetable oil and evaluate existing vegetable oil processes to determine the costs and 

yields of PVO from lipids. 

Ester Fuel 

Another option for using algal lipids offers some improvements over PVO in critical 

areas. This option is the transesterification of the triglycerides: the interchange of the 

glycerol (the component exploited in PVO fuels) for methanol or ethanol, thereby producing methyl or ethyl esters. This process has also been utilized with seed oils, so the 

adaptation to algal lipids is straightforward. Important properties of the resulting socalled ester fuel are discussed by Hill and Feinberg (1984) and are listed in Table 2-6. 

Although the fuel value is about 10% lower than diesel fuel (5% lower than vegetable 

oils), ester fuel has significant advantages over seed oil in both viscosity and cetane 

number. The distillation curve (boiling range) is between those of diesel and vegetable 

oil. The only disadvantage of the ester fuel is that the cloud point is estimated to be 

between 0' and 10'~. Use of ester fuel at or near these temperatures would require 

some fuel or fuel line preheating, although a diesel-ester blend (as well as a diesel and 

seed-oil blend) would minimize this requirement. 

The M-Gas Process and Its Potential 

The high energy content of hydrocarbons, plus their greater familiarity to petroleum 

refiners, has led to a recent interest in the conversion of oxygenates to hydrocarbons. In 

particular, methanol has been evaluated extensively as a candidate because it can be 

produced from coal. Mobil Research and Development Corporation has done extensive 
work in developing thermal-catalytic processes that convert methanol to light hydrocarbons in the gasoline range (Voltz et al. 1976). Mobil researchers have also investigated similar processes using other feedstocks such as corn oil, castor oil, jojoba oil, and 

latex (Weisz et al. 1979), which all resemble microalgal lipids. 

A typical product distribution from corn oil is shown in Figure 2-6. The distinguishing 

products are paraffins, olef ins, and aromatics, with the rest identified as nonarornatics. 

Of particular interest are the aromatics with carbon numbers 6 through 8; these compounds, benzene, toluene, and the xylenes (BTX), represent octane-enhancing refinery 

streams currently used in both leaded and unleaded gasolines. The other significant products are in the liquefied petroleum gas (LPG) range and include mostly paraffins and 

some olefins. Both castor oil, which contains hydroxy-substituted fatty acid chains, and 

latex, a polymer of isoprene (2-methyl, 1,3-butadiene), produced an even larger BTX fraction. The similarity between these feedstocks and the algal lipids provides encouragement, although the algal lipids contain more polyunsaturated acids than the corn oil, and 

their isoprenoids may be ring compounds rather than straight chained as in latex. These 

differences affect reactivity, yields, and product distribution. Nonetheless, algal lipids 

appear to be good candidates for catalytic conversion to high-value liquid fuels. 

More research with Mobil's or a similar catalyst would be required to verify the potential 

of algal lipids as feedstocks. Drying, extraction of the reactive fraction (triglycerides, 

etc.), and other preprocessing might be required to prepare the harvested algae for such 

a process. 

Fuel 

20- gas 

LPG 

range 

2 8% 

I Gasoline range 

5 8% 

Carbon number 

Light 

distillate 

range 

9 O/o 

Cu 

Nonaromatics $ 

(total 1 3.Ei0/0) " lt2zza 

Aromatics 

Olefins 

(total 6.0%) 

Essi 

Paraffins 

(total 

0 

30.7%) 

Figure 2-6. Product Distrib~tion from Catalytic Conversion of Triglycerides 

(Corn oil at 450 C) (Source: Weisz et al. 1979) 

44 
2.4.5 Other Products and Processes 

In addition to lipids, algae contain carbohydrates and protein that can be used in more 

developed (relative to lipid-based) processes to produce liquid or gaseous fuels. Two 

relatively well developed technologies are chosen as alternative processes for producing 

fuels from rnicroalgae. 

Methane 

Anaerobic digestion is the one known process by which most of the algal cell mass is converted into a single fuel product. Methane, the primary constituent of natural gas, is 

widely used as both a fuel and as a chemical feedstock. Since under normal conditions it 

is a gas and therefore bulky to handle, its use as a transportation fuel has been limited. 

However, natural gas boilers and combustion turbines are in wide use. Liquefied natural 

gas (LNG), with increased density? has begun to be used as a transportation fuel. 

An anaerobic digester contains synergistic microbial populations to convert a variety of 

organic substrates to methane and carbon dioxide. Thus algal organic compounds (lipid, 

protein, or carbohydrate) can be converted to methane. 

Few problems are expected in the adaptation of the microalgae feedstock to anaerobic 

digestion. Microalgal feedstock digestion is already being studied (Wagener 1981; Samson 

and Leduy 1983), and macroalgae are also promising feedstocks for methane (GRI 1983). 

The major technical concern is the continuing development of more efficient digesters. 

The analysis presented here was based on a plug-flow reactor design, which has been 

studied widely. Other digester designs, in particular those employing fluidized beds, 

might offer better conversion efficiencies and shorter residence times. 

The primary digester product, usually referred to as biogas, is not pure methane, but contains large amounts (typically 40%) of carbon dioxide, plus trace amounts of water and 

hydrogen sulfide. Water, sulfur, and nitrogen must be removed prior to use, but removal 

of the C02 is optional. If the gas is to be burned on site, C02 removal is not required 

but the gas will have a lower Btu content per cubic meter, so a larger volume of gas is 

required compared to natural gas. Direct use of this medium-Btu gas, as it is commonly 

called, is still probably cheaper than C02 removal in small applications. However, when 

large volumes of gas are sold to a pipelme company or gas utility, COZ removal will be 

required. This can be accomplished via several gas absorption processes; water, sodium 

.carbonate, and ethanolamines are the most common absorbents. Removal of the carbon 

dioxide from microalgae-based biogas would create the potential for C02 recycle into 

the culture system, while producing a high-Mu gas suitable for either pipelines or further 

chemical processing. 

Ethanol 

Ethanol is the highest-volume product, fuel or otherwise, produced commercially by biological fermentation. In the beverage and fuel industries, the primary substrate is starch 

from corn or other grains, although any carbohydrate source is suitable for conversion to 

sugars and subsequently to ethanol. 

Algal protein could be separated from the fermentable sugars and recovered as a salable 

food or feed product similar to soybean meal, although species-specific research is 

required to characterize the protein product. Also, the polysaccharides in microalgae 

are in the form of starch, so the sugars are accessible for yeast fermentation. The major 
issues concerning ethanol production from microalgae are the maximum carbohydrate 

content that can be achieved and the portion of the carbohydrate that could be converted 

to fermentable sugars and then to ethanol. 

Therrnochemical Conversions 

It is likely that fuel products other than those discussed here may be produced from 

microalgae. Most of the microalgal research to date has concentrated on their production and growth, and maximizing production of one fraction (Lee, lipids). Little at tention 

has been paid to other potential products. The opportunities are great for improvements 

and even breakthroughs. For example, the entire algal mass, with a suitable catalyst, 

could be hydrogenated to a mixture of paraffins and olefins in the 16-22 carbon range, 

and either blended directly into crude oil or used as a cracking stock. This reaction has 

been investigated using a strain of Chlorella pyrenoidosa (Chin and Engel 1981). Other 

conversions are also possible, most notably a therrnochernical conversion to produce 

higher alcohols, which have octane-enhancing properties of ethanol and methanol but 

pose fewer water solubility and phase separation problems. At this point there are insuff icient data to analyze these processes further. 

2.4.6 Summary 

Table 2-7 summarizes estimated yields of five fuel products from microalgae with the 

composition shown in Table 2-5. These comparisons provide an overview of the various 

fuel options. A complete evaluation of the feasibility of producing these fuels from 

microalgae requires an estimation of feedstock product ion costs, which is presented in 

the next section, and conversion costs, which is presented in Section 4.0. 
Table 2-7. Summary of Reference Processing Product yieldsa 

- 

PVO Ester 

Fuel Gasoline Methane E t hmo 1 

Major algal component 

Fraction of total algae 

Fraction available for conversion 

$ Efficiency of utilization of major component 

Major production (t/t algae) 

Common unit/t algae 

Product energy content (GJlt) 

Main energy products (GJ/t algae) 

Total energy available (GJ/t algae) 

Recovery efficiency (major product) 

- - 

Lipid 

0.30 

0.40 

1 .oo 

0.1 20 

34.4 gal 

39.5 

4.74 

24.92 

19.0% 

Lipid 

0.30 

0.40 

0.98 

0.112 

33.5 gal 

36.90 

4.14 

24.92 

16.6% 

Lipid 

0.30 

O.6Ob 

0.90 

0.085 

30.7 gal 

43.86 

3.74 

24.92 

15.0% 

All 

- - 

0.90 

0,80 --.. 

14.00 GJ -- 

14.00 

24.92 

56.2% 

Carbohydrate 

0.20 

0.057 

18.99 gal 

26.7 

1.52 

24.9 2 

6,1% 

a~lgal feedstock composition as shown in Table 2-5. 

b~3 basis hydrolysis efficiency x 0.95 fermentation efficiency. 

'0.90 hydrolysis efficiency x 0.95 fermentation efficiency. 
3.0 MASS CULTURE ECONOMICS 

An important part of a technical and economic evaluation of mass culturing microalgae 

is estimating the costs associated with the production process. This section discusses the 

development of these cost estimates. Following a review of major system components 

and design issues we examine available physical and economic data for individual components. Finally, we develop a reference microalgae production system and a cost estimate. Cost estimates were facilitated by the use of the Algal Production and Economic 

Model developed by Hill (19841, which allows easy variation of key components to identify cost sensitivities, the starting point for establishing research targets. 

3. 1 Process Design Economics 

3.1.1 Pond Configurations and Cost Factors 

The Algal Production and Economic Model (APEM) has as its basic production unit a pond 

configuration similar to that described by Benemann et al. (1982). The facility is composed of individual modules or ponds arranged to facilitate the use of centralized distribution systems for nutrient and water additions and for harvesting. Between 15% and 

20% of the total land area is dedicated to on-site nonproductive uses such as support 

facilities. Cost estimates are developed for the major subsystems of the facility and for 

individual modules using construction cost estimates reported by Benernann et al. (1982) 

and additional cost estimates from a variety of sources. 

Civil construction costs, exclusive of lining for the growth ponds are estimated to be 

$8450/ha (1984 $). These costs include site preparation and surveying, laser grading, and 

primary berm construction. Channel dividers in the ponds, modified from Benemann's 

(1982) secondary berm design, are thin plastic fences, which are less expensive than 

secondary berms and take up less surface area. In addition, each module is lined with a 

granular cover over a clay bed at a cost of $5000/ha. At many sites, soil characteristics 

are conducive to compacted earth, granular-bed channels. However, with extensive 

adoption of the technology, developers may encounter soils of high permeability, and 

more expensive plastic or polymer liners that could cost two to three times as much to 

install may be required to prevent excessive water loss. 

The circulation system for each module employs conventional paddlewheels at an est imated cost of $2500/ha. Alternative mixing systems differ in their hydraulic mode and 

pump efficiency. For example, the mixing system efficiency is 5996 for a paddlewheel 

but only 27% for an air lift pump. Either system may be installed at the $2500/ha cost 

estimate, and the choice of mixing systems depends on the desired properties and module 

design. While more expensive to operate, the air-lift system can also distribute C02 into 

the culture medium. The reference system for this study assumes paddlewheel mixing. 

Capital costs for the carbon dioxide sparger system and the waterlnutrient distribution 

system within the facility are based on the estimates presented by Benemann et al. 

(1982) designed for the distribution of dilute carbon dioxide flue gas but adjusted for the 

use of pure COZ. Exclusive of the capital costs for the large concrete supply pipe, 

Benemann's cost estimates include $4.6 million of fixed costs for each 2000-acre facility 

and $46,000 in incremental costs for each module. The waterjnutrient distribution 

system requires $420,000 of fixed costs and incremental module costs of $21,000. 
The prices of major nutrients are assumed to be contracted prices. These nutrients 

include ammonia gas, superphosphate, and potassium muriate. The quantity required for 

each nutrient is based on total consumption determined by algal productivity requirements, outgassing losses, and blowdown losses. Nutrients that are not incorporated into 

algal cell mass but remain in the media may be recycled to the extent that the culture 

salinity tolerance is not exceeded; the amount recycled is an input parameter determined 

exogenously. 

3.1.2 Harvesting Opt ions 

Harvesting cost estimates are based on three harvester subsystems commonly employed 

in algal dewatering schemes: a microstrainer, a centrifuge, and a belt filter. The microstrainer and belt filter systems are used as the first-stage systems; the centrifuge used 

as a second stage brings the product stream to the design requirement of at least 10% 

solids. 

The concentration factors for the harvester systems are 10, 70, and 150 for the microstrainer, belt filter, and centrifuge respectiv ly (Mohn 1980; Dodd 1984), and energy con- 9 sumption rates are 0.2, 0.88, and 1.2 kWh/m of throughput, respectively. Capital cost 

estimates represent the Iargest commercial system available for each harvester. The 

effluent from the ponds is divided into individual streams that are capable of being 

hand d by the harvesters. The microstrainer and belt filter are ypable of handling 

13 m 9 /minute, whereas the centrifuge has a design limitation of 3.8 m /minute. 

3.1.3 Production Labor 

Production labor was estimated at $1345/ha of total facility size (Benemann et al. 1982). 

For a 1000-ha facility, the labor estimate assumes annual salary requirements for 1 plant 

engineer, 4 shift supervisors, 20 pond operators, 8 secondary harvesting operators, 8 processing operators, and 2 laboratory personnel. Additional overhead expenses of 75% of 

direct labor are also included in final production labor costs for the facility. These small 

labor requirements are consistent with a highly automated facility, the basis for all 

design examined in this report. 

3.1.4 Energy Requirements 

Energy consumption for the mass culture facility is divided among harvesting, pumping, 

and mixing systems. Energy consumption for mixing and pumping is calculated from 

total head loss. Energy consumption for harvesting is based on throughput to the harvesters using the energy consumption estimates of Mohn (1980). Total energy used for 

mixing was calculated from Manning's equation for head loss in channel flow systems. 

Energy used in pumping the medium from the modules to the harvester subsystems, 

recycled water from harvesters to the headbox, makeup water, and disposal water are 

estimated from assumed head losses for a nearly flat site. Each major pumping routine is 

summed for the entire facility based on flow rates appropriate for maintaining mass 

balances among all processes. Based on horsepower requirements for each pumping 

routine, energy consumption is determined by the number of days the facility operates. 

Energy costs are based on average energy costs to large industrial users in the Southwest. 
3.1.5 Resource Supplies 

Water 

Microalgae culture in this analysis is based on utilization of low-value saline waters that 

are presumed to be abundant in the southwestern United States. The high salinity precludes their use for potable or irrigation water, and enhanced oil recovery (EOR) projects 

represent the only demand for these resources--carbon dioxide or other inert gases and 

saline water are injected into low-pressure wells to force entrapped oil to the surface. 

Sufficient saline water for this purpose is found in conjunction with the oil or gas, so 

EOR will not compete for water from saline reservoirs. 

Data collected on saline water resources typically reflect only the interests of the oil 

and natural gas industry and are insufficient for ascertaining the quantity and quality 

available for microalgae recovery. Well logs record the occurrence and cheni ical composition of saline water found near oil and gas reserves. However, well log data do not 

include estimates of how much of the saline water resource can be pumped to the surface, since historically this water is of little value. 

To estimate the available quantity of saline water for use above ground, additional data 

on area yield is required. Area yield estimates characterize the reservoir in terms of 

pumping capacity and the impact on adjoining (perhaps potable) water reservoirs and give 

estimates of sustainable pumping limits and the total available water resource. Without 

detailed pumping data it is not possible to estimate area yields, and the quantity of saline 

water available for use in microalgae culture remains uncertain. 

3 The water costs are based on a delivered cost ($/m ) to supply a microalgae facility with 

sufficient water as makeup to control culture salinity. Because of the uncertainty in the 

design and scope of a water supply project that would be capable of supplying the necessary water to a. mass culture facility, a range of water costs was estimated based on 

analyses conducted by Maxwell et al. ( 1984). Area-specif ic estimates of drilling costs 

and delivered force await more extensive evaluation of the saline water resources of the 

Southwest. 

Carbon Dioxide 

Carbon supply costs are estimated initially on the basis of C02 obtained from a power 

plant. This offsite facility is assumed to use a potassium carbonate extraction system 

with purification and compression; the C02 would be transported via pipeline to the mass 

culture facility. This supply system is costed based on a functional relationship between 

daily supply requirements, transport distance, and delivered average unit cost for the 

carbon dioxide. 

Once the mass culture facility's daily requirement for C02 is determined, the delivered 

cost of C02 is determined by calculating the 30-year amortized cost of the potassium 

carbonate extraction system and the amortized cost of a pipeline to transpor? the CO to 

the culture facility. The cost functions for these parameters are based on Jata 

developed for determining the delivered cost of C02 to enhanced oil recovery fields 

(Anada et al. 1983). The initial design serves as a basis for completing a full production 

configuration but is subject to uncertainty, so we examine later alternative C02 delivery 

schemes and the range of costs they imply. 
3.2 Analytical Method for Evaluation 

3.2.1 Model Description 

The Algal Production and Economic Model (APEM; Hill 1984) consists of a series of 

linked physical and economic relationships representing the major aspects of microalgae 

cultivation, product ion costs, and economics. While a comprehensive treatment of all 

factors affecting cultivation and cost must await resolution of many issues, the model 

was designed and has evolved to provide sufficient detail in critical areas of microalgae 

production technologies. 

A schematic of the APEM is presented in Figure 3-1 (see Appendix A for the entire 

model). The main computer model contains the algal production system, which specifies 

the appropriate data for all the other models. Because the operations and production 

levels within the culture system determine the levels of resource input requirements and 

ultimately establish production output levels from the facility, knowledge of the basic 

biology of the system is most important. 

The production model reflects a steady-state, continuous production calibrated to 

empirical data from field experiments. Cross productivity is determined by specifying 

culture depth, density, and detention time (or the inverse of dilution rate). Then, based 

on the assumed chemical composition of the microalgae and the size of facility, data on 

flow rates, nutrient requirements, and product yields are calculated according to deterministic, functional relationships. 

Resource inputs are determined by productivity levels. Requirements for carbon, nitrogen, potassium, and phosphorus are based on inputs required to maintain growth in the 

culture system. Gross nutrient demand includes the deficits resulting from blowdown for 

culture salinity control and culture outgassing losses; this demand can be partially offset 

by nutrients present in the source water. The amount of water required is a function of 

water losses due to culture surface evaporation, blowdown, and harvesting. 

The culture density and rate of harvest from the system determine the harvester subsystem requirements. Nominally, a product requirement of 10% solids is used to determine the extent of harvester concentration required. Harvester flow rates determine the 

number of f irst-stage harvester systems--microstrainers or belt f ilters--required. The 

product stream from these harvesters is concentrated to the design value (10% solids) by 

centrifuges. 

3.2.2 Overview of Reference Values and Ranges for Model Parameters 

The Reference Case 

In order to explore the critical issues of mass cultivation of microalgae for fuel production, a reference (base) case was established. This reference case, defined as a specific 

set of input parameters to the model (Table 3-l), reflects a consensus by SERI 

researchers and other experts on values that might be expected if mass cultivation of 

microalgae were undertaken today in the southwestern United States. A consensus was 

difficult to reach on several parameters because data are lacking. In such cases, one 

reference value was chosen from the range of possible values. The complete reference 

case represents neither a minimum nor a maximum of the potential for microalgae but 

rather a set of values from the range of possible results. The reference system, based on 
Water 

A. Net make-up 

B. supply k 

I system cost 

Nutrients 

For N, K, P 

- A. Quantity 

8. Cost 

C. System cost 

I Carbon I 

Algal production 

A. Yield (gross) 

6 . Efficiency on PAR 

C. Facility design 

D . Facility costs Harvester 

E . Flowlmass balance *- A. Capital cost - *- 

F . Power requirements B. Energy costs 

. G. Module hydraulics 

H . Energy requirements 

I. Salinity tolerance 

Media 

recycle 

Product slate 

A. Net biomass 

B. Lipid quantity 

C. Carbohydrate quantity 

D. Protein quantity 

Processing 

and storage I 

-( Nutrient recycle k-----------I I 

Economics 

A. Annual operating cost 

* B. Capital investment C. Life cycle revenue 

required 

D. Product pricing 

Figure 3-1. Overview of Algal Production and Economic Model (APEM) 

(Source: Hill 1984) 
Table 3- 1. Parameter Ranges Identified for Microalgae 

Systems (1984 $) 

Range 

Low Reference High 

Category A: Resource Parameters 

Evaporation (m/d) 

Salinity of source water (g TDS/L) 

3 Nitrogen in source water (g/m ) 

3 Phosphorus in source water (g/m ) 

3 Potassium in source water (g/m ) 

3 Carbon in source water (g/m ) 

Land cost ($/ha) 

Energy cost ($/kwh) 

Water 3 cost ($/m ) 

Ammonia cost ($it) 

Superphosphate cost ($It) 

Potassium cost ($It) 

Distance to C02 source (km) 

Co2 cost ($/m) 

Category B: Facility Design Parameters 

Effective culture area (% of total size) 

Effective culture downtime (% of total surface) 

Module size (ha) 

Channel width (m) 

~hanne 1 roughness (Manning's "n") 

Depth of culture (m) 

3 Carbon in medium (g/m ) 

3 Nitrogen in medium (g/m ) 

3 Phosphorus in medium (g/m ) 

3 Potassium in medium (g/m ) 

Carbon losses (g/rn3/d) 

3 Nitrogen losses (g/m /d) 

Mixing velocity (m/s) 
Table 3-1. Parameter Ranges Identified for Microalgae 

Systems ( 1 984 $) (concluded) 

Range 

Low Reference High 

Mixing system efficiency (%) 

Harvester solids removal (%) 

Harvester type 

First stage 

Second stage 

Category C: Biology parametersa 

Ash content (% dry wt) 

Lipid content (% dry wt) 

Carbohydrate content (% dry wt) 

Protein content (% dry wt) 

Intermediate content (% dry wt) 

Salinity tolerance (g TDS/L) 

Phosphorus cell content (g/g dry wt) 

Growing season (d) 

Photosynthetic efficiency on PAR (%) 

Depth (m) 

Detention time (d) 

Density (mg/L) 

Category D: Financial Parameters 

Return on debt (%) 

Return on common stock (%) 

Return on preferred stock (%) 

Capital cost escalation (%/yr) 

Operating cost escalation (%/yr) 

Maintenance cost escalation (%/yr) 

Cover cost ($/rnZ) 

Liner cost ($/ha) 

Microstrainer Belt filter 

Centrifuge Centrifuge 

a~he chemical composition sensitivity analysis examined three cases where lipid content, 

carbohydrate content, and protein content were varied as a consistent group or set. 

Parameter ranges reflect a high lipid producer (high case) or a high carbohydrate producer (low case). 
current knowledge of mass culture facilities, is a point of departure for further evaluation of cost improvements that could be achieved through continued research and technical progress. 

Specific Data 

Each parameter used in the algal production and economic model was assigned a reference value within a range of values based on information in the literature and the judgment of experts who reviewed concepts and offered direction in setting ranges. 

In the sections below the input parameters used in the algal production and economic 

model are divided into four groups representing resource, biology, facility design, and 

financial information. The determination of the base value and range for each parameter 

is described for each general category. Table 3-1 summarizes the range of values for all 

parameters and lists the reference value for each. 

3.2.3 Resource Parameters 

Evaoorat ion 

Evaporation from a culture pond's surface is the major source of water loss from outdoor 

microalgal cultures. Gross evaporation is typically reported as "pan A" evaporation rate, 

while net evaporation rates include precipitation that offsets gross evaporation. 

Figure 3-2 illustrates average annual net "pan A" evaporation rates for the United States. 

The pan A net evaporation data in Figure 3-2 must be modified to estimate evaporation 

rates for open reservoirs. Benemann (1982) reports that reservoir net evaporation rates 

are 50%-70% of pan A net evaporation rates, although the net evaporation in open culture microalgae systems would be higher than for open reservoirs because the algal ponds 

are shallower, absorb more light, and are stirred. 

Vigon et al. (1982) reported that gross reservoir evaporation rates in the Southwest range 

from less than 1.00 to more than 2.00 m/yr and selected an average precipitation rate of 

0.2 m/yr for their analysis of microalgae production, resulting in net evaporation rates in 

the Southwest between 0.82 and 1.82 m/yr. These net reservoi~~a~oration rates are 

consistent with the 0.75- to 2.0-m/yr range indicated in Figure 3-2 for the desert Southwest. 

For this analysis, we initially selected this range of 0.82-1.82 m/yr (0.0022-0.005 m/d) to 

determine net evaporation in microalgae culture systems located in the Southwest. 

However, based on information from consultants, a value of 0.01 m/d for the high end of 

the range was finally selected; this value reflects peak evaporation rates during part of 

the year. The range of 0.002-0.01 m/d chosen for this analysis is comparable to "pan A" 

data for the Southwest. Also, the value of 0.01 m/d presents a very conservative 

(resource-intense) view of net evaporation in mass culture facilities. The microalgae 

reference system assumes a net evaporation rate of 0.0035 m/d (1.28 mjyr). 

Salinity of Source Water 

The salinities investigated cover the range found in saline groundwaters of the southwestern United States (Feth et al. 1965). The salinity levels that distinguish potable 

from nonpotable water are not clearly defined. While U.S. Public Health Service standards specify not more than 0.5 g TDS/L in potable waters, waters with about 

2 g TDS/L are used for domestic water supply in some regions. In addition, crops are 

being developed that will grow at salinities significantly higher than previously accepted 

for plants. Thus, 2.5 g TDS/L was selected as the lower salinity limit for available 

groundwaters and 35 g TDS/L as the upper limit. Isolated wells have salinities greater 

than 25 g. In contrast, the salinity of seawater is relatively constant at about 

33 g TDS/L. The reference value 25 g TDS/L, imposes on the system the use of highly 

saiine water with little or no alternative uses. 

Enerm Cost 

Electrical costs at large industrial facilities are composed of demand charges and energy 

charges. Demand charges are established in the utility rate base as the measured peak 

instantaneous power demand (in kilowatts) for the facility. Energy charges represent the 

actual consumption charged to an industrial facility. 

The reference system assumes an electrical cost of $0.065/k~h. Within the Southwest, 

comparable electrical energy costs (e.g., demand char es plus energy charges) from 

investor-owned utilities range from $0.04 to $0.053/kWh 7 Edison Electric Institute 1982). 

A more conservative high end of this range was selected based on information from the 

Energy Information Agency (1983), which indicates that a few utilities in the Southwest 

may charge industrial users up to $0.10/k~h. Therefore, depending on the utility company, the estimated cost of electrical energy for a microalgae facility ranges from 

$0.040 to $O.l~/kWh. The reference value of $0.065/k~h is about the midpoint of that 

range. 

Land Costs 

A common assumption associated with the microalgal fuel production concept is that 

low-value, arid land deemed unsuitable for other uses will be used for mass culture 

facilities. Benernann et al. (1982) estimated that land costs between $1 185 and $1480/ha 

can be anticipated in such areas. In comparison, irrigated cropland could cost over 

$7200/ha. For the reference system land costs were $1245/ha. And the low and high 

values for sensitivity analyses are $1000 and $l5OU/ha, respectively. 

Nutrient Costs 

The major nutrients required for mass cultivation of microalgae for fuel are carbon, 

nitrogen, phosphorus, and potassium. Each nutrient can be supplied in various watersoluble forms that can be directly utilized by the microalgae. 

Carbon cost estimates are based on the use of carbon dioxide obtained from flue gas. 

While certain levels of bicarbonate are available in the saline source water, additional 

carbon is required to support the high productivity of microalgae. This additional carbon 

from flue gas was costed based on information from the petroleum industry, which uses 

large quantities of flue gas carbon dioxide in enhanced oil recovery projects (Anada et al. 

1983). Because the price of carbon dioxide from a flue gas source depends on the quantities of carbon dioxide required and the distance transported, the microalgae production 

and economic model used in this analysis determines the quantity of carbon dioxide 

required to support the assumed productivity level and then, based on transport distance, 

determines the price of delivered carbon dioxide to the facility. 

The model C02 costs are calibrated to the cost of flue gas from a coal-fired power plant. 

The carbon dioxide must be scrubbed from the flue gas using a potassium carbonate 
recovery system, dried, and compressed for economical transport via pipeline. The pipeline size and required distance represent additional costs for the delivered carbon 

dioxide. 

The reference case assumes a pipeline distance of 80 km. Carbon dioxide can b trans- '5 ported as far as 300 km. Based o reference system productivity levels of 17 g/m /d and 

the availability of 200 g ca on m3 in the source water, the reference system microalgae 

facility feqyires 83 x 10 9 m/d. The delivered price for this carbon dioxide is 

$0.13/10 m . 

Nitrogen is supplied as ammonia gas at a cost of $185/t. Potassium is supplied as potassium muriate at a delivered price of $92/t, and phosphorus is supplied as superphosphate 

at a delivered price of $254/t. The amount of nutrients required is determined by productivity levels within the culture system; like carbon, these nutrients may be partially 

supplied by the source water. 

Nutrient prices are based on supplier quotes for spot purchases as listed in the Chemical 

Marketing Reporter (1984) and do not reflect contract prices that might be avaiiable to 

users of large quantities of industrial chemicals. Contract prices for the chemicals used 

in microalgae production may be lower than these spot prices; alternatively, actual 

delivered prices for these nutrients may be higher due to delivery charges to remote 

locations in the Southwest. 

The reference system values for delivered prices of these noncarbon nutrients reflect 

spot prices for ammonia gas, potassium muriate, and superphosphate. The range for each 

nutrient is about 10% above and below the spot price to account for the uncertainty in 

actual delivered prices. 

Nutrients in Source Water 

Reference system values for concentrations of nitrogen, carbon, and potassium in the 

source water are drawn from data on saline well water presented by Feth et al. (1965), 

and from the results of a multivariate statistical analysis of New Mexico groundwater 

(Barclay 1984a). Neither study includes data for phosphorus. 

Feth et al. (1965) present nitrogen (as nitrate) content data for wells in New Mexico. 

The m n nitrate content of all well waters with salinities greater than 4 g TDS/L was ep 13 g/m , which was taken as the reference value. Some well wa ers contained no detect- 5 able nitrate, whi4e the maximum reported by Feth was 96 g/m . We1 logs have shown 

that a few wells have nitrate concentrations in excess of 10,000 g/rn13, but these data 

appear to be anomalous, and sustainability of such a supply would have to be questioned 

(Barclay 1984b). If such concentrations were sustainable, these wells would e resent a 5 significant nutrient source. Unpolluted seawater can contain up to 0.6 g N/m when collected from deep ocean sources. 

3 The reference phosphorus or phosphate content of the source water (0.5 g/m ) was estimated by assuming a nitrogen:phosphorus ratio of approximately 30 for the reference 

case; a range of +loo% was arbitrarily established for phosp orus content. The phosphorus content of unpolluted seawater seldom exceeds 0.1 g/m t . 3 The reference value for potassium (46 g/m ) is the same as Barclay's Type I low-salinity 

water, which is an average associated with water that has a salinity of 8 g TDS/L, the 
reference value (Barclay 1984a). Data presented by Feth et al. (1965) for rizona and 

New Mexico showed potassium concentrations b tween 7.6 and 372 g/rnP for saline 

I li groundwaters. Seawater contains about 400 g K/m . 

Many groundwaters contain significant concentration~of bicarbonate ions, in sharp contrast to seawater, which contains only about 23 g/m of total dissolved carbon dioxide 

(dissolved CO plus bicarbonate plus carbonate). Barclay's Type I ow-salinity water containr about I d 0 rng HCO -, which is equivaleni to about 130 g/rni, the value chosen for 

the refere ce case (Barc 1 ay 1984a). Feth's data show bicarbonate concentratio s as low 

as 50 g/mr, while Barclay shows concentrations of bicarbonate up to 2100 g/rnFj. These 

values are equivalent to the minimum and maximum values of source water inorganic 3 carbon content chosen for the analysis (10 and 420 gJm ). 

3.2.4 Facility Design Parameters 

Effective Culture Area 

The effective c 

cultivation and 

terns, buildings 

liminary design 

to module size, 

:ulture area parameter apportions the total facility size to land area under 

land area used for "nonproductive" accoutrements such as harvesting sysand laboratories, access roads, pipe runs, and the headbox. Based on preanalysis, the effective culture area was found to be relatively insensitive 

ranging from 85.6% for 20-ha ponds to 86% for 60-ha ponds. 

The reference case assumes an effective culture area ratio of 86.0%. Because of uncertainty in this value until more extensive research is conducted, the sensitivity range for 

this parameter is from 70.0% to 90.0%. 

Downtime 

Additional cultivation surface area is lost due to downtime. This parameter reduces the 

total production surface area due to cleaning, repairing, draining, and filling ponds. The 

reference case assumes a downtime of 10%. The low value in this sensitivity range was 

arbitrarily set at 30% (most downtime) and the high value at 5% (least downtime). 

Module Size 

Module size is another parameter for which little information is available. The available 

data indicate that certain economies of scale may be achieved by increasing the size of a 

module or pond, although there is likely to be a loss of operational control with 

increasing size. Benemann et al. (1982) presented a cost analysis of microalgae 

cultivation based on a facility composed of twenty 40-ha ponds. 

The reference system assumes a similar size module or pond. Thus, the reference system 

is composed of forty-three 20-ha modules that have a total surface area of 860 ha (86% 

of nominal facility size), of which 774 ha are under productive cultivation at any given 

time (LO% reduction for downtime). The reference size of the individual modules (20 ha) 

represents a significant increase in scale over current technology. For this reason, 10 ha 

was selected as the lower value of module size for the sensitivity analysis, and 40 ha was 

selected as the upper value. 
Channel Width 

Within each module the reference design assumes that the total surface area is divided 

into individual channels. The maximum width of a channel is determined by the largest 

paddlewheel capable of operating in the channel. Benemann et al. (1982) reported that 

the largest conceivable paddlewheel is approximateiy 60 m; this value was selected as 

the maximum channel width. 

As before, the uncertainty in constructing and operating a paddlewheel mixing system of 

this size necessitated sensitivity ranges for channel width that were determined 

according to input from consultants. While a 60-m width was deemed possible with careful engineering, a channel width of 6 m was also examined to estimate the impact on 

costs of employing narrower channels. The reference system assumes a channel width of 

30 m. 

Channel Roughness 

Channel roughness is used to calculate head loss and thus to estimate power requirements 

to circulate the culture medium within a module. In shallow, open-channel systems, 

headloss is calculated from the Manning's equation, which requires a channel roughness 

parameter "n." Average values for n, obtained from Perry and Green (1984) indicate 

ranges from 0.023 for earthen channels that are straight and uniform to 0.03 for winding, 

sluggish channels with pools and eddies. Based on this parameter range, the reference 

roughness value is assumed to be 0.025, with variation in this value consistent with the 

above range. 

Mixing Velocitv 

The speed at which the culture medium is circulated within the module is a trade-off 

between keeping the algae in suspension and increased power requirements. Power 

requirements increase as the cube of the velocity; however, increased velocity induces 

more turbulence in the culture system, which will provide for better culture mixing and 

may lead to higher productivity. 

The reference system's nominal mixing velocity is 0.2 m/s based on similar mixing speeds 

used in experimental cultivation facilities. To determine the cost sensitivity of the 

mixing speed, the low value of mixing speed employed was 0.05 m/s and the high value 

0.3 m/s. 

Culture Depth 

Culture depth in SERI-sponsored experimental microalgae cultivating facilities has 

varied from 0.1 to 0.3 m. Experience has demonstrated trade-offs in culture depth; shallower systems support greater culture densities and result in lower harvest costs than 

deeper systems. But shallower systems require higher mixing speeds and thus higher 

energy costs. Deeper systems provide greater temperature stability. The reference culture depth is 0.15 m and a range of 0.1-0.3 m is considered. 

Nutrient Concentrations in Medium 

Concentrations of nutrients in the culture medium determine the quantity of nutrients 

lost with the water that enters the product stream and exits in the blowdown stream, 

The availability of nutrients to the algae is also determined by the concentration in the 
medium, but algae can obtain nutrients from very low medium concentrations. The 

major difficulty in maintaining low-standing nutrient concentrations lies in the development of a control system that can supply nutrients at the same rate at which they are 

required by the algae for growth. Historically, this problem has been solved by providing 

the aigae with high standing nutrient concentrations to eliminate the risk of nutrient 

shortage, and accepting the associated costs of nutrient loss. 

3 The reference value for carbon content (total C02) of the medium (12 g/m ) has been 

observed to support the growth 05 Phaeodactylum tricornutum (Caperon and Smith 

1978). The maximum value (25 g/m ) repre ents the approximate total C02 content of S seawater, while the minimum value (7.5 g/m ) represents a value at which the growth of 

P. tricornutum is moderately carbon-limited. 

The dependence of the phytoplankton growth rate on medium concentrations of various 

nitrogen sources has been extensively investigated (e.g., Caperon and Meyer 1972). 

Normally, algae can grow at concentrations of inorganic nitrogen as nitrate or ammonium that are nearly undetectable by present techniques--less than about 1 micromolar. 

Growth occurs near the maximum rate at concentrations of about 10 micromolar. 

Nevertheless, nutrient concentrations in outdoor cultures have frequently been maintained in the range of 100-1000 micromolar for the reasons discussed abov The 

reference case valuer far medium content of jtrogenour nutrients is 0.28 g/rnfi; while 

the mi 'mum value is 10 micromolar (0.14 g/m ) and the maximum is 1000 micromolar 

(14 g/m 9 ). 

Microalgal growth is sustained at concentrations of phosphate that are as low as or lower 

than those observed for nitrogen sources (e.g., Terry 1982). Thus, the molar values 

selected for the medium phosphate concentrat ions were identical to those selected for 

mediurr) nitrogen concentrations. These molar values for phosphate are 0.31, 0.62, and 

31 g/m . Few data are available on the growth response of microalgae to potassium concentrations. Input from the microalgae assessment consultant team indicated that at least 3 some species can grow rapidly at very low potassium concentrations, and 5 g/m was 

chosen as a minimum. McLachlan (1964) suggests that the response to potassium concentrations in the medium is species specific; some species are ypable of growth at these 

low conce trations. The reference value is set at 25 g K/m and the upper bound at i 

3 400 g K/m . !] 

\ 

Carbon and Nitrogen Losses 4 

If the carbon dioxide content of the nutrient medium exceeds the value expected in equilibrium with the atmosphere, significant losses of C02 can occur through the culture surface. Control of such losses will be a critical feature of the carbon dioxide system. With 

proper control, it is likely that these losses can be completely eliminated. The minimum 

value in the sensitivity analysis represents no losses, while the reference value represents 

an equilibrium exchange between the culture yrface and the atmosphere. The maximum 

value is an arbitrarily extreme rate (3.0 g/m /d). A similar logic was used to develop 

nitrogen losses from the culture surface. 

Mixing Svstem Ef f iciencv 

Recirculation within a culture system may be accomplished by a variety of mechanical 

devices. Because shear stresses within many pumping devices may break algal cell walls, 
the various options typically considered by aquaculture operators include paddlewheels 

and air lift pumps. A major distinction between these two mixing systems is the efficiency of the pump. Paddlewheel efficiencies are about 60% (Benemann et al. 1982), 

whereas air lift pump efficiencies are about 30%. Additional efficiency losses of up to 

24% are experienced in the drive system. 

The reference case assumes a mixing efficiency characteristic of a paddlewheel system 

(60%). To evaluate the cost sensitivity to mixing system selection, the sensitivity 

analysis examines trade-offs between an air lift pump mixing system (e.g., mixing pump 

efficiency of 30%) and an improved mixing system design having pump efficiency of 75%. 

Harvester Solids Removal Efficiency 

The reference case assumes a solids removal efficiency of 90%. This relatively high 

efficiency level either presumes chemical additions similar to those described above or 

assumes that microalgae species of sufficient size are cultivated to obtain easy harvestability using nonchemical, conventional harvesting technology. Because of these assumptions, the sensitivity of solids removal efficiency allows for removal efficiencies as low 

as 50% and as high as 98%. 

Harvester Subsvstem 

The selection of a harvester subsystem for a mass culture system depends on a number of 

variables such as cell size, capital and operating costs, throughput, and efficiency. Many 

of these issues are discussed in earlier sections of this report. 

The reference case was developed to incorporate a two-stage harvester system. The 

first stage is a microstrainer capable of handling large influent flow rates and achieving 

concentration ratios up to 10. The second stage is a centrifuge system used to bring the 

product stream from the first stage up to a design criterion of 10% solids, which is suitable for subsequent processing and conversion to a fuel product. As an alternative firststage harvester system, the sensitivity analysis examines the use of continuous belt filter 

harvester systems with a concentration ratio of 70, which requires a 50% higher capital 

investment. 

3.2.5 Biological Parameters 

Operating Salinity Tolerance 

The salinity parameter employed here represents the expected salinity of the system 

under operating conditions, although the system salinity will drop temporarily after 

major rainstorms. Some species of algae will grow only in fresh water, while other 

species will tolerate saturated sodium chloride brines. Theoretically, algal systems can 

be operated anywhere in this range. Very high salinities, however, will present significant problems in equipment maintenance because of the extremely corrosive nature of 

the water. The lower limit of system operating salinity is established by the salinity of 

the source water employed. In all cases the operating salinity will be higher than that of 

the source water due to evaporation; the minimum operating salinity of 10 g TDS/L was 

chosen to be slightly higher than the reference case source water salinity of 8 g TDS/L. 

The reference case salinity, 35 g TDS/L, is close to that of seawater and is a salinity at 

which a number of mass culture systems have operated. A maximum salinity of 

120 g TDS/L has been employed in systems for the culture of the halotolerant chlorophyte Dunaliella, and serves as the upper range value. 
Phosphorus Content in Cells 

The phosphorus content in microalgal cells varies widely. While the algal requirement 

for phosphorus is not highly variable between species, there is considerable variability in 

the capacity of cells to store phosphorus. In many cases, this capacity for phosphorus 

storage can be extremely large. Thus, microalgal systems that are operated at saturated 

medium concentrations of phosphate produce cells heavily loaded with phosphorus. The 

values selected for the cell phosphorus content represent the phosohorus content of 

Phaeodactylum tricornutum under extreme nutrient limitation (0.0012 g/g dry weight), at 

optimum phosphorus content for total biomass production (0.007 gig dry weight), and at 

phosphorus-saturated conditions (0.023 g/g dry weight). 

Productivity 

The productivity of the system is determined by three input parameters: depth, density, 

and detention time. These parameters are discussed in detail elsewhere in this report, 

but their selection was based on a range of values established for the potytial productivity of microalgal sy tems. Productivities range from 1 g dry wt/m /d minimum 

value) to 25 g dry wt/m 1 /d (reference value) to 60 g dry wt/m 9 /d (maximum value). The 

reference value is representative of the rates that have been achieved in outdoor systems 

of conventional design. Rates near the minimum have been observed under less favorable 

conditions. The maximum value is reprewntative of the rates achieved under carefully 

controlled conditions in outdoor raceway systems, where considerable energy is expended 

to enhance production. In subsequent analyses (Section 6.0), we treat these variables as 

design targets set to achieve system cost-effectiveness. 

Proximate Chemistry 

Proximate analysis of cellular biochemical composition calculates cell content of lipids, 

carbohydrates, proteins, and metabolic intermediates. The term "metabolic intermediate" refers to any component of the cell biomass not extracted in one of the other 

three classes. Since each component is presented as a fraction of the total cell biomass, 

the sum of these fractions must equal 100%. Thus, the model accepts values for the content of lipids, carbohydrates, and intermediates (including ash); the remainder of the cell 

biomass is allocated to protein. The model flags the results of any calculation that leads 

to unrealistically low cell protein content. Individual members of this set of parameters 

\ 

:; 

can not be varied independently since increases in the content of one biochemical frac- 1 

tion must be accompanied by decreases in the content of the other fractions. Thus, these 

parameters were linked in the sensitivity analysis, and the three sets of parameter values 

were established to represent low, moderate, and high lipid concentrations. The low lipid 

content was taken from data collected by Laws (1984) for Platymonas sp. grown in outdoor cultures under nutrient-saturated conditions. This species, which does not produce 

lipid as a storage product, contained 20% lipid, 49% carbohydrate, and an estimated 10% 

metabolic intermediates. The high lipid content case represents the composition of the 

lipid-producing species Phaeodactylum tricornutum , grown under nitrogen-lim ited conditions (Terry et al. 1985). This species produced about 60% lipid, 10% carbohydrate, and 

10% intermediates. The moderate lipid content (i.e., reference case) species are 

assumed to produce 30% lipid, 20% carbohydrate, and 10% metabolic intermediates, with 

an ash content of 8% and a nitrogen content of 32%. 
3.2.6 Financial Parameters 

Escalation Rate 

The analysis presented is this report uses constant 1984 dollars for reporting costs to 

avoid an additional assumption regarding future inflation rates. The reference escalation 

rates are based on zero real increases in the costs of goods and services. Because of the 

uncertainty in future projections of real escalation rates, the sensitivity analysis 

examined a 2% rate of escalation for all cost categories. 

Ca~ital Investment Leveraeine 

Most capital investment projects involve a combination of equity capital and borrowed 

money. Leveraging projects can provide a means for altering the rate of return for 

investors. If the after-tax cost of borrowed money is less than the discounted rate of 

return, it is economically desirable to borrow money rather than investing equity capital 

into a project. 

The reference case presumes a project financing scheme in which 30% of the investment 

is borrowed and the balance is financed through stock equity. Another consideration in 

estimating the costs of financing a mass culture facility is the required rate of return for 

the debt portion and equity portion. The reference returns are 3.7%, 6.5%, and 4.5% on 

debt, common, and preferred stock, respectively. These reference rates are increased by 

50% to represent a high cost of capital case but are held constant at the reference level 

in the low case sensitivities. 

Capital Cost 

While considerable attention was devoted to developing realistic cost estimates for each 

component of the mass culture facility, questions on the validity of the capital cost 

estimates will remain until a more detailed analysis can be completed. Short of conducting such analysis, the sensitivity cases to evaluate possible changes in capital 

investment requirements for the mass culture project were based on 225% changes in 

total capital investment from the reference case. This can be interpreted as representing a case where capital costs for the balance of the system are not precisely identified in the reference systems due to the rudimentary status of development of the 

technology. 

3.2.7 Summary 

Parameter value ranges and reference case values are summarized in Table 3-1 for the 

four groups of parameters. 

3.3 Reference Case Economics 

The economics of the reference system are developed by two models: the microalgae 

production model and a revenue requirement economic model. The microalgae production model develops f irst-year cost estimates for a facility and determines production 

levels based on user-defined inputs and requirements. These first-year costs and production levels are then transferred to the revenue requirement model, which determines the 

required selling price of the algal products to achieve a specified minimum return on 
equity investment. These prices are determined by annualizing the present value of all 

facility cost streams and dividing these annual costs by the production volume. All costs 

are reported in constant 1984 $. 

The total estimated present value capital investment for the microalgae reference system is $37 million, which represents the installed depreciable and nondepreciable costs 

for the entire 1000-ha facility (860 ha are aftually available for cultivation). The ynit 

cost for the facility is $43,283/ha or $4.33/m of cultivation surface area or $4.33/m . 

Total annual operating and maintenance costs are $8.2 million. These direct production 2 costs, at 5% PSE or 17 g/m /d, equal $393/t algae produced. 

By annualizing the capital investment over the facility's lifetime, the total annualized 

production cost is about $13 rnillion/yr. Total algal biomass production costs are $393/t 

or $0.39/kg based on a net algal biomass yield of 33,171 t/yr. At a 30% lipid content, the 

total annual lipid yield for the reference system facility is 71,012 bbl. 

3.3.1 Cost Contribution to Reference Case 

One method for evaluating the potential for reducing costs through continued research is 

an examination of the individual cost centers to determine which ones contribute significantly to the final product cost. These cost centers can be grouped in separate categories such as exogenous costs and endogenous costs to indicate that the cost is not or is, 

respectively, affected as a result of program R&D. Additional detail within each of 

these general categories can provide specific information regarding the relative importance of specific subcategories to final product cost. 

The general cost contribution to the microalgae reference case is illustrated in 

Figure 3-3. Operating costs represent about 68% of the total product cost. Capital 

costs, including both depreciable and nondepreciable capital investments, represent 23% 

of the total product cost and maintenance costs contribute almost 9% to the total annual 

cost of production. 

Contribution of Direct Costs 

Direct production costs, or the combined annual maintenance and operating costs, represent the largest cost category. These combined costs contribute 76.7% of the total production cost of microalgae lipids, providing a cost contribution of $302/t of algae produced. Because these direct costs are so significant, it is worthwhile to examine the 

subcategories within this general cost category to identify the major cost centers. 

Figure 3-4 iliustrates the subcategories for the direct cost of maintenance and operation. As a percentage of total present value, nutrient expenses are 33.7% and are the 

largest cost category within the direct expenses. These nutrient expenses include carbon, nitrogen, and phosphorus. Potassium expenses are zero in the reference system 

because the saline source water is assumq to contain sufficient quantities of potassium 

to support a productivity level of 17 g/m /d. Carbon dioxide expenses represent 78.6% 

of the total nutrient expenses, or about 26% of total cost. 

Labor and overhead are the second largest cost category, contributing about 24% to total 

production cost. Water, the third largest portion of cost (almost 16%), is a vital component of production and is subject to considerable uncertainty given the range of 

possible costs discussed above. The other major factor input, electricity, contributes a 

substantial 7% to costs and, like water, is an area of considerable uncertainty. Other 

operating costs, estimated as 7% (nominal) of other operating costs, provide for a general 

category of maintenance expenses such as liner repair. 

Contribution of Capital Costs 

The total capital costs for the reference facility are about $37 million and are the fixed 

costs incurred with design and construction of the mass culture facility. The fixed costs 

shown in Figure 3-3 represent 23.3% of the total product costs. Within this general cost 

category, depreciable equipment costs are 51% of the total present value for capital. 

Figure 3-5 presents a summary of the relative contribution of the major capital cost 

components, combining both depreciable and nondepreciable investment categories. The 

culture system was estimated to contribute 37.7% of the total capital investment. The 

costs in this category include module construction, internal distribution systems for 

nutrients and water, pond lining, mixing systems, buildings, and electrical service to the 

facility site. Site preparation and surveying account for 26.2% of capital costs and the 

harvester subsystems represent 13% of the total capital investment. Because of harvester design capacity limitations, the reference system requires 22 microstrainers that 3 have a design capacity of 13.3 m /min per machine and eight second-stage centrifuges to 

concentrate the microalgae to at least 10% solids. 

Nondepreciable Capital Investment 

Nondepreciable capital investment items include site preparation and surveys, land costs, 

engineering fees, and contingency allowances; these items are nonequiprnent expenditures that cannot be depreciated for tax purposes. Together, these cost categories represent 49% of the total present value of the capital investment. This relatively high percentage is due to the site preparation and survey costs, which are major cost items in 

pond construction. The distribution of these nondepreciable capital items is presented in 

Figure 3-6. 

Contingency allowances for new construction typically are between 10% and 15%, 

depending on the stage of development of the technology and whether prior construction 

experience has been obtained. The novel concept of mass culturing microalgae for fuel 

requires a higher contingency fee of 20% (nominal) of total capital investment plus 

engineering fees, which results in relatively high costs; however, as construction experience is gained with these systems, these costs can be expected to decrease (Huguenin 

1984). 

Engineering and architectural fees for new construction range between 4.9% and 16% of 

project cost. Nominal fees for this category were based on several points. While most of 

the construction expense is for earthmoving, the scale of the facility may present unique 

engineering problems. Thus, a 15% fee for mechanical equipment and an 8% fee for 

earthwork were selected as nominal values for the reference case. The contribution of 

these engineering fees to total nondepreciable capital investment is 15.5% 

(Huguenin 1984). 

Land costs represent 7.8% of the nondepreciable capital investment. This low percentage is expected because of the low value of the marginal land contemplated for mass 

culturing microalgae. 

s=q - [;@,I 

\=/ 

De~reciable Capital Investment 

Depreciable capital investment includes equipment items that may be depreciated over a 

period of time established by the IRS. Specific lifetimes differ for various pieces of 

equipment, and while the analysis does not account for different lifetimes, a maintenance operating cost has been included as a sinking fund for equipment replacement. 

Also, by extending the depreciable lifetime of all equipment to 15 years, product costs 

increase because faster depreciation schedules allow quicker write-offs, thus decreasing 

revenue requirements. 

Of the total depreciable investment, the largest contributing cost category was the firststage harvesting system. As illustrated in Figure 3-7, almost 26% of the total equipment 

investment is to purchase and install the microstrainers and centrifuges necessary for 

harvesting. Lining the pond bottoms contributes 26% to capital equipment costs. Other 

major equipment items include construction of the pond dike and piping system (I((%), 

the paddlewheel mixing system (1 3. I%), and the electrical system to service the facility 

(9.1 %)* 

3.3.2 Summary 

Table 3-2 presents a summary of the various cost contributions to reference case product 

costs for each general and specific cost cate ory. The final cost estimate for producing 

microalgae in the reference case is $393/t f 1984 $). For a research program with the 

goal of producing cost-competitive liquid fuels early in the next century, the present 

economics for mass cultivation are unfavorable; however, the production cost estimates 

in Table 3-2 reflect an opinion of what could be accomplished using the present capability of mass culture techniques to produce liquid fuels. These results presume one set 

of parameters derived by consensus; however, other plausible conditions could occur. 

Section 3.0 examines the cost sensitivity of these reference system parameters to variation within defined limits. First, we examine concepts for refining the raw algal product 

into useful fuels that compete with conventional, hydrocarbon-based products. 

/ 1 Harvesters \ 

Lining 

Culture 

system 

14% 

Buildings 7 /~lectric I Mixing \ / 

Carbon dioxide 

transfer system - 

4.2% 

Figure 3-7. Cost Contribution Detail for Depreciable Capital Investment 

69 
Table 3-2. Summary of Reference Production Facility Cost Contributions 

for Annual Direct Cost and Capital Cost (1984 $1 

Cost Category lo6 1984 $/yr $/la 

Ca~ital Costs 

Site preparation 

Culture system 

Harvester systems 

Engineering fees 

Contingency 

Land 

Total capital cost 

Operating Costs 

Labor and overhead 

Utility 

Nutrients 

Water 

Operations 

Maintenance 

Total operating cost 10.002 301 

Total feedstock cost 13.042 393 

a33, 17 1 t/yr microalgae production. 
4.0 ECONOMICS OF PRODUCING FUELS FROM MICROALGAE 

In the previous section we discussed the economics of mass microalgae culture as they 

relate to applications in the southwestern United States. We now turn to a discussion of 

the final component of the concept under review, the economics of producing fuels from 

microalgal feedstocks. The main components of microalgae--lipids, carbohydrates, and 

proteins--are amenable to conversion to fuel products through a variety of processes discussed in Section 2.4. We will develop conversion costs for three fuels that use the lipid 

fraction of the microalgae and two fuels that primarily use other components. 

Algal carbohydrate conversion has a commercial analog in ethanol production from carbohydrates (e.g., corn). The major processing issues are the quantity and compostion of 

the microalgae carbohydrate and the available technology for hydrolysis of carbohydrates 

to sugars and subsequent fermentation to ethanol. Likewise, conversion of carbohydrates 

and lipids to methane gas via anaerobic digestion is a commercial technology. We therefore examine processing economics for ethanol and methane to determine the viability of 

using existing commercial processes for fuel production from microalgal feedstocks. 

A unique aspect of the concept of fuels from microalgae is that many algae are known to 

accumulate cellular lipids, compounds that are similar enough to hydrocarbons to offer 

potential for producing highly refined fuels that could substitute directly for conventional transportation fuels. In Section 2.4 we reviewed processes that can utilize lipids 

to produce partial or complete substitutes for gasoline and diesel fuel. These lipid-based 

processes are neither highly developed nor optimized specifically for microalgal feedstocks. However, our objective to evaluate fuels from microaigae requires that we 

develop the best possible data on these processes to allow comparison with more conventional sources. Below, we estimate the costs of converting cellular lipids to pseudo vegetable oil (PVO) and ester fuel (both diesel substitutes) and to gasoline. 

This section presents process flows and costs estimated for the five fuel options. Capital 

and operating costs are estimated for processing plants designed to handle the algae 

feedstock produced by the reference culture facility (1000 ha) discussed in Section 3.0. 

As with mass algal culture, a reference case for each fuel processing option is developed 

along with estimates of production costs. These reference fuel costs give us a perspective for measuring R&D improvements to be developed in subsequent sections. 

4. l Estimating Algal Processing Costs 

For each of the five fuel conversion processes in the study, the best available process and 

economic data have been compiled according to the steps shown in Figure 4-1. The processes selected convert the microalgae feedstock to the desired fuel products with the 

least amount of modification, although commercial viability depends on successful development work. Available fuel processing data are characterized by different levels of 

precision. These levels are listed below in order from most to least precise, and available microalgal feedstock processing data are categorized by the available level of 

precision. 
Microalgae 'feedstock 

product Major product Capital costs, Iblock 

By-products Operating costs 

I +-I Anaerobic 

digestion Methane 

block Capital costs, By-products 

Operating costs 

I Sludge 

I By-product 1 

'-4 Other Capital costs, production k -- L block _] by-products Operating costs 

--- 

Fuel products Total capital costs 

Other products Total operating costs 

Figure 4-1. Overview of Microalgae Fuel Refining 

I. Established commercial processes with precise cost and performance data for: 

a. Microalgae feedstock (no processes) 

b. Similar feedstock (anaerobic digestion, ethanol fermentation) 

2. Detailed engineering estimates of pilot- or laboratory-scale processes with extensive data for: 

a. Microalgae feedstock (PVO extraction, transesterif ication) 

b. Similar feedstock (PVO, ester fuels) 

3. Component data from analogous processes and facilities synthesized to represent 

microalgae applications (catalytic conversion). 

Based on the available data, we constructed an estimate of capital costs (the initial fixed 

investment) and annual operating costs for the primary fuel process and subsequent byproduct recovery. The capital costs are expended as a lump sum at the beginning of the 

project; the construction period is taken as negligible compared to the facility's lifetime 

of 20 years. When only on-site data were available (not including support systems such as 

boilers and cooling water systems), the total fixed investment was adjusted by applying a 

contingency factor of 50% (a range of 30% to 100% is common). 

Capital costs were estimated for facilities of various sizes by application of the sixtenths factor (Perry and Green 1984), which is a good estimator of economies of scale for 

chemical and biochemical process facilities. Scale-up by a factor of 10 would increase 

the capital costs by a factor of 4. All processes are designed to handle the annual algal 

output (33,000 t) of the reference 1000-ha production facility. 
Operating costs consist of the following major components: raw materials, utilities 

(electric power, steam, and coolin water), maintenance, labor and supervision, taxes, 

depreciation (10 year, straight line 5 , and return on investment (IS%, based on a debt/- 

equity ratio of 3). Below we summarize capital and operating costs for the five fuel production options. The lipid-based processes are discussed first followed by processes that 

do not rely solely on the lipid content of the algal feedstock. 

4.2 Lipid-Based Conversion Routes 

4.2.1 Pseudo Vegetable Oil 

Figure 4-2 is a simplified flow diagram of a process to produce PVO from microalgae. 

No chemical reactions are involved; the components recovered are already present in the 

algae in a state suitable for diesel fuel. 

This process has been adapted from one used comercially on soybeans (Adams et al. 

198 1). Its success depends on identification of a suitable solvent; the hexane used in the 

soybean process might extract other algal lipids (e.g., glycolipids) that are detrimental to 

the process, resulting in lower conversion rates. Process optimization requires the 

development of a selective solvent to extract from microalgae cellular lipids a fuel that 

has the desired properties. 

Microalgae 

r-k-- Brine (to disposal) 

Harvesting 

(for recycle) 

Figure 4-2. 

digestion 

production facility 

Gums 

Pseudo-Vegetable Oil (PVO) Production Process Generalized Flow Diagram 
The lipids not extracted and the remaining portions of the algae (carbohydrates and protein) are anaerobicaliy digested to produce methane and carbon dioxide. Alternatively, 

the protein is recovered and used as animal feed, and only the carbohydrates are 

digested. Table 4-1 summarizes mass and energy data estimated for the PVO process 

based on processing the feedstock of the.reference algal culture facility (31,000 t annually of microalgae biomass with 30% lipid content). 

4.2.2 Transester if ication 

Figure 4-3 shows how the transesterification process might be adapted to producing fuels 

from microalgae (Kusy 1982; Freedman and Pryde 1982; Technical Insights Inc. 1980). 

This part of the process is noticeably similar to the PVO extraction process because of 

the front-end solvent lipid extraction. However, the rest of the process, including the 

reactor itself, is more complex because lipid extracts, used directly in PVO conversion, 

are chemically modified by the addition of methanol or ethanol. Substitution of 3 moles 

of methanol (or ethanol) for I mole of glycerol yields glycerol that is recovered as a byproduc t . 

4.2.3 Catalytic Conversion 

The Mobil methanol-to-gasoline process, as adapted to crude algal lipids, is shown in 

Figure 4-4 (Weisz et al. 1979; Voltz et al. 1976). A pre-extraction step, similar to those 

mentioned for the PVO and ester processes, would probably make higher conversion efficiencies possible. Again, details of the solvent system and extraction process are preliminary and depend on process optimization reactor studies. Calculated production data for 

the reference 1000-ha refinery are presented in Table 4-1; the methane quantity represents the total from catalytic conversion (5% of the total) plus anaerobic digestion (95% 

from unreacted organics). 

4.3 Nonli~id-Based Processes 

4.3.1 Anaerobic Digestion 

Figure 4-5 shows schematically the anaerobic digestion process as adapted for microalgae (Ashare and Wilson 1979). As discussed earlier, the organic fractions of the algae 

(all components except ash) are anaerobically digestible, so once the algae has been harvested, little if any pretreatment is required. Note that the other fuel processing options 

also utilize anaerobic digestion and produce methane as a secondary product. The biogas 

product typically contains 60% methane and 40% carbon dioxide by volume. The liquid 

effluent contains soluble nitrogen from the original algal proteins; the nitrogen can be 

recovered in the form of ammonia for recycle to the culture. Table 4-1 summarizes the 

various product yields from anaerobic digest ion. 

4.3.2 Ethanol 

Figure 4-6 is a flow diagram of an ethanol production process adapted from a corn-based 

process (Ralph Katzen Associates 1980; Technical Insights Inc. 1980). Some details of 

the process would change for a 10% algal slurry; for example, the mash cooking step has 

somewhat higher energy requirement because of the amount of water present. The fungal amylase enzyme is added to 'the cooked mash to convert the starch polymer to 

monomeric sugar units. The fermentation products are separated by distillation, and all 

nonfermentables are discharged out the bottom of the still. From a corn substrate, the 
Table 4-1. Reference Production Summary for Microalgae Processing Options 

Basis: 1000-ha algal culture facility 

33,000 t of algal biomass processed annually 

7% photosynthetic efficiency 

30% algal lipids 

Major Fuel Products 

Process Products (units) 

PVO Ester Fuel Gasoline Me thane Ethanol 

Ma jogf uel product ion 

(10 gallyr) 2.1 

Other products 

Methane (lo1' J/yr) 600 

Nitrogen (1 o3 t/yr 

as NH3) 2.4 

Glycerol (t/yr) -- 

LPG(~~:~ J/yr) -- 

(10 gal/yda -- 

Diesel (10;' J/yr) -- 

(10 gallyr) 

(see below) 1 .2 

a~s butane, 103,000 Btulgal 

components would be dried and sold as distiller's dried grains (DDG), slop, or stillage. 

Fermentables represent a much smaller fraction (13%) of the total algae biomass than 

they do in corn (65%), so separation of the nonfermentables before or during the mash 

cooking step might be economically desirable. Table 4-1 shows the various products from 

the ethanol-from-microalgae process. About 85% of the carbon dioxide produced is 

derived from anaerobic digestion; the remainder is derived from the fermentation unit. 

4.4 Summary of Fuel Processing Costs 

Table 4-2 and Figures 4-7 and 4-8 summarize the components that contribute to gross 

operating costs of each of the five reference fuel production processes based on processing the output of the 1000-ha reference facility. The cost components shown are raw 

materials (not including the microalgae feedstock itself), utilities, labor, maintenance, 

taxes, and depreciation. Note that labor and depreciation a r e t h e primary components in 

every case. These refining operations would be considered highly capital-intensive; only 

in the PVO process does depreciable capital contribute less than 30% to the gross operating cost. The efficiency of capital in these processes could be improved by (1) further 

process development, including equipment design, to improve the operating efficiency, or 

(2) a centralized refinery that serves a number of algal ponds; the larger refinery would 

realize significant economies of scale, as described by the six-tenths factor. The fuel 

processing plant sizes associated with the 1000-ha algal facility are well below their 

optima for efficient, intensive use of capital. The large labor and maintenance components might also be reduced in a full-scale process. 
Sulfuric acid Neutral salts 

Dried algae 

Solvent 

Purification Ester fuel product 

Extraction Protein, - 

tower carbohydrate, - 

Figure 4-3. Schematic of 

4.5 Fuel By-product Values 

Final product and by-product values for all 

k b + -other lipids - 

Solvent 

stripper 

Caustic soda 4 digestion 

--t Met hane 

- 

Ethanol Esterif ication Crude glycerol 

(or methanol) reactor CO, recycle to 

5 production facility r -I 

Transesterification Process 

fuel processing options are fixed according to 

estimates of year 20i 0 prices. Price forecasts are taken from DOE/BETD value-based 

cost goals that represent estimates of prices that alternative technologies must achieve 

to be competitive in 2010. 

Fuel cost goals are presented in Table 4-3 for all the primary fuels under consideration. 

Prices of gasoline, ethanol, methane, and diesel fuel are taken directly from the cost 

goals. Other primary fuel prices are derived by adjusting the fuel price according to its 

Btu content relative to a similar fuel. Prices of pseudo vegetable oil and ester fuels are 

derived from the projected price of diesel fuel, and the price of fuel gas from the projected price of methane. 

By-products from the various primary fuel processing options and their assumed value are 

listed in Table 4-4. In this table all fuel by-products are valued at their cost goal level. 

Values for other by-products are set at their current market price since year 2000 projections for these products are not available. Thus, total revenues from processing are 

underestimated by the real price increase expected for these by-products. In the absence 

of consistent projections for nonfuel by-products, we chose to err on the side of conservatism in evaluating fuel option feasibility. 

4.6 Reference Fuel Product ion Costs 

So far we have described the basic elements of fuel processing facilities, described the 

range of parameters for each element, and established a reference system for the five 

processes. We now can combine reference microalgae production and fuel with 



Table 4-2. Summary of Reference Capital, Operating, and Allowable 

Feedstock Costs for Fuel Processing Options 

Process 

Cost Category 

Ester Gasoline Methane Ethanol PVo Fuel 

Capital Costs ($1 06) 

Main process unit 2.73 

Glycerol by-product unit -- 

Methane by-product unit 3.27 

Subtotal 6.48 

Operating Costs ~$10'/~r) 

Raw materials 

Electr'c ower 

Water B 

Steam 

Labor, maintenance, taxes 

Depreciation 

Return on investment 

Subtotal (gross) 

Credits from Product salesc ($10~/~r) 

Carbon dioxide (438) 

Water (1 3) 

Nitrogen (223) 

Methane (2026) 

LPG 0 

Diesel 0 

Glycerol d 0 

Main product (2484) 

Subtotal (credits) (mj 

Net operating costa (2804) 

A1 a feed requirement 

tlyr) 33 

Allowable algae coste ($It) 85 

a~ot including algae feedstock. 

b~rocess and cooling water. 

C~y-product prices as in Table 4-4. 

d~ain product prices as in Table 4-3. 

e~he value ($/t) of the algal feedstock in producing the product slate indicated in 

Table 4-1 at the prices in Tables 4-3 and 4-4. 


Table 4-3. Estimated Year 2010 Fuel Cost 

Goals for Microalgae Products 

Project Price 

Fuel Product 

$/106 Btu $/gala 

Gasoline 

Diesel fuel 

Methane 

Fuel gas 

LPG 

Ethanol 

PVO fuel 

Ester fuel 

a~stirnated from Btu value using lower heating 

value of the liquid fuel. 

Table 4-4. Algal Fuel Processing By-products and Values 

Primary Fuel Byproducts Value (units) utilizationa 

Ethanol CO $0.07 (lo6 m ) Captive 3 3 

~ekane $7.40 (1 0 Btu) Export 

Methane by-products (see below) 

PVO Methane $7.40 (lo6 Btu) Export 

Methane by-products (see below) 

Ester Fuel G lycer o 1 $1.54 (kgk Export 

Methane $7.40 (10 Btu) Export 

Methane by-products (see below) 

Methane 

Gasoline 

C02 

3 3 $0.07 (10 m ) Captive 

Nitrogen (NH ) $1 83 (t) Captive 

Supernatant (?i20) $0.06 (m3) Captive 

Sludge Disposal 

Fuel gas $7.40 (10: Btu) Export 

LPG $7.40 (10 ptu) Export 

Diesel fuel $13.30 (lg Btu) Export 

Methane $7.40 (10 Btu) Export 

Methane by-products (see above) 

a"~aptive" products are used at the microalgae production facility. Export 

assumes sale of by-product to merchant market. 
processing costs to calculate the reference case fuel production cost. We also have 

noted that cost goals for fuels have been developed for biomass technologies. The purpose of these cost goals is to fix year 2010 prices at levels consistent with expected supply and demand for energy. Technology evaluations use these goals as fixed targets for 

directing research and evaluating program progress. 

Me use these cost goals to aid our evaluation of microalgae technology by assuming that 

all fuel products are sold at their cost goal level (Table 4-3). In addition, we assume that 

by-products are sold at the values indicated in Table 4-4. Since revenues are fixed when 

product and processing streams are identified, to determine technology status we must 

compare the cost of producing a microalgae feedstock with its value in the production of 

fuels and by-products of fixed value. 

The measure of cost-effectiveness we utilize for the reference case, and in later sections 

to evaluate the benefits of R&D improvements, is a comparison of production cost with 

the allowable processing cost. The allowable processing cost is the maximum value of (or 

amount a facility operator would pay for) microalgal feedstock used to produce a slate of 

products and by-products valued at the cost goal level. To calculate reference allowable 

processing costs, we use the feedstock composition and quantity of the reference algal 

biomass as inputs to each fuel process. Me then calculate product yields according to 

data for the reference process described above. Capital costs are fixed by the assumed 

plant size, and operating costs are calculated according to the amount of material processed. 

Annualized processing costs must be offset by revenues from the sale of products, so we 

estimate total revenues from the sale of all products, subtract the annualized capital and 

operating costs, and assume that the algal feedstock is free. The difference between 

revenues and costs is the maximum value of the feedstock. Dividing this value by the 

total amount of feedstock processed yields the allowable processing cost for the feedstock--the maximum amount a facility operator would pay for it. This allowable cost 

must be equal to or less than the production cost for competitive fuel production from 

microalgae. 

Allowable processing feedstock costs for the reference production and fuel systems are 

listed in Table 4-5. The large difference between estimated algal feedstock production 

cost and its value in fuel production illustrates the nascent stage of technology development. In the following sections we identify specific areas where technology can be 

improved and calculate the extent to which these improvements narrow the gap between 

production cost and processing value. Just as we chose specific parameter values to 

derive reference production and processing cost, the comparison of reference cost and 

value serves to place R&D improvement in perspective. The reference comparison shows 

that fuel production from microalgae currently is not commercially viable. 

In the next sections we evaluate how the gap between production and allowable processing cost might be closed and how a cost-effective technology can be developed by evaluating production cost improvements. We then evaluate processing improvements and 

combine them into an attainability concept that embodies achievement of the cost goals 

through identifiable, attainable R&D improvements. 
Table 4-5. Comparison of Feedstock Production Cost and 

Allowable Processing Cost for the Reference Case 

Fuel Processing Opt ion 

Reference Case 

Feedstock 

Production 

Allowable 

Processing 

Cost Feedstock Cost 

($It) ($It) 

Ethanol 393 

Methane 393 

Pseudo vegetable oil (PVO) 393 

Ester fuel 393 

Gasoline 393 

Note: All parameters for feedstock production and processing are 

set at the reference value, and all products and coproducts 

are valued at their cost goal value. All costs are in 1984 $. 

The feedstock production cost is the reference case cost of 

producing microalgae of 30% lipid content. The allowable 

processing cost is the value of the algal feedstock to a processing facility faced with fuel and by-product prices listed in 

Table 4-4. 
snn :el 

5.0 INITIAL SCREENING OF COST CONTRIBUTiONS 

To evaluate the effect of changes in microalgal feedstock cost from the reference value 

due to changes in individual parameters, a sensitivity analysis was conducted for each 

parameter discussed in the previous section. The parameters were grouped into four 

general areas--resources, biology, facility design, and financial--to permit relative comparisons among similar parameters. In this way, those parameters that significantly 

affect product cost can be identified. These "high sensitivity" parameters within each 

general area become a subset of parameters for subsequent examination. Our objective 

is to first screen the numerous variables to identify those with the greatest impact on 

production cost. Then we examine the effect of parameter changes that can be associated with specific improvements where several parameter values change, not just one. 

Sensitivities were derived in a static-comparative manner--parameter values for each 

variable were varied over the ranges developed in Section 3.0 holding all other variables 

at their reference value. Thus, they measure the relative sensitivity of each variable, 

not groupings of related variable changes. In the next section we will examine optimizations of the system by adjusting or improving groups of functionally interrelated effects. 

Two other methodological points should be kept in mind in evaluating the sensitivities. 

First, the range of sensitivity for each variable corresponds to those established in 

Section 3.0 as representative of current technology status and likely (achievable) and 

worst case values. Thus, in comparing sensitivities among variables, the differences do 

not represent numerically symmetric variations (e.g., 10% or 20% from the reference 

value) from the base case. Instead, relative sensitivity values compare likely outcomes 

derived from the set of technical or economic possibilities. 

Second, some variables cannot be independently varied due to technical interrelationships. Carbon dioxide supply cost is calculated as a fixed and variable cost with scale 

economics. Thus, parameter changes that induce production variability increase or 

decrease the amount of carbon dioxide required and therefore costs change. We have not 

adjusted sensitivity values of algal production cost for these variations as they are 

inexorable and representative of the technology. Other variables are subject to summation constraints; e.g., the sum of the proportions of lipid, carbohydrate, nitrogen, ash, 

and intermediate content of cells must equal one. Thus, changing one requires adjustments in all others. These variables are varied as a complete set maintaining a viable 

biochemical cell composition. 

In summary, sensitivities presented in this section serve to evaluate the effect on algal 

production cost of prescribed variations in the levels of key variables. These sensitivities 

are useful to identify factors whose level have a large impact on algal production cost 

and to establish priorities for reducing cost. 

5.1 Resource Parameter Sensitivity 

Thirteen of the 14 resource input assumptions were varied under the general category of 

resource parameters; only the assumed daily solar constant, established at a reference 2 value of 5000 kcal/m Id, was not varied. This reference value for the daily solar constant represents average sunlight available in the Southwest and is used to calculate the 

photosynthetic efficiency of the algal based on gross yield within the culture. Since we 

evaluate the region in general rather than specific sites, we hold this variable constant at 

a level representative of the region, thus taking the solar resource as given. 
The resource parameter sensitivities are presented in Figure 5- 1. We have identified 

three major areas in which resource issues significantly affect feedstock cost that can be 

categorized as water, nutrients, and energy. 

The water parameters are interrelated. Based on the reference system values and sensitivity ranges for these parameters, evaporation, salinity of the source water, and water 

supply costs are the major variables affecting product cost. Maximum evaporative losses 

in the microalgae facility can increase product costs 12% from the reference value of 

$3931~ However, even if evaporative losses were significantly reduced to 0.002 m/d, the 

effect on product cost is smaller (-3%) because acquisition costs for makeup water 

remain high and there are other nonevaporative water losses. The cost of water can have 

a large impact on algal costs and will be largely determined by local resource conditions. Although some individual water parameters do not have a large impact on reference production cost, their combined impact can be greater. In Section 6.0 we examine 

water linkages in detail and derive an integrated perspective on water issues; that is, we 

relate all sets of water variables to conditions likely to be encountered in practice. 

The major nutrient expenses for the reference system are carbon dioxide and nitrogen 

(ammonia). Carbon and nitrogen requirements and costs are determined by gross algal 

productivity minus the available supply in the source water and the unit costs for each 

nutrient. A more nutrient-rich source water can reduce nutrient cost (3% for nitrogen 

and 7% for carbon) but these benefits are tied to particular water resource characteristics and may be limited in availability. 

Energy costs also affect product costs for the reference system. While this parameter is 

less significant than water and nutrients, the large energy requirement for the facility 

indicates that a low-cost electrical energy supply is beneficial and should be considered 

during facility siting and design. 

Of all the parameters shown in Figure 5-1, six warrant additional analysis because their 

influence on product cost is profound. These parameters are evaporation, salinity of the 

source water, carbon content of the source water, energy costs, water supply costs, and 

the distance carbon dioxide must be transported to the microalgae production facility. 

5.2 Facility Design Parameter Sensitivity 

Of the 21 design-related parameters used in the model, 15 parameters are varied. 

Figure 5-2 presents the sensitivity of product cost to changes in these 15 parameters. 

Parameters that were not varied include basic pond geometry (length-to-width ratio set 

at 10 in the reference system), facility size (fixed at a nominal value of 1000 ha), efficiency of the drive system for mixing the culture system (90961, and the minimum solids 

concentration of the product stream (10%). 

Effective culture area and effective operational downtime are design features that 

directly affect the productivity of the system; improvements in either parameter 

increase the overall productive area for the facility, thereby increasing the total net 

yield. CKanges in mixing velocity, mixing drive efficiency, and depth change the energy 

required to move or pump the culture water and hence energy costs for the system. 


Similarly, carbon and nitrogen losses due to outgassing from the culture surface are significant design parameters also affected by resource cost issues. Finally, the type of 

harvester system or the efficiency of solid removal by any harvester system affects final 

product cost. 

The important facility design parameters influencing product costs are effective culture 

area, downtime, channel width, mixing velocity, culture depth, carbon and nitrogen outgassing losses, mixing sys tem pump efficiency , and harvester solids removal efficiency. 

5.3 Biolo~v Parameter Sensitivitv 

The model utilizes 18 parameters to define the biological relationships of the mass culturing microalgae. Of this total, many parameters are fixed because they are typically 

well understood and are used by the model to calibrate other system effects. Examples 

of such values are minimum nitrogen cell content (0.03 g/g dry weight), nutrient content 

of the cell, and energy content of the various cell contents. Parameters such as lipid, 

carbohydrate, and protein content and parameter values used to calculate algal productivity must be varied as a group to maintain consistency among related variables. Independent variation of any one parameter was avoided to prevent unrealistic single parameter values. The biological parameters affecting feedstock cost are presented in 

Figures 5-3 and 5-4. The sensitivity of nonproductivity related parameters in Figure 5-3 

indicates that salinity tolerance and growing season have a large effect on feedstock 

costs* 

Sensitivities for detention time, depth, and density are illustrated in Figure 5-4. Functionally interrelated in nature and in the model, these factors are each associated with 

common aspects of the technology. Increased depth at constant density has a corollary 

in increased density and a fixed depth. However, achievement of improvements in either 

are tied to the characteristics of algal species and to the design of the facility. Thus, 

not all combinations represented in the sensitivities are feasible when viewed in light of 

species characteristics. However, the sensitivities do proxy for the d r a m a t i c c o s t reductions possible if species can be developed to live in denser cultures at greater depths. 

Likewise, decreased detention time illustrates enhanced productivity, a derivative of 

improved species efficiency. In the next section we will summarize these effects 

through low mass productivity to illustrate more clearly possible improvements in the 

technology. 

5.4 Financial Parameter Sensitivity 

Financial parameter sensitivities illustrated in Figure 5-5 capture t h e e f f e c t of a variety 

of economic uncertainties associated with any large construction project. As is customary in technology evaluations, real cost increases for basic inputs are assumed zero. All 

costs are measured in real (constant 1984) dollars and assuming constant real costs is 

consistent with assuming that a microalgae project, by itself, would not tax resources 

sufficiently to induce real price increases and that the facility uses materials not 

expected to incur price inflation above the general rate of inflation. To illustrate the 

impact of relaxing these assumptions, we calculated algal production costs at 2% real 

price increases, throughout the period of construction and operating period for capital, 

operating, and maintenance costs. The effect is most severe for operating costs, 
450 

440 

h 

=[ 430 69 * 

00 

420 V 

c. 

cn 

," 410 C 

. 

0 - 

w 

400 

0 

393 a 

Q] 

a 

- (3, 

380 2 

0 .- 

2 

370 

I 

300 

Parameter 

Units 

Range 

Reference 

Salinity 

tolerance 

g/TDSL 

10-120 

35 

Growing 

season 

days 

days 

200-365 

250 

Cell 

phosphorous 

content 

Figure 5-3. Biological Parameter Sensitivities 

increasing the price by about 63%. We illustrate these impacts for completeness as there 

is no reason to believe that real increases in microalgae costs will exceed those of the 

general economy. 

The reference case assumes an open system design with culture exposed to the environment. Covers have been proposed as a means to protect the culture from adverse environmental impacts. T see how the addition of covers affects costs, we add capital costs 9 equivalent to $1.50/m for the effective culture area. The resulting feedstock cost is 

$490/t, a 22% increase over the reference case. We did not reflect the possible productivity benefits of covers in this analysis in the spirit of the sensitivity analyses of this 

section. Below, we will examine the integrated impact of covers. 

The reference cost assumes a capital cost of $5000/ha for pond liners. To capture the 

uncertainty attendant to this particular component we examine a 25% decrease and 

increase in cost. As illustrated in Figure 5-5, the variation assumed changes algal cost 

by only 2%, an encouraging result given the speculation that current liner technology 

may not be sufficient for extended, commercial operation (Dodd 1984). 
h 

.cI 012345678 

;>; Detention time (days) 

d 

cX, C 

(T, 

=. 500 - + 

400 - 0 Reference value C 

.s +300 - 0 

200 - I 

2 

a 

a 100- m 

a 

m 0 I I I I I I I 

9 0 0.15 0.25 0.35 0.45 0 .- 

2 Depth (m) 

500 

400 - Reference value 

300 - 

200 - I 

400 800 1200 1600 2000 

Density (rng/L) 

Figure 5-4. Sensitivity of Algal Production Cost to Detention Time, Depth, and Density 

We reflect capital cost uncertainty by increasing them 25%, a means of accounting for 

possible underestimation of balance of system costs. The result is an 8% increase over 

the reference algal production cost. 

Finally, higher rates of return are often applied to new, risky investments for compression with more mature technologies. We raise the return on debt and common and preferred stock by 50% over the reference case and find that production cost increases by 

8% to $425/t. This increase may be appropriate for early, risky applications but a 

mature microalgae technology should compete for capital at rates closer to the reference values. 

In the next section we look at integrated representations of these uncertainties as a 

means of bounding expected costs and of deriving the extreme bounds of required 

technology improvements. 
Parameter Capital ( cost 

escalation 

Units */o 

Range 0-2 

Reference 0 

Maintenance 

cost 

escalation 

O/o 

0- 2 

0 

Operating 

cost 

escalation 

O/o 

0-2 

0 

All 

cost 

escalation 

O/o 

0-2 

0 

Cover 

cost 

$/m2 

0-1.50 

Liner 

cost 

$/ha 

4000-60( 

5000 

Balance 

of system 

costs 

Yo 

0-25 

0 

Return 

on 

nvestmenl 

25% over 

base 

Figure 5-5. Financial Parameter Sensitivities 

5.5 Important Parameters for Further Evaluation 

Figures 5-1 through 5-5 present all the sensitivity results of changing input assumptions 

to calculate product cost. Table 5-1 lists those parameters by category that have a significant effect on feedstock cost through the range of assumed values. Analyzing sensitivities performed one at a time gives an indication of the relative importance of the 

parameters. However, structural linkages among variables, when taken into account, can 

exacerbate or dampen single variable sensitivities. The next section examines functional 

linkages and their effect on microalgal production cost with the object of identifying 

technology configurations that maintain resource and biological feasibility and marketdetermined production cost goals. 
Table 5-1. Summary of Most Critical Resource, Facility 

Design, and Biology Parameters 

Resource 

Evaporation 

Source water salinity 

Carbon content of source water 

Energy cost 

Water supply costs 

Distance to carbon dioxide supply 

Facility Design 

Effective culture area 

Downtime 

Channel width 

Mixing velocity 

Culture depth 

Carbon outgassing losses 

Nitrogen outgassing losses 

M ixine: drive ef f iciencv 

~arvster solids rerno;al efficiency 

Biology 

Salinity tolerance 

Phosophorus content 

Growing season 

Algal chemical content 

Algal productivity 
6.0 EVALUATION OF IMPORTANT TECHNOLOGY ISSUES 

In this section we assess the major R&D issues associated with producing fuels from 

microalgae in order to determine the viability of the technology and the appropriate 

development paths. This objective is achieved in five sequential stages of analysis. The 

first is to determine the important linkages among the technical parameters that ultimately dictate the cost of producing microalgae feedstocks. 

Section 5.0 discussed 20 critical parameters in the mass culture system identified 

through sensitivity analysis. These parameters are too numerous for extensive comparative static analysis and are related in important respects. By establishing the linkages 

among them, the analysis in Section 6.1 develops quantifiable cost impacts, brings the 

major research issues to the forefront, and thereby facilitates the analysis of R&D 

priorities. 

The second stage of the analysis is to evaluate alternative fuel products to determine 

which are the best candidates for microalgae production. This stage is conducted by a 

process of elimination that includes three distinct steps. (I) The total revenues are projected for each fuel product and production configuration, including the value of the primary fuel and by-products produced; (2) the costs of fuel processing for the reference 

case are subtracted from the estimated revenues, and the residual is the amount that can 

be spent for the feedstock, which we call allowable feedstock processing costs; and 

(3) allowable feedstock processing costs are compared to projected feedstock production 

costs for the five major fuel types. Those fuel products for which projected production 

costs are closest to the allowable processing costs are judged to be the best candidates 

for production from microalgae. These fuel cycles serve as the basis for defining 

research targets that, if realized, meet the economic requirement--a commercially 

viable fuel production technology. 

The third stage of analysis is to construct and compare attainability targets for microalgae feedstocks production. Our comparison of costs includes a number of assumptions 

about critical technical parameters, such as microalgae productivity. This analysis is the 

basis for establishing attainability targets by varying those parameters that can be 

affected by R&D investments. 

The fourth stage of the analysis is to evaluate attainability targets for fuel processing. 

These attainability targets embody improvements in specific parameters that are judged 

to be technically attainable but require research and development. Attainability targets 

for feedstock production and fuel processing are combined to construct an integrated 

attainability concept, which is compared with fuel cost goals to determine the potential 

of the technology for becoming a cost-competitive source of energy. Finally, the targets 

are then subjected to sensitivity analyses to examine alternative combinations of parameter levels that meet the cost-effectiveness test. This last step explores alternative 

R&D paths that fulfill the criteria for technology feasibility. 

The objective of this analysis is to ascertain what improvements in microalgae production and processing technology are needed for competitive (with conventional fuel technology) fuel production. In order to judge what level of parameter improvements are 

needed, we need some standard or goal to serve as the threshold for costcompetitiveness. We have chosen the year 20 10 cost goals for rnicroalgae-derived fuels 

presented in Section 4.0 (and summarized in Table 6-1). We wish to derive, if feasible, an 

integrated set of improvements in microalgal production and fuel processing that achieve 
fuel costs equal to or less than these cost goals. Our method of analysis therefore calculates fuel and by-product revenues at their respective cost goal levels, and the standard 

by which we measure technology feasibility is the comparison of feedstock production 

cost with the allowable processing cost. Equality of these values signals fuel production 

at the target cost goal level since the sum of value added at each stage of production 

exactly matches the competitively determined market price. 

Before identifying and evaluating technology improvements, it is essential to understand 

the implications of the methodology, Fixing fuel values at their cost goals, and evaluating the technology by equating feedstock values in the two stages of production, 

ignores uncertainty inherent in the projection of year 2010 fuel values. This uncertainty 

is not passed through to the evaluation of production and processing costs, which are 

compared as though they are discrete, certain indicators of costs, values, and technology 

feasibility. 

Initially, we make rigid (deterministic) comparisons based on these cost goals since they 

represent a uniform estimate of year 2010 values and since the present goal of the 

microalgae program is to achieve cost effectiveness soon after the turn of the century. 

But neither the cost goal values nor their year should be taken as fixed targets that, if 

not met, signal technology infeasibility. Fuel values could be higher in 2010, so exact 

equivalence of microalgae-based fuels with forecasts of conventional fuels is not an 

absolute indicator. Moreover, expected depletion of fossil fuel reserves results in everincreasing fuel prices after 2010, so if the target is missed for that year, the technology 

may still be able to compete at a later time. We seek to match the conditions of technical feasibility to market conditions to aid in program development decisions. 

The comparisons in the following sections set very rigid criteria for technology feasibility--goals for year 2010 must be met exactly or exceeded. We will relax this constraint after it has served its purpose, which is to focus our attention toward identifying 

specific, attainable research and development improvements. Sensitivity analyses in the 

final sections will provide a wider perspective on the questions of technology feasibility 

and R&D requirements. 

6.1 Important Linkages and Critical Issues in Microalgae Feedstock Production 

The parameter screening in Section 5.0 evaluated the changes in algal biomass production 

costs resulting from a change in the value of an individual parameter, all other parameters held at their reference level. This procedure isolates the relative importance of 

parameters but ignores biological, engineering, and economic interrelationships inherent 

in the technology configuration. , 

For exampie, we saw that varying culture depth from the reference value has a significant impact on the cost of producing lipids (see Section 5.0). However, this first-order 

impact, estimated by varying a single parameter, could be reduced or exacerbated when 

secondary impacts are taken into account. For example, at constant productivity, the 

advantage of deeper cultures is that there is less variation in culture conditions (e.g., 

temperature) with diurnal variation. Such protection may be very important to microalgae that meet high performance levels but are sensitive to temperature. On the other 

hand, deeper cultures increase head loss and flow rates in the system, resulting in higher 

energy consumption for mixing and pumping. In order to map response effects accurately 

and use these mappings to determine the optimal R&D approach, we must explore these 
Table 6-1. Major R&D Issues for Fuels from Microalgae 

Water demand 

Carbon dioxide demand 

Algal productivity and products 

Harvesting 

A. Algal Production 

Water Demand 

Carbon Dioxide Demand 

Algal Productivity and Products 

Algal Harvesting 

B. Fuel Processing 

Alternative Fuel Products 

Scale Economies 

Process Productivity 

By-product Values 

C. Cost Goals (1985 $) for Fuels from Microalgae (2010) 

Gasoline $1.75/gal 

PVO $1.75/gal 

Ester Fuel $1.75/gal 

Methane $7.40/mscf 

Ethanol $1.20/gal 

interdependencies; we must estimate both the marginal costs and the marginal benefits 

implied by variations in the system configuration or its parameters. 

The reference case represents a set of equilibrated parameter levels picked so that the 

complete production system is a technically feasible and consistent representation of 

values across all components of the system. The cascading effects that emanate from 

the variation of a single parameter have an impact on many important process parameters. These linkages are vital also to our effort to isolate factors important in technology improvement and to evaluate ways to reduce production costs. 

The ',initial screening of individual parameters indicate how the system parameters affect 

feedstock cost. However, by careful examination of these initial parameters, we can 

define interrelationships or linkages among groups of these parameters that enabled us to 

reduce the number of major effects to those listed in Table 6-1. The analysis begins with 

the parameter sensitivity identified in the initial screening, works through the major 

linkages inherent in the model, and then identifies the implications for R&D improvements. This approach can be viewed as an attempt to develop a frame of reference for 

evaluating alternative approaches to lower production costs by reducing a large number 

issues to their lowest common denominator--the technology improvements required for 

an economic production system. We begin by exploring interrelations with and among the 

algal production groupings of Table 6- 1. 
&,%, I 'Water Demand 

Inspection of the sensitivities for resource and biology parameters In Section 3.0 indicates the critical sensitivi~y of reference production cost to water demand. In order to 

systematicaIly evalua~e the technology of growing microalgae in the saline waters of the 

southwestern United States, we begin by illustrating the linkages among water quality, 

environmental conditions, and algal species characteristics. Figure 6- 1 illustrates the 

progressive linkage of source water through system design alternatives to net water 

demand and cost. This figure shows an expansion of the original production concept (on 

the right side of the figure) to include a means of mitigating high water costs--an environmental protection system or cover to reduce evaporation and nutrient outgassing (on 

the left side of the figure). First, we examine mechanisms for controlling water costs in 

the reference (uncovered) system and then we estimate the value of covers to the system. 

For uncovered systems, water demand is a function of net makeup water, which is 

determined by evaporation; salinity of the source water; and the maximum operating 

salinity of the culture system, which is determined in turn by the salinity tolerance of 

the microalgae. For a given evaporation rate, higher source water salinities or lower 

species salinity tolerance result in higher blowdown requirements to maintain culture 

salinity. These parameters are linked; controllable parameters (e.g., species salinity tolerance) can be varied to mitigate effects of uncontrollable parameters (e.g., salinity of 

the source water) to minimize overall water demand, subject to the degree of control 

that can be exercised. 

Variations of the reference cost of producing algae to variations in both the evaporation 

rate and the algal salinity tolerance are illustrated in Figure 6-2. The three curves, A, B, 

and C, map combinations of salinity tolerance and algae production costs for the reference, high, and low evaporation rates, respectively. Because of the effect on blowdown 

and water demand for any given evaporation rate, algal salinity tolerances above the 

reference level of 35 g TDS/L decrease production cost and tolerances below 35 g TDS/L 

increase costs. 

The combined effect of salinity tolerance and evaporation rate, illustrated in Figure 6-2, 

demonstrates the importance of choosing an algal species with as high a salinity tolerance as possible to control costs when evaporation rates are high (either at a specific site 

or as a result of diurnal or seasonal effects). Curve A shows the reference case variation 

in production cost due to variations in the salinity tolerance. Production costs rise, as 

salinity tolerance decreases, from the reference tolerance of 35 g TDS/L, reaching 

$420/t at 20 g TDS/L (Curve A, Figure 6-2). At salinity tolerances above 35 g, (again 

using Curve A with the reference evaporation rate) production costs decline, although 

slowly, until most of the cost reducing benefits have been realized at 80 g TDS/L. 

Increasing salinity tolerance from 35 to 80 g TDS/L (130%) amounts to a cost reduction 

of only about 2%; incremental benefits are not symmetrical to incremental decrements 

measured relative to reference salinity tolerance. 

As the evaporation rate is varied from the reference value of 0.0035 m/d to the high and 

low levels, 0.01 and 0.002 m/d, respectively, the same relationship is apparent--some 

benefits of higher salinity tolerances are realized, while cost increases dramatically at 

lower than reference salinity tolerance levels. The result is most dramatic for the high 

evaporation case (Curve 8) where costs increase almost 12% as the salinity tolerance is 

decreased from 35 to 25 g TDS/L. On the other hand, increasing the tolerance from 35 

to 80 g TDS/L for the same evaporation rate (8) virtually compensates for the cost 
Fixed by design Evaporation rate and climate Evaporation rate 

1 

I 

I I 

Species saline Species salinity 

tolerance tolerance 

I Species I 

pecies temperatu characteristics and Species 

tolerance requirements Temperature toleran 

tradeoffs I 

Operating salinity Operating salinity 

I I 

Protected Lower 

Design cost tradeoffs b1 I 

Lower Water resource 

Water demand tradeoffs Water demand 

I I 

Reference water cost 

Uncovered 

1 Allowable cost for I 

1 

I protection 

Figure 6-1. Linkages among Water Source Salinity, Water Use, and Algal Species 

Requirements 
- A Reference evaporation \ / rate = 0.0035 rn/d 

37OF \ L- c LOW evaporation 

3601 \ rate = 0.0002 m/d 

Reference 

salinity tolerance = 35 g 

Salinity tolerance 

Figure 6-2. Sensitivity of Reference Production Cost to Variations in the Evaporation 

Rate and Algal Salinity Tolerance 

increase imposed over the reference case. That is, under hi h evaporation situations, 

production costs can be maintained at the reference level ( 8 393/t) by raising salinity 

tolerance from 35 to about 80 g TDS/L, thus verifying a biological control function. 

Uncovered algal production systems are likely to experience diurnal and seasonal changes 

in the evaporation rate. Thus, even if the average rate of evaporation is 0.0035 m/d, as 

characterized in the reference case, salinity tolerances above the reference 35 g TDS/L 

can help mitigate these variations. Since most benefits of increased salinity tolerance 

are realized at 80 g TDS/L, regardless of the evaporation rate, screening and selection of 

algal species should include provision for characterizing species that can sustain production in waters of salinity as high as 80 g TDSJL. This affords the maximum protection 

available against the effects of evaporation and provides an additional benefit in terms 

of mitigating the effects of higher salinity source water to be discussed below. The 

80 g TDS/L target is the ideal, but as Figure 6-2 illustrates, there are substantial benefits to be realized by increasing salinity tolerance to the 30-60 g TDSJL level and this 

should serve as a minimum target for directing species selection and optimization. 

Given the prominent role that evaporation rate plays in cost sensitivities, it is useful to 

examine strategies to reduce evaporation. Covers have been proposed as a means of providing environmental protection to algal cultures, reducing evaporation along with providing protection against dust, predators, wind, and other deleterious effects. The complete definition of the technical benefits of covers has not been established, so at this 

time it is not possible to define the complete system impacts. Moreover, cover systems 

proposed to d a t e a r e not optimized for microalgae and therefore estimated costs are subject to considerable uncertainty. 
A preliminary estimate of the value of covers is derived by estimating the maximum physical impacts expected, and calculating the associated cost reduction and maximum 

allowable cost for covering the system, either entirely or in some intermittent fashion 

(only over carbon dioxide spargers, over half of the pond, etc.). Maximum benefits are 

calculated by lowering, from the reference to the low values, the evaporation rate and 

all nutrient outgassing losses; raising the growing season to 365 days; increasing the 

source water salinity to 35 g TDS/L and salinity tolerare to 80 g TDSJL; and evaluating 

the algae production cost at $0.05, $0.067, and $.20/rn water cost. We do not contend 

that these targets can be met singularly or collectively but use these levels only to 

establish the maximum expected benefits of covers. 

Measured relative to the reference algae production cost of $393/t, the low, reference, 

and high water cost cases, with cover effects, result in $96/t, 108/t, and 112/t cost 

reductions, respectively. These savings, amortized over the facility life (20 years), 

translate into allowable initial capital costs for covers of $2.25-$2.35 per square meter 

of covering material, assuming the entire pond or raceway area is covered. Thus, 

covering half of the pond area doubles allowable cost and so forth, although the likelihood of the assumed optimistic parameter levels and associated cost reductions being 

achieved with partial coverage is low. These allowable cover costs are insuffi ient corn- s pared to covers typical of greenhouses that cost in the neighborhood of $15/m and must 

be replaced every 3-4 years (no allowance for maintenance or replacement is embodied in 

the estimated allowable cover cost). Clearly, conventional covers are not cost effective, 

except where production costs well in excess of $393/t are feasible owing to high product 

value. Cost-effective covers, even given high water costs, will require new materials or 

concepts with low costs and high benefits. Research directed in this area must look for 

novel concepts that can meet the rigorous standards used above and provide system protection for well below the cost of conventional covers. 

Another tradeof f function, involving the development of biological improvements to mitigate resource constraints, is between algal salinity tolerance and the cost of saline 

water. Since algae compete with other uses for water, at least for low salinity water, we 

expect that the lower the source water salinity the higher the cost and therefore the 

greater need for higher salinity tolerance to absorb cost increases. Conversely, high 

salinity water has few alternative uses and should be lower in cost. Therefore, in the latter case salinity tolerance should be less important, provided the tolerance is above the 

salt content of the source water. 

We examine this hypotheses through two cases. The first is constructed by setting the 3 water cost at the high level ($0.20/m ) and the corresponding source water salinity at the 

low value (10 g TDS/L) and then calculating the algae production cost for the reference 

level of salinity tolerance and for higher values (50 and 0 g TDS/L). The same calcula- 4 tions are also performed for the low water cost ($0.05/rn ) with high (25 g TDS/L) source 

salinity. The results of these two cases (Table 6-2) confirm the hypothesis that if more 

expensive, lower salinity water is used, salinity tolerance of the algae is more important 

in terms of minimizing algal production cost, 

The two cases illustrated in Table 6-2 both result in variations in price from the reference case. We can further enrich the comparison by calculating the increase or decrease 

in the evaporation rate that restores the cost to the reference level of $393/t. In the 

high water cost case (A), the evaporation must be lower to compensate for increased 

water costs and for the low water case, a higher evaporation rate is allowable, thereby 

relaxing another binding environmental constraint. We define the equivalent evaporation 

rate as the level of evaporation that, given specific changes (from the reference case) in 

water cost and salinity, returns the algal production cost to the reference $393/t level. 
Table 6-2. Tradeoffs between Salinity Tolerance and Water Costs 

Case A: High cost ($0.20/m3)/low salinity (10 g TDS/L) water 

Salinity Tolerance Algae Cost Increase from Reference Equivalent 

(g TWL) ($/t) ($It) Evaporation Rate 

Case 8: Low cost ($0.05)/high salinity (25 g TDS/L) water 

Salinity Tolerance Algae Cost Decrease from Reference Equivalent 

(g TDS/L) ($It) ($It) Evaporation Rate 

The equivalent evaporation rate is that which restores algal production cost to the reference level, given the indicated level of salinity tolerance. 

For Case A, with high water cost, the equivalent evaporation rate, assuming a salinity 

tolerance of 35 g TDS/L, is 0.0009 mld, a reduction of 75% compared to reference evaporation. Thus, to make up for the more expensive water, drastic reductions in evaporation rate are required to maintain algal production costs. If salinity tolerance is raised 

from 35 to 80 g TDS/L to offset the increase in water cost, the equivalent (allowable) 

evaporation rate in Case A rises to 0.0012 m/d--mitigating the higher water cost still 

requires a 65% reduction in evaporation rate or production costs will rise from the reference case level of $393/t. These stringent evaporation rate limits are not achievable 

without covers in extensive technology adoption in desert climates and, as demonstrated 

above, are not currently feasible. Our first approximation of the impacts of high water 

cost on microalgae for fuel production suggests that high cost/low saline water resources 

are not economical and algae must be developed to withstand high source water 

salinities. 

The low water cost case, Case B in Table 6-2, is characterized by reduced production 

costs owing to the assumed lower (than reference) water cost associated with the higher 

source water salinity. In contrast to Case A, the equivalent evaporation rate is higher 

than the reference rate of 0.0035 m/d; higher evaporation can be tolerated because the 

water is cheaper. At 35 g TDS/L salinity tolerance in Case B, the equivalent evaporation 

rate is over 0.0085 m/d and higher for higher salinity tolerances. The availability of low 

cost water greatly relaxes the evaporation rate constraint, at least in the reference production regime. Note, however, that in Case B even though evaporation rates considerably higher than the reference case are allowable, holding production cost constant at 3 the reference $393/t, the absolute cost reduction due to low cost ($0.05/m ) water and 
very high algal salinity tolerance (80 g TDS/L) is only $12-13/t. Low water cost helps 

reduce production costs, but only marginally. To substantially reduce the reference case 

microalgae production cost, we will need to identify other high impact areas that offer 

the opportunity for cost reduction. 

To summarize this section on water costs, we conclude that algae salinity tolerance is an 

important characteristic and that technology feasibility depends on development of 

species with a tolerance of at least 35 g TDS/L, and the goal should be at ieast 

50 g TDS/L to provide most of the potential protection possible against extreme environmental conditions. Although covering algal systems offers many benefits, the task of 

developing a cost-effective covering system is arduous. At t e extreme, the allowable 

cost for covers in the reference system is only about 2.25/m 9 of production area, well 

below the cost of conventional plastic covering systems. Partial covering, say over the 

carbon dioxide delivery system to improve utilization of this expensive input, may prove 

cost effective, but efforts to devise a full system cover are constrained by the low allowable cost and must be evaluated relative to other opportunities to reduce production 

cost. 

6.1.2 Carbon Dioxide 

The cost of carbon dioxide is a large fraction (about 30% in the reference case) of product cost because 40%-50% of the dry organic weight of algae is carbon. Additional carbon dioxide requirements, beyond the biological requirements for respiration and algal 

carbon storage, are due to carbon losses from outgassing and blowdown. 

The supply assessment in Section 3.2 concluded that flue gas might be a vast potential 

source of large quantities of carbon dioxide for mass culture of algae. The cost of flue 

gas carbon dioxide is a function of three components. The first is the cost of buying the 

C02 from the power plant. Given projected estimates of the demand for CO &ee Ec:$ 

power plant operators are likely to charge a nominal price for the flue gas. 

component of CO price is the amortized cost of recovery, purification, and compression 

of C02 from the T lue gas. These recovery costs are a large part of the final delivered 

cost because carbon dioxide represents only about 16% of the total flue gas volume and 

purification requires extensive processing. The third price component is the amortized 

pipeline and pumping costs for delivery of C02 to the algal production facility. 

3 The cost ($0.13/m ) of purchased C02 for the reference microalgae facility was about 

$80/t microalgae producedg Tgis cost was based on the assumptions that the microalgae 

system required 0.082 x 10 m /d of carbon dioxide piped from a power plant 80 km from 

the production facility. 

The effect of varying transportation distance on C02 cost is illustrated in Figure 6-3. 

6 3 Unit costs of CO iven reference demand (0.082 x 10 m Id) for the mass culture 

facility, increase $.& for each add- ional kilometer of pipeline. However, the unit cost 

of C02 does not drop below $0.1 l/mY, the amortized fixed capital and operating costs of 

the recovery system. 

Due to assumed scale economies in the transportation of ca on dioxide, the unit cost 6 increases sharply with CQ2 demand levels below 0.082 x 10 m /d. Figure 6-4 illustrates 

scale economies for constructing a C02 supply system. At CO delivery rates below 

6 3 0.082 x 10 m /d, unit costs for carbon dioxide increase rapidly ? e assuming a fixed 

6 qe9 supply distance of 80 km). At delivery rates above 0.082 x 10 m Id, these unit costs 

decrease. If be piped to production facilities, cost reductions can arise from 

facilities. If three facilities were colocated, the cost of C02 

result achieved above if the transportation distance is zero 

(the production plant is colocated with the C02 source). 

The following scenarios illustrate possible means to reduce CO costs to a mass culture 

facility. The scenarios take advantage of ways to minimize Eo2 supply distance and 

maximize CO supply economies to reduce the final C02 unit costs and therefore product cost. ~abfe 6-3 presents the cost summary for reference during the following discussion of combining possible cost reductions to achieve minimum-cost COZ. 

Above we illustrated that a 15% ($0.13 to $0.1 l/m3) decrease in unit cost for C02 is 

achieved if we assume that the microalgae mass culture facility is located next to a 

power plant and the transport distance is zero. The same result achieves by assuming 

that at least 3 algal facilities are colocated and scale economies are thereby realize . The combined effect of both assumptions is a reduction in C02 cost to $0.09/rn 9 . Locating a mass culture facility next to a small coal- or gas-fired power plant has considerable merit, given that the mass culture facility would represent a captive market 

for the power plant's flue gas, some of the electricity, and the waste heat. An alternative scenario would be that the operators of the algal facility elect to construct and 

operate a power plant as a co-generating facility, selling excess energy to the utility 

grid. 

3 A coal-fired power plant is estimated to produce 5400 m of flue gas p r MWh containing P 16% C02 (Hesketh 1979). A natural-gas-fired plant produces 4700 m of 11% C02 flue 

gas. To produce sufficient C02 to meet a 1000-ha mass culture facility's need would 

require a coal-fired power plant of 5.6 MW or a gas-fired plant of 9.4 MW. The power 

plant is assumed to operate at a 70% capacity. Additional power plant capacity would be 

required to ensure a constant, reliable source of C02 to the facility, so two 10-MW are 

used as a conservative basis for analysis of colocation of a single production facility and 

associated electrical power plant. 

Projected capital costs of coal-fired power plants range from $900 to $1000/kW with a 

busbar energy cost of between 30 and 40 mils/kWh, depending on the type of coal and 

region of the country (Reynolds 1982). Because of the small size of the coal plants, a 

capital cost of $1200/k~ was assumed. The two 10-MW power plants would add 

$35 million to the algal plant capital costs. Operating costs for the power plant are 

assumed to be recoverable through the sale of excess energy (95% of plant output) to the 

uti1if.y grid, sale of energy (at $O.O3/k~h), and sale of carbon dioxide to the mass culture 

facility. These transactions, accomplished through "arms-distant" agreements for 

internal accounting purposes, would ensure 3 constant supply of C02 and energy to the 

facility. The resuiting C02 cost is $.07/m and algae production costs drop to $356/t 

(from the reference $393/t). 

If several microalgae production systems are colocated with a large coal facility, then 

scale economies are possible due to economies in power plant production rather than C02 

transportation. Five colocated production facilities combined with ownership of a 3 100-MW coal-fired plant might be able to internally price C02 as low as $0.05/m and 

lower production costs to $345/t. 

Above, we illust ted specific scenario3 that lower the cost of C02 from the reference 

level of $0.13/m 7 to as low as $0.05/m , thereby loweringjproducti n costs to as low as 3 $345/t. Over the range of C02 costs from zero to $0.20/m , a 1Q/m change in C02 cost 
Table 6-3. Summary of Alternative Scenarios to Reduce C02 

Costs to a Mass Culture Facility 

Scenar io CO C~st Algae Production 

) ($It) 

Reference system 0.13 

Power plant ownership 

increase scale 0.1 1 

Increase scale and colocated 

plants 0.09 

With plant ownership and 

colocated power plant 0.07 

Several colocated power plants 0.05 

changes algal production costs about $6/t. So, free CO 23Y ields a production cost of about 

$315/t and the most expensive C02 considered ($0.20/m ) implies a cost of $435/t. 

Currently, no developed and available carbon dioxide suppiies, sufficient to meet a single 

plant, exist in the desert southwest except at sites located next to existing pipelines, and 

to draw from these sources are currently in place. The reference CO 

was derived by assuming the plant is located 80 krn from a dedicate2 

C02 well--the cost includes provision for drilling, laying the pipe, and transporting the 

C02 to the algae production site. 

Isolated C02 resources may be available to support the reference scenario but extensive 

algal culturing will require a supply infrastructure including vast C02 resources, main 

transportation pipelines, and a distribution system to serve individual sites. If the source 

is a large or several large power plants coloyted in the same region with the algal 

farms, then costs as low or lower than $0.05/m are possible. Alternatively, if the C02 

resources and infrastructure are developed as part of enhanced oil recovery technology, 

then the emergence of an algal industry may be supported by a large, reliable C02 supply 

and infrastructure and low-cost C02 will be available. 

At this time, we can only speculate about the availability and cost of COq for microalgae 

production in the desert southwest. The technology development effort must emphasize 

the efficient utilization of C02 through well-designed sparging systems, hydraulics and 

process controls, and by screening for algae that operate as close as possible to the limits 

of energy conversion efficiency. But even with these provisions, the cost of carbon will 

still dominate production costs, and factors beyond the control of the scientific domain 

will play a major role in the economic feasibility of funds from microalgae. Resource 

studies, conducted in support of the technology development program, must continually 

evaluate systems for providing C02, including possibilities such as direct extraction of 

carbon dioxide from the atmosphere, and provide updated costs. In this way, it is possible to update targets for controllable parameters in light of the best available economic information and evaluate the feasibility of microalgae technology for fuel production. Achieving the highest possible productivity with respect to the most expensive 

resource is essential in all competitive production practices, and for algal production, 

carbon dioxide is the vital resource. 
6.1.3 Algal Productivity and Products 

Section 5.0 presented the refer nce microalgae production cost of $393/t given a total f biomass productivity of 17 g/m /d consisting of 30 wt % lipids. The sensitivity analysis 

in Section 5.0 indicates that lipid costs are highly sensitivf to biomass yield and lipid 

content. Biomass productivity rates in excess of 40 g/m /d, achieved in highly controlled, outdoor experimental cultures, and lipid yields of over 60% in laboratory experiments indicate the potential for improving productivity or lipid content or, albeit more 

difficult, both. 

While these experimental performance levels are encouraging, they do not by themselves 

establish that needed productivity improvements can be achieved in large-scale facilities 

on a sustained basis. The improvements in lipid content or productivity have resulted 

from using different species or by maximizing one characteristic, often at the expense of 

other characteristics. To examine the effect of species improvement on biomass and 

lipid production costs, we examine alternative productivity yields where we assume that 

a species is developed especially to maximize the yield of a specific product. 

The algal production model (Hill 1984) used in this analysis includes as inputs culture 

depth, density and detention time, cell energy content (as a function of lipid, carbohydrate, protein, and ash content), and daily solar constant. Productivity and photosynthetic efficiency are then calculated. In effect, we are approximating the effect of 

manipulating carbon allocation in the algal cell, where lipids or carbohydrates are maximized. The purpose of this analysis is to evaluate the benefits attributable to reductions 

in biomass and lipid production costs from improvements in both algal productivity (or 

photosynthetic efficiency) and carbon allocation properties. 

For our first level evaluation of the effect of productivity and algal lipid content on production cost, we examine 9 different combinations of photosynthetic efficiency and lipid 

content levels. Table 6-4 is a complete list of input parameter assumptions, and 

Figure 6-5 displays the experimental design conducted to evaluate algal species 

improvements. Note in Table 6-4 that, for a given photosynthetic efficiency, increasing 

the percentage of lipids by altering the carbon allocation comes at the expense of carbohydrate production and vice versa. 

The data in Table 6-4 illustrate the impact of alternative levels of lipid content at the 

reference and two higher nominal levels of PSE, as illustrated in Figure 6-5. The PSE is 

not varied directly but is calculated through the equation 

Depth x Density 

PSE = x Z PC.ECi x DSC x 0.42 Detention Time ;=I 1 (6-1) 

where 

PC, = percentage content of product i, 

ECi = energy content of product i, 

I = lipid, carbohydrate, protein, ash and intermediate cell products, and 

DSC = daily solar constant (multiplied by 0.42 to convert PSE to a PAR basis). 

According to Eq. 6-1, for any given level of PSE we can vary the lipid content, adjusting 

the other cell components so that the fractions add to one; algal output will remain the 

same but PSE will vary as will production costs. So, a higher lipid content, offset by a 

lower carbohydrate, for fixed productivity, requires more energy inputs (C02, etc.). For 

variations about the reference case of 4.86 PSE, we simply adjust the relative cell product contents to evaluate different lipid contents (20% and 50%). 
Table 6-4. Parameter Assumptions for Evaluating Photosynthesis on Lipid Content 

Algal Proximate Chemistry 

Photosynthetic 

~fficienc~ Carbo- Protein Detention Algal Ash and Depth Density Time Algala 

(96) Lipid hydrate (%) (%) (m) (mg/L) 9 Intermediates Bi mass ffpids 

(%) (%) (dl (10 t/yr) (10 bbl/yr) 

a~ssumes 308.2 lb lipidslbbl lipids. 

b~eference. 
Facility size Module size 

ha ha 

Photosynthetic 

efficiency 

PAR % 

Lipid 

content 

0'0 

a Nominal PSE. Actual PSE level is system determined for each set of assumptions 

Figure 6-5. Experimental Design for Evaluation of Algal Species Improvements 

To evaluate the changes in lipid content at other nominal rates of PSE, e.g., 12% and 

18%, it is necessary to calibrate all elements of Eq. 6-1 such that the nominal PSE is 

achieved. This is achieved by setting the lipid content at the reference level of 30% and 

varying depth, density, and detention time until a PSE of the nominal rate (12% and 18%) 

results and then lipid content can be varied to 20% and 50%. A large number of combinations of depth, density, and detention time can be constructed such that PSE equals the 

nominal design levels of 12% and 18%. To get the desired PSEs, we first lowered detention time to bring PSE close to the desired level and then adjusted the density to get the 

nominal level (within 0.1%). Table 6-4 gives the input parameter values used to identify 

the level of density and detention time in Eq. 6-1 and used as the basis for altering the 

lipid content. Because we selected integer values for detention time and density levels 

were rounded to the nearest 5 mg/L, the resulting PSEs do not match exactly the nominal 

experimental design levels indicated in Figure 6-3. 

As indicated in Table 6-4, sensitivity of lipid content is examined at three different 

levels of algal biomass production corresponding to the three different levels of PSE. 

But, for any nominal level of PSE, varying the lipid content from the reference level of 

30% changes the gross lipid output and the PSE. Thus, costs of production change since 

variations in cell energy content change input requirements even though productivity 

remains constant. 

The costs of producing algal biomass and algal lipids for each case identified in Table 6-4 

are listed in Table 6-5 and the results are illustrated in Figure 6-6. Increasing the nominal PSE from the reference 4.86% to 12% and to 18%, with lipids held constant, reduces 
Table 6-5. Comparison of Reference Algal Feedstock Values and Attainable 

Levels for Productivity Variations 

Productivity Assumptions Comparative Values 

Photosynthetic Lipid Biomass Allowable Productivity 

Annual Major Fuel Feedstock Attainable Efficiency Content Prod tion 

(96) (96) "f Product Processin costa Production costb 

(10 t) ($/8 ($/d 

Ethanol 

PVO 

Ester fuel 

Methane 

Gasoline 

Ethanol 

PVO 

Ester fuel 

Methane 

Gasoline 

Ethanol 

PVO 

Ester fuel 

Methane 

Gasoline 

Ethanol 

PVO 

Ester fuel 

Methane 

Gasoline 

Ethanol 

PVO 

Ester fuel 

Methane 

Gasoline 

Ethanol 

PVO 

Ester fuel 

Me thane 

Gasoline 

Ethanol 

PVO 

Ester fuel 

Methane 

Gasoline 

Ethanol 

PVO 

Ester fuel 

Me thane 

Gasoline 

Ethanol 

PVO 

Ester fuel 

Methane 

Gasoline 

a~llowable feedstock cost is the maximum value of the feedstock to the indicated processing facility in the. 

production and sale of products and by-products at the prices dictated by the fuel costs goals. Feedstock 

allowable costs are calculated for the reference processing systems. 

b~roductivity attainable production cost is the cost of feedstock production for the indicated levels of 

photosynthetic efficiency, lipid content and biomass production with all other variables held at their 

reference level. Thus, the concept of attainability, as used here, relates only to improvements in algal 

productivity. 

the algal product cost from the reference $393/t to $224/t and $180/t, reaffirming the 

large impact of PSE (through improvements in depth and density, characteristics of the 

biological culture system) on algal production cost. Lipid costs drop considerably less, 

only about 20% from increases of 5% to 18% PSE, since the cost is calculated based on 

sale of the lipids only; although lipid output increases as PSE increases, carbon costs per 

unit of lipid are constant, and a large proportion of operating costs therefore increase 

proportionately. [Thus, while lipid production increases from 71 to 264 thousand barrels 

from 5% to 18% PSE (lipid content constant), an increase of 27196, per unit costs 

decrease only 5096, so the cost per barrel drops only about 50%.] Since we assume that 

variable costs increase in proportion to production, improved productivity spreads the 

fixed cost over a large base, but total costs do not drop proportionally with productivity 

improvement. 

Variations in lipid content around a given PSE level illustrate that while the cost per ton 

of algae varies only slightly (reflecting increased or decreased inputs to support the 

implicit energy content of the cells), the lipid cost varies dramatically as fixed costs are 

spread over proportionally more or less barrels of lipid produced. Reference costs of 

$103 per barrel lipids increase sharply (60%) if only 20% lipids are produced and decrease 

slightly at 50% lipid content. At higher PSEs, where algal productivity is higher, the 

variations about 30% are more symmetric, approximately 40%. Lipid costs, across both 

PSE and lipid content variations, range from $267/barrel to $50/barrel. While these data 

help show the impact of lipid content on the cost per barrel, evaluating the value of the 

algae produced requires examining refining processes and final product values, a topic 

addressed below. The cost comparison notwithstanding, algal productivity improvements 

are essential to realizing substantial decreases in algal production costs. 

6.1.4 Harvesting 

As discussed in Section 3.0, the reference harvesting system, consisting of microstrainers 

followed by centrifugation, contributes over 25% to the total capital investment of the 

mass culture facility. In Section 5.0, some variations in harvesting strategy were considered. Increasing solids removal efficiency would decrease operating costs by allowing 

the use of smaller equipment and thus less electric power to harvest the same amount of 

microalgae. Another type of first-stage harvester, the belt-filter, was also considered 

but was found to increase product cost despite this system's higher concentration ratio of 

70, compared with the microstrainer's concentration ratio of 10. 

There is much uncertainty concerning the technology and cost of harvesting algae. Solidliquid separation is an inexact science, especially when the important characteristics of 

the influent vary as widely as in microalgae species. Also, most of the available technical data were gained using fresh-water influent streams, and the effects of saline water 

on harvestability are difficult to estimate. 

Operating strategies can be suggested that might improve the harvesting situation. 

Increasing culture density will decrease the overall concentration factor required by the 

harvesting system. The more expensive stage (usually the second stage) could then be 

scaled back, resulting in capital and operating cost savings. Operating at the higher density allows shallower pond depth, which decreases head losses and reduces power 

requirements. These savings must be weighed against the decreased temperature stability at the shallower depth systems. More operating data will be required to fully evaluate these and other harvesting strategies to determine the best solution to the harvesting problem. Data are insufficient at this time to characterize completely all 

harvesting research improvements and their impact on microalgae production cost. 
6.2 Assessment of Fuel Product Options 

The first task in evaluating fuel processing options is to establish a reference case to 

characterize current processing technology status and to identify potential improvements. We use the basic process data and costs presented in Section 4.0 for each of the 

five fuel options to be evaluated. Then, we fix the value of the final fuel product, and all 

by-products, and derive the total revenues for each fuel product. The difference 

between revenues and processing costs gives an allowable feedstock processing cost--the 

amount a processing facility operator can afford to pay for the microalgae feedstock and 

produce the final product slate at the given prices. These allowable costs are compared 

to algal production costs for various levels of productivity to determine the feasibility of 

the various fuel product options. That is, algal production costs must be less than or 

equal to the allowable feedstock cost for the system to be judged cost effective by the 

fuel price standards established for 2010. 

6.2.1 Process Options 

The processing costs developed in Section 4.0 are sensitive to both the total amount of 

algal biomass processed and the lipid and carbohydrate content of the feedstock. Thus, 

in developing processing cost and subsequent allowable feedstock costs, we match 

assumptions used in developing feedstock production costs with those employed for processing cases: the quantity and content of the feedstock processed are matched to the 

corresponding productivity and feedstock composition assumptions for every combination 

of production and processing. In all cases, the processing plants process the output of the 

1000-ha facility. 

Figure 6-7 illustrates the cases evaluated for screening fuel processing options. Three 

levels of photosynthetic efficiency and three levels of lipid content are examined and 

correspond to the productivity linkages examined in Section 6.1. The five fuel options 

and processes correspond to those identified in Section 4.0. Thus, a total of 45 different 

allowable costs for fuel production are estimated and compared to the corresponding 

production cost, where allowable and production costs are matched according to the 

assumptions about photosynthetic efficiency, lipid content, and fuel type. 

Throughout the next section we compare productivity (photosynthetic efficiency and lipid 

content) cases (which we call productivity attainable production cost) to reference fuel 

processing values (allowable feedstock processing costs) to screen alternative fuels for 

the most promising options. In the sections below we will examine, for the most promising options, the implications of research improvements in fuel processing. 

6.2.2 Screening of Fuel Option Feasibility 

The allowable algal feedstock costs derived from the fuel product cost goals and the reference processing costs for each of the 45 fuel option cases are presented in Table 6-5 

along with the corresponding productivity attainable production cost. The largest gap 

between productivity attainable production cost and the corresponding allowable feedstock processing cost is at the 5% photosynthetic efficiency level--the current state of 

the art in algal production technology. For this low level of algal biomass productivity, 

even at 50% lipid content, production costs ($388/t) far exceed the value of the feedstock for all five fuel process options. It is apparent that current levels of productivity 

are inadequate for the production of cost-competit ive fuels. 

At 12% photosynthetic efficiency (nominal), the gap between allowable costs and production cost is less, but still too wide to indicate feasibility. At the most optimistic productivity levels-- 18% photosynthetic efficiency and 50% lipid content--production costs 

exceed fuel values by 5%-45%. This comparison involves reference processing cost and 

potential productivity costs only--we do not incorporate other product ion cost reduct ions 

identified in Section 6.1. Thus, we are testing whether microalgae productivity 

improvements (the so-called productivity attainable production cost) alone yield costcompetitive fuel production. The result is that productivity improvements alone do not, 

at least within the constraints imposed by the fuel cost goals, achieve the costcompetitiveness goal. 

Some preliminary conclusions about the various fuel options can be made from the data 

in Table 6-5. First, feedstocks of greater lipid content are always of higher value for 

fuels that utilize primarily the lipid content (PVO, ester fuel, and gasoline). This is to be 

expected because the structure of the lipids is conducive to producing higher-valued 

fuels, and the remaining components are conducive to lesser-value fuels (methane or fuel 

gas) or to by-products. The greater the fraction of lipids in the feedstock, the higher the 

value of the feedstock in fuel production. One exception is the production of ethanol, 

which utilizes only the carbohydrate fraction of the algal feedstock. In the best case 

examined in terms of carbohydrate content, the lipid content is 20% and the corresponding carbohydrate content is 49%. Since carbohydrate levels of 70% have been 

reported to be attainable, in the next section we will look at ethanol as a special case in 

which both production conditions and processing design are optimized for carbohydrate 

and ethanol production. 

Comparison of alternative fuel options for various levels of productivity indicates that 

methane processes consistently have lower feedstock values relative to other fuels. 

While all of the fuel processes have a methane process attached to convert wastes to 

fuel, the process that uses the feedstock exclusively for methane does not comp re well i? against the other fuel options. The relative price target of methane, $7.40/10 Btu, is 

about half that of the other fuels; therefore, despite high conversion rates, allowable 

feedstock costs are lower for methane-only processes. Moreover, examination of the 

other fuel processing options indicates that as the algal composition switches from carbohydrates and protein (methane-only substrate) to lipids, process economics improve. 

Thus, it appears that the high cost of producing algal feedstocks rules out production of 

methane as the primary fuel product. Feasible technology schemes must produce higher 

valued products from the lipid fraction to offset the high cost of producing algal feedstocks. 

Based on the data in Table 6-5, we can eliminate several of the experimental design 

cases from further evaluation. First, all cases with 20% lipid content exhibit discrepancies between production cost and feedstock values of a factor of 2 or more and all evidence indicates that higher lipid content is needed to achieve cost effectiveness. 

Second, PSE levels of 5 and 12 (nominal) appear to be too low to merit extensive evaluation, so we eliminate these cases in order to concentrate on higher levels of productivity. Third, cost-effective alcohol production depends on high carbohydrate content of 

the algae, so we can limit the evaluation of alcohol fuel feasibility cases to those of high 

carbohydrate content. And, as noted above, methane from algae appears to be not 

viable, so we eliminate combinations that involve methane processing as the primary 

product. 

The combined effect of screening the initial 45 options originally proposed leaves 4 cases 

for further analysis--the two initial lipid content levels (30% and 50%), an intermediate 

lipid level of 40% to allow for greater specification of the lipid response function, and a 
special case constructed to evaluate ethanol productions. These cases are listed in 

Table 6-6 where each case is associated with an alphanumeric indicator to indicate the 

fuel type (L = lipid-based fuels; A = alcohol) and the corresponding level of the primary 

chemical content (30, 40, and 50 for L cases and A 60 case referring to the algal lipid and 

carbohydrate content). With this small set of cases, we can concentrate on evaluating 

the value of improvements in the processing of microalgae and then integrate production 

and processing improvements to derive a set of technology targets that meet the operative definition of cost ef fectiveness--achieving fuel production at 20 10 cost goal levels. 

6.3 Attainability Targets for Microalgae Feedstock Production and Fuel Processing 

The objective of this section is to match sets of algal production parameters with sets of 

consistent processing values such that a cost-effective system is defined. Sensitivity 

evaluations of previous sections suggest the degree to which individual parameter variations improve costs and how clusters of parameters can be varied to represent likely situations. The large number of parameters, most of which are measured on a continuous 

scale, mean that the solution space is dense--a large number of parametric combinations 

may meet the criteria of equality of algal production and allowable feedstock cost. 

Choosing among these for technically feasible sets is a necessary condition for establishing attainability targets to guide technology research and development objectives and 

priorities. 

Solving for attainability configurations is confounded by the subjective nature of costeffect ive solutions--they invariably include expectat ions and judgments about future 

economic conditions and about the attainability of technology advancements. Me cannot 

hope to enumerate all solutions available since they number in the thousands. An alternative is to evoke standard operations research procedures to explore the simulated solution space based on an objective function. This approach is inappropriate in this evaluation due to the indeterministic nature of the models' data and functional relationships 

and because it is premature to specify the required objective function. 

Given the objective of this technology evaluation, to define the limits of and needs for 

technology improvement consistent with achievement of a cost-effective system, we 

must be content with providing a perspective on needed improvements wide enough to 

allow for constructing meaningful tradeoffs among options. This section approaches 

attainability through the use of a first approximation to an "attainable" technology configuration with subsequent variation of the subjective elements of that configuration, 

Table 6-6. Productivity and Fuel Type Cases 

for Algal Processing Evaluation 

Case Carbohydrate Lipid 

Content Content PSE 
enumeration of their impact, and identification of mitigating instruments--ways of overcoming constraints by improvement of additional aspects of the technology. This 

approach will not exhaust the full set of possible solutions but will provide a context 

from which technology analysts and scientists can construct a technology development 

program. 

6.3.1 An Attainable Production Technology Configuration 

An attainable production technology configuration is defined as a fully specified production scheme, with single values for all parameters, that meets a specific criteria for 

cost-effectiveness. This attainable technology configuration can then be varied 

according to important dimensions and by manipulation of various controllable elements. The attainable configuration is defined by specific levels of resource, facility 

design, and biological and financial parameters: To define an attainable feedstock production concept, we must select levels for all variables that define the system. Below 

we review the categories of model parameters and indicate our initial specification of 

attainability. 

Resource Parameters 

The large sensitivity of production cost to growing season length, illustrated in 

Section 5.0, is an obvious area for improvement. Achievement of a season longer than 

the reference 230 days per year rests on a number of improvements in the system. 

Greater temperature tolerance of culture algae is needed to endure seasonal temperature 

fluctuations. In the absence of covers to keep heat in during cool weather and ventilation to prevent elevated temperatures in hot weather, temperature tolerance improvements can be considered an innate characteristic of the algae themselves and one that 

must be developed as part of the species selection, development, and adaptation program. 

An accurate representation of temperature fluctuations and growing season effects 

requires a more disaggregate representation of time--the annual frame of the existing 

model can only reflect averages over all seasons and conditions. For the purposes h re, 5 an annual evaluation will have to suffice using average sunlight energy (5000 kcalJrn Id) 

and a growing season of 300 days, representative of desert Southwest production for 

10 months (February through November). 

Another attainability target is lower CO cost, which speaks more to the site selection 

and efficient carbon utilization, recyc P ing, and uptake. Extensive development of 

enhanced oil recovery operations in the southwest could leave the area with an extensive 

transportation network and developed supplies available at low cost. Alternatively, one 

could speculate that the pristine, undeveloped nature of prime algal production regions in 

the southwest persists into the 21st century and algal facilities must construct their own 

infrastructure for C02 and electricity, in which case costs could be higher than the reference case. 

Carbon dioxid cost for the attainability case is set at $3.00 per thousand standard cubic B feet ($~.lO/m ), a reduction of about 23% from the reference case. This reduction could 

come about as a result of lower acquisition costs or from reduced outgassing and recycle 

of carbon (Weissman 1985), or both. 
Facility Design Parameters 

Large reductions in algal production cost can only feasibly come about with improvements in the PSE through higher densities and shorter detention times. For the attainability case, we lower the detention time from the reference level of 7 days to 3 and 

raise the culture density from 800 to 1000 rng/L. Culture densities of 1000 mg/L can be 

achieved by good hydraulics that circulate the algae into and out of the light, thereby 

making fuller use of the available sunlight. Shorter detention times require more vigorous, faster-growing algae, a property that must be developed, and through species selection and development combined with system optimization of the culture environment. This attainability target is under the control of scientists, provided it is technically feasible. 

Financial Parameters 

Future economic conditions are difficult to forecast accurately and generally are beyond 

the control of technology development activities. These variables establish the conditions within which the technology operates. All financial parameters are held constant 

except the return on debt. The base rate of return on owner capital, 4.5% (real), is low 

for highly speculative investments. Although we do not expect a LOO0 hectare facility to 

be proposed, financed, and constructed until the technology feasibility has been established at a pilot scale and the near-term horizon for economic success is favorable, 

higher returns may be required for the first generation of facilities to offset inherent 

risks. In the spirit of constructing a "believable" set of attainability parameters, we 

raise the return on debt from 4.5% to 6.5%, which raises financing costs over the reference case that must be offset by physical parameter improvements. 

Biological Parameters 

Attainability, as defined by the parameters above, is further partitioned by the assumed 

cell lipid content into three cases, 130, L40, and L50 corresponding to 30%, 40%, and 

50% lipid content with corresponding adjustments in the carbohydrate and nitrogen content. In addition, salinity tolerance is raised to 80 g TDS/L (from the reference level of 

35). Since this characteristic was demonstrated above as having substantial value in 

overcoming resource constraints, this level of salinity tolerance appears to be a critical 

target for attainment of an economic system, although relaxing the requirement to 

50 g TDS/L would offer sufficient water related insurance for most situations. 

Attainabilitv Costs 

Full detail of the attainability algal production cases, relative to the reference case, is 

presented in Table 6-7, and resulting production cost and summary data for the cases are 

in Table 6-8. For all three levels of lipid content except for the ethanol case A60, the 

cost of production is below $200/t, ranging from $192/t to $199/t and lipid costs range 

from $93 to $54/bbl, the lowest cost associated with 50% lipids. The allowable cost of 

these feedstocks must be derived from evaluation of the processing options, the topic of 

the next section. 
Table 6-7. Parameter Levels for Attainability Microalgae Production 

Cases: Changes from Reference Case 

Parameter 

- - 

Attainability casea 

Units Reference 

L30 L40 L50 A60 

Resources 

Growing season 

C02 cost 

Facility design 

Detention time 

Depth 

Density 

Biological parameters 

Lipid content 

Carbohydrate content 

Nitrogen content 

Salinity tolerance 

PSE 

Financial parameters 

Return of debt 

day3 

S/m 

days 

in. 

mg/L 

% 

% 

% 

g TDS/L 

% 

% 

a~ases L30, L40, and L50 correspond to assumed algal lipid contents of 30%, 4O%, and 

50%, respectively, and Case A60 corresponds to 60% carbohydrate content; the implied 

PSEs for these cases are identified in Table 6-6. 

6.3.2 Fuel Processing 

Processing costs can be reduced, and allowable feedstock cost increased, by increased 

process conversion efficiency and by the use of larger scale processing facilities. The 

conversion efficiencies used in the reference cases were derived from preliminary evaluation of the feedstock composition and utilization of available conversion technology. 

Directed process research and development may be able to significantly raise lipid conversion efficiencies for PVO, ester fuel, and gasoline technologies. These improvements 

increase the proportion of the feedstock converted to high-value, primary fuels while 

reducing lower-valued by-product production. For PVO and ester fuel processes, the fuel 

yield from 50% lipid feedstocks is raised from 50% to 75% and for gasoline from 80% to 

90%. These improvements assume that lipid-rich feedstocks are mostly storage lipids, 

which are highly conducive to processing. 

Scale economies can be expected if larger feedstreams are available. The fuel outputs 

from the reference cases, based on the feedstock produced on a 1000-ha facility, range 

from about 1.4 million gallons for PVO, ester fuel, and gasoline to 600,000 gallons for 

ethanol. By commercial standards, these are very small processing plants that do not 

take advantage of scale economies that are usually available in chemical and fuel processing. Thus, we raise the scale of the processing plants to approximately ten times 

that of the reference case, again evoking an assumption in which several colocated algal 

production facilities are linked to a large processing facility. 
Table 6-8. Summary Output for Attainability Microalgae Production Cases 

Attainability casea 

Parameter Units L30 L40 L50 A60 

System algal yield 103tlyr 116 116 116 116 

Productivity g/m2/d 50 50 50 50 

Specific growth rate d 0.37 0.37 0.37 0.37 

PSE % PAR 14.13 15.17 16.18 10.35 

C02 demand lo9 SC~/Y 3,24 3.24 3.24 3.24 

Water demand 6 3 10 TI /g 118 118 118 118 

Capital investment lo6 19845 41.08 41 .08 4 1 .08 4 1.08 

$/hab 48 48 48 48 

Operating cost lo6 1984$ 17.32 16.94 16.59 16.39 

Algal production cost 1984$Jt 199 195 192 192 

Lipid product ion cost 1984$/bbl 93 63 54 1344 

a~ases L30, L40, and L50 correspond to assumed algal lipid contents of 30%, 40%, and 

50%, respectively. 

b~or effective culture area (86% of facility size). 

Reference case and attainability values for microalgae processing cases L30, L40, and 

L50 and A60 are listed in Table 6-9. The improved ethanol efficiency comes from the 

assumption that, in addition to the carbohydrate fraction which is hydrolyzed to glucose, 

the remaining carbohydrates are hydrolyzed to xylose, which also may be convertible to 

ethanol, Recall also that we constructed a special case, not used in the reference evaluation, where 60% of the algal biomass is carbohydrate at the highest rate of photosynthetic efficiency. This best attainable ethanol case is constructed to determine the maximum potential for production of ethanol from algal biomass. 

Mass and energy balances for the attainability fuel processing concepts are presented in 

Table 6-10, and fuel and by-product outputs are listed in Table 6-11. By increasing processing efficiencies and raising the scale of the processing plants (to serve a total of 

8,000-10,000 ha of microalgae production), major product yields more closely represent 

commercial design criteria for product ion level--annual output exceeds 80 million gailons 

of primary fuel for PVO, ester fuel, and gasoline. 

Estimated plant capital costs are illustrated in Figure 6-8 and operatin costs in 

Figure 6-9. Total fuel processing capital costs (Table 6-12) range from about ! 70 million 

for PVO to over $160 million for ester fuel processing plants. The complexity of the 

transesterif ication (ester fuel) process, which requires multistage operation and interstep 

solvent recovery and catalyst neutralization, results in estimated capital costs higher 

than the other two processes. We caution the reader that fuel process configurations, 

and therefore costs, are based on preliminary data of feedstock composition; considerable evaluation of process kinetics is needed before more detailed d a t a c a n be developed. 
Table 6-9. Reference Case and Attainability Values for Lipid Processing Plant Scale and Conversion Efficiency 

Processing Plant 

Attain- Major Fuel Processing Efficiency Scale 

ability Product 

Case (96) Basis for Reference Attainable Re erence l At ainable 

Conversion Process (96) (96) El (10 gallyr) (10 gallyr) 

Ethanol 

PVO 

Ester fuel 

Gasoline 

Ethanol 

PVO 

Ester fuel 

Gasoline 

Ethanol 

PVO 

Ester fuel 

Gasoline 

Carbohydrate 

Lipid 

Lip id 

Lip id 

Carbohydrate 

Lipid 

Lipid 

Lipid 

Carbohydrate 

Lip id 

Lip id 

Lipid 

no cases run 

no cases run 

no cases run 

no cases run 

no cases run 

no cases run 

no cases run 

no cases run 

-- - -- - -- 

Notes: Efficiency and processing scale attainability values are not varied for different levels of photosynthetic 

efficiency since the major variation in scale refers to lipid content, and efficiencies are independent of 

product stream size. 
Table 6-10. Summary of Attainability Product Yields 

Ester 

Fuel PVO Gasoline Ethanol 

Major algal component 

Fraction of total algae 

Fraction available for 

Lipid Lip id 

0.50 0.50 

Lipid 

0.50 

Carbohydrate 

0.60 

0.75 0.75 

1 .OO 0.98 

0.38 0.35 

107.5 gal 104.8 gal 

conversion 

Efficiency of utilization 

Production (t/t algae) 

Common unit/t algae 

0.24 

78.9 gal 

0.2 1 

76.7 gal 

Product energy content 

(GJ/t) 

Main product energy content 

(GJ/t algae) 

Total energy available 

(GJ/t algae) 

Recovery efficiency 

(major product) - -- 

a~~ basis 

b90% hydrolysis efficiency x 95% fermentation efficiency 

Table 6- 1 1. Production Summary for Attainability Microalgae Refinery Options 

Major Products 

Process Product (unit) 

PVO Ester Fuel Gasoline Ethanol 

6 Production ( 10 gal/yd 107.5 

Other Product? 

Methane (10 J/yr) 4.7 

6 3 C02 (10 m /yr) 85 

Nitrogen (lo3 t/yr) 

as NH3 16.9 

Glycerol (lo3 t/yr) -- 

-- 

a~~ butane, 103,000 Btu/gal. 

blncludes 1.0 x 1015 J/yr from catalytic conversion and 3.1 x 1015 Jlyr from 

anaerobic digest ion. 
Glycerol 

18' 1 1 (by-product) 

Primary fuel 7 -" t 

0 l 1 1 

PVO 3ter tuel Gasol~ne Ethanol 

Figure 6-8. Capital Costs for Attainability Fuel Processing Plants 

Operating costs for the attainability fuel processes, illustrated in Figures 6-9 and 6-10 

are representative of commercial chemical and fuel processes. By achieving all the 

highest possible conversion rates, feedstock cost becomes the largest single fraction of 

total operating cost. The success of the fuels-from-microalgae concept depends on the 

development of processes that can meet the standards imposed by the fuel processing 

industry, including capital-intensive, large-scale plant designs operating at high ef f iciency. 

6.3.3 Analysis of Feedstock Production and Fuel Processing Requirements 

Now that we have screened algal production options to the most promising cases and 

have quantified the impacts of improvements in the processing of algal feedstock for 

fuels, the two stages of production can be integrated to derive conditions consistent with 

our measure of cost effectiveness. As before, we fix the value of all fuel products and 

processing coproducts at 2010 levels and compare the value of the feedstocks implied by 

a particular processing concept with the cost of producing the feedstock, seeking equality of these values. 

Table 6-13 presents the integrated attainability algal production costs (the cases L30, 

L40, L50, and A60 with improvements in other parameters as defined in Table 6-8) and 

scale, process, and combined improvements in processing described in Table 6-9. 

Table 6- 12. Summary of Attainability Capital and Operating Costs 

for Fuel Processing Options 

Process 

Cost Category 

PVO Ester Fuel Gasoline E than01 

Capital costs ($1 06) 

Main process unit 

Glycerol by-product unit 

Methane by-product unit 

Total 

Operating costs (2 1 061yr) 

Raw materials 

Electr c ower 

Water k 

Steam 

Labor, maintenance, taxes 

Depreciation 

Return on investment 

Total (gross) 

6 Credits from product salesC ($10 lyr) 

Carbon dioxide 

Water 

Nitrogen 

Methane 

LPG 

Diesel 

Glycerol 

Subtotal (credits) 

Net operating c st 

Fuel production 8 

Main product cost ($/gal) 

a~nclude algae feedstock ($1 9Z/t). 

b~rocess and cooling water. 

C~y-product prices as in Table 4-4. 

6 d~ain product, 10 galJyr. 

Appropriate comparisons, seeking equality of value, are between columns 4, 5, 6 (scale, 

process, and both processing improvements) and column 7, the production cost associated 

with the feedstock assumed for each processing configuration and primary fuel type 

(Table 6- 13). 

Our evaluation of the data in Table 6-13 will be by fuel type, beginning with ethanol 

(case A60). None of the individual processing improvements (columns 3 and 4) achieves 

IP@, *= / 

Table 6-13. Comparison of Integrated Allowable Costs and Production Costs for Microalgae-Derived Fuels 

Fuel and Production Case Allowable Algal Feedstock Processing Cost ($It) 

Attainable b Scale Attainable Scale and Integrated Primary Producgon Reference Only~ Process Impro e- (Y Attainable Process Attainability Fuel Case 

(1) Case (3) ments Only 1mprovementse Production Cost (4) (2) (5) (6) (7) 

Ethanol 

PVO 

PVO 

C PVO 

N 

-4 

Ester 

Ester 

Ester 

Gasoline 

Gasoline 

Gasoline 

a~ttainable production cases L30, L40, L50, and A60 are defined in Table 6-6. 

b~eference processing throughput is 116,000 t/yr. 

C~cale processing throughput is 1,000,000 t/yr (9 plants). 

d~rocessing throughput is the reference level (1 16,000 t/yr), but yields are higher. 

e~rocessing throughput is 1,000,000 t/yr. 
equality of algal feedstock production cost and processing value, and even assuming all 

possible processing improvements (column 6), the production cost exceeds the processing 

value (column 7) by a factor of almost three. The high cost of producing carbohydrates 

by algal culture appears to preclude the cost-effective production of alcohol, given the 

2010 alcohol cost targets established for this analysis. This conclusion can be seen from 

another perspective by comparing the ethanol cost goal for 2010 with the ethanol cost 

implied by processing algal feedstock costing $7 1, the best case evaluated. Table 6-14 

shows that ethanol prices would have to exceed the 2010 cost goal by about $1.55 for this 

technology to be cost effective. Since current expectations for fuel prices equivalent to 

$2.75 ethanol are beyond 2020, it appears that ethanol from microalgae is a technology 

with, at best, a distant opportunity and therefore deserving a low priority for research 

and development. 

The prospects for PVO are not as dismal as those for ethanol, but this fuel option also 

fails our cost-effectiveness test. Even with the most favorable realization of production 

(LSO) and processing improvements (both scale and processing improvements), the production cost exceeds the processing value by 14%, implying that technology feasibility 

would occur sometime after 2010 and, as we see below, this process is inferior to other 

options and therefore may never become attractive as a commercial technology. Again, 

the value of research directed at this option appears low. 

Ester fuel suggests the possibility of becoming cost effective but only if all possible 

improvements are realized. As Table 6- 14 indicates, realization of these conditions 

results in production costs of $0.10 below the cost goal level and thus our cost-effective 

criterium is exceeded. The same is true for gasoline from microalgae, again, in t h e c a s e 

where all specified production and processing improvements are achieved. 

The result of our extensive screening and integrated production and process analysis suggests that two fuels show promise of cost effectiveness by the first decade of the 21st 

century. However, ester fuel and gasoline can achieve this distinction only if the full set 

of technology improvements specified above are achieved. Specifically, algal production 

must achieve a PSE of slightly above 16% with 50% lipid content and production must 

find conditions tha support exten ive carbon dioxide and water resources with costs at 3 or below $O.lO/m3 and $O.l3/rn , respectively, and the other reference parameter 

assumptions must be met. On the processing side, substantial process efficiency 

Table 6-14. Derived Microalgae Fuel Production 

Costs for Integrated Attainability 

Concept 

Fuel 

Product 

Cost 

Goals 

($/gal) 

Attainable 

Fuel Cost 

($/gal) 

PVO 1.75 2.40 

Ester fuel 1.75 1.65 

Gasoline 1.75 1.70 

Ethanol 1.20 
improvements must be achieved and industry scale economies realized to achieve the 

needed cost level, $192/t. Tables 6-15 through 6-17 list the important variations from 

reference assumptions and represent the technology development targets derived from 

the integrated technology evaluation. 

The scale requirement for cost effectiveness appears to describe a chicken-egg dilemma; 

scale economies are needed for commercial viability but unless first applications are at 

the required scale (10,000 hectares), they will not be cost effective. This apparent paradox should not be judged as reason to rule out development of this technology since our 

cost-effectiveness criterium requires that the first year cost meet the exact cost goal 

for 2010, thus ignoring that later year prices exceed the cost goal and thus losses in the 

early years could be compensated by later year revenues above costs. Taking the price 

forecast for 2010 for gasoline, for example, gives a first year price of $1.75/g, but the 

process improvement only case implies a production cost of about $1.90/g, with an 

implied loss of $0.15/g. However, as gasoline prices rise from 2010, this loss is eroded 

and eventually positive margins accrue to first offset the loss and later contribute to 

return to investment. 

We adopted a stringent first year cost criteria to focus attention on R&D requirements. 

However, given the near equality, for gasoline and ester fuel, of production cost and 

value, it would be remiss not to take into account outyear forecasts in determining technology feasibility. We leave subsequent studies of market penetration rates and the 

economics of early adoption to another forum. 

Table 6-15. Parameter Values for Attainability Microalgae Production: 

Variances from Reference Case Values 

- - - -- - 

parametera Reference Value Attainability 

3 Carbon dioxide cost ($/m ) 

Nitrogen cost ($It) 

Phosphorus cost ($It) 

Potassium cost ($It) 

Electricity cost ($/kwh) 

Productivity (g/m2/d) 

Algal composition 

Lipids (%) 

Carbohydrate (%) 

Protein (%) 

Potassium (g/g DW) 

Nutrient outga sin S Carbon (g/m 19) 

Nitrogen (g/m Id) 

Source water composition (g TDS/L) 

Algal salinity tolerance (g TDS/L) 

Carbon dioxide transportation (km) 

a~arameters not listed are the same in the reference and attainability cases (see 

Table 3-1 for a full listing of reference case parameters). 
Table 6-16. Input and Output Material Flows for the 

Integrated Attainability Microalgae Production 

Concept (1 000-ha facility) 

Material Flows Quantity 

Inputs to Microalgae Production 

Resources 

Carbon digxi9 (lo6 m3/yr) 

Water (10 m /yr) 

Nitrogen (tlyr) 

Phosphorus (t/yr) 

Potassium (t/yr) 

Labor (t/yr) 

Energy 

Electricgy (lf6 kWh/yr) 

Sunlight (10 kcal) 

Outputs 

Microalgae (t/ yr) 

Lipids (t/yr) 

Water disposal (146 m3iyr) 

Brine disposal (1 0 t/yd - 

a~otal sunlight to the facility adjusted for effective culture area (86%) and 

downtime (10%); e.g., net energy input to production is listed. 

6.4 Sensitivity Analysis 

The attainability cases we constructed above are representative of the many combinations and permutations of the model and technology parameters that meet the criteria of 

an algal production price of $192/t. In this section, we examine variations of the attainability case to see how our concept of attainability withstands alternative assumptions 

about the assumed economic and resource conditions. 

6.4.1 Sensitivity of the Attainability Production Cost to Constraints 

Cases in this section represent variations in parameters from their level in the first attainability case specified in the last section. The objective of this exercise is to lend a 

perspective on the stability of "attainability" under alternative assumptions about the 

state of the economic environment--factors largely beyond the control of the technology 

development program. These constraints, increases in implicit cost of production, are 

evaluated in terms of increases in controllable parameters, mainly productivity, needed 

to mitigate these effects. 

Sensitivity cases for increased cost are described in Table 6-18 and cover a variety of 

factors subject to considerable uncertainty. The levels of parameter changes are intended to give a perspective on the sensitivity of attainability over a range of values that 

correspond to progressively more stringent constraints, but the particular values are 

arbitrary--not referenced to a particular site or set of circumstances. 
Table 6-17. Summary of Integrated Attainability Production 

Facility Cost Contributions (1984 $) 

Cost Category S/t Percentage Percentage 

of Category of Total Cost 

Capital Cost 

Culture system 

Harvester system 

Engineering fees 

Contingency 

Land 

Total capital cost 

Operating Costs 

Labor and overhead 

Utilities 

Nutrients 

Water 

Other operating 

Total operating cost 

Total feedstock cost 

The main factor, under the control of the development program, for mitigating these 

impacts is algal productivity (PSE). For each case where an individual economic parameter is varied, we sequentially raise the PSE until the production cost is decreased back 

to the attainability level of $192/t. Changes in the PSE are induced via increases in the 

density from the attainability target of 1000, up to 1400 mg/L, where the PSE exceeds 

23%. Further cost decreases, where needed, are achieved by increasing the level of 

other biological targets (nutrient outgassing, growing season, and the evaporation rate) 

judged to be more difficult to achieve but which do offer opportunity for improvement, if 

required. 

Table 6-19 and Figure 6-11 present the results of solving for the mitigating level of PSE 

for each sensitivity case. As defined in Table 6-18, there are 10 primary sensitivity 

cases (A.1-A.6, B.1-B.2, C.1-C.2), each with two or three subdivisions of the levels of 

increase or decrease (growing season days are decreased to represent more stringent 

environmental conditions) for a total of 28 cases. Taking all first level increases as 

approximately equal increments of increases in the constraint and the same for second 

and third level increases allows for a subjective evaluation of the need to raise the PSE 

target above the 16% attainability level.* 

Of the 10 first level increases (the first level change for all cases, column 4 in 

Table 6-19), two require a PSE increase to less than 16.49%, two more of these increases 

*This is a subjective assessment of relative probability across diverse events and is not 

derived from any formal probabilistic analysis. 
Table 6- 1 8. Sensitivity Cases for Attainable Microalgae Production costa: 

Uncertainties that Increase Costs 

Levels of Change b 

Parameter Units Attaina- Ist Case Parameter 2nd 3r d bility TY pe 

A. I Resource Evaporation rate m/d 0.0035 0.007 0.01 0.02 

A. 2 Resource Land cost $/ha 1245 2500 5000 10000 

A.3 Resource Water cost $/m5 0.067 0.13 0.17 0.20 

A.4 Resource Carbon dioxide cost 0.10 0.12 0.14 - - 

A.5 Resource Source water salinity g TDSIL 25 3 5 50 . -- 

A.6 Resource Growing season dl yr 300 285 270 263 

8.1 Facility design BOS costC % 0 10 15 2 0 

B.2 Facility design Liner cost $1 ha 5000 7500 10000 12500 

C. 1 Financial Maintenance cost % 7 10 15 20 

C. 2 Financial Equity capital 961~01~ 3016.5 10017 10018 L00/9 

a~ases represent imposition of additional constraints, through higher levels of indicated variables, on the 

attainability microalgae production concept. 

b~evels correspond to a second level of indexing of cases, e.g.; A. 1.1 (1st level), A. 1.2 (2nd level), A. 1.3 

(3rd level). 

'A balance-of-system contingency defined as a percentage of capital cost. 

d~~ = capital investment. 

e~oth the percent debt capital and the return on investment (RoI) on debt capital are varied. 

are mitigated by a PSE less than 17%, and all but one (A.4.1, a 16% increase in CO cost) 

require a PSE of less than 17.5%. Except for the three most constraining cases f~~4.3, 

D.1.3, and D.2.3), all second and third level increases are mitigated at PSEs below 

18.5%. First we offer some remarks about these cases and then look more closely at the 

effect of increased C02 costs. 

The most restrictive cases are those requiring increases above 18.50% PSE. Extreme 

cases that are still below that threshold include evaporation rates up to 0.02 m/d (twice 

the high level used above); land costs eight times above the attainability level; water 3 costs up to $0.20/m (also the high level); a growing season as low as 265 days; liner costs 

two and a half times as high as the attainability level; and 100% equity financing at the 

high ROI of 8%. These represent a wide range of uncertainties and demonstrate that 

raising the target PSE to 18% would result in a feasible technology configuration for 

most situations. 

Cases requiring PSE in excess of 18.5% to keep the algal production cost at $192/t are 

full equity financing at 9% ROI, maintenance costs of 20% of t e capital investment 3 cost, and carbon dioxide costs in excess of $4/thermal scf ($0.14/m ). Rates of return of 

9% (real) and 100% equity financing are conditions usually associated with risky investments, and these extreme conditions can be avoided by careful technology 
Table 6-19. Mitigating PSE Levels for Attainability Production Case sensitivitiesa 

Equilibrating Level of PSE 

caseC Parameter 

Type 

Parameter Levels of Change 

(1) (2) (3) 

1 st 2nd 3rd 

Resource 

Resource 

Resource 

Resource 

Resource 

Resource 

Facility design 

Facility design 

Financial 

Financial 

Evaporation rate 

Land cost 

Water cost 

Carbon dioxide cost 

Source water salinity 

Growing season 

BOS cost 

Liner cost 

Maintenance cost 

Equity capital return 

a~ased on attainability algal production case L50. 

b~he level of PSE required to maintain algal production cost at $192/t, the level of attainability case L50, given the parameter changes specified for the various cases. 

'cases are defined in Table 6-18. 

development including staged demonstrations of individual and integrated components 

that confirm technological and economic feasibility. In the same vein, well-designed 

components should keep maintenance costs closer to the typical level of 796-1096 of capital cost. The impact of extreme maintenance costs should alert technology developers to 

stress equipment reliability at all stages of development. We conclude that two of the 

three extreme cases (requiring a PSE in excess of 18.5%) can be viewed as signaling 

design requirements and not indicative of rigid constraints that threaten feasibility. The 

extreme carbon dioxide case, however, presents a more formidable constraint. 

The cos of C02 was increased in two i rements from the attainability assumption of 3 T $0.10/m to first $0.12 and then $0.14/m , increases of 20% and 40%, respectively. The 

20% increase was mitigated by increasing the PSE to 18.5% and decreasing outgassing 

losses to zero, but the 40% increase in C02 cost requires an equilibrating PSE of about 

23%. Thus, we looked at other improvements to reach the $192 price at a PSE below 

20%. At almost 20% PSE, the production cost still exceeded the attainability requirement of $192/t by over $20/t. We first raised the lipid content to 60% (lowering 

cost to $206/t), then increased the growing season to 320 days ($200/t), and finally 

reached $192/t by lowering the evaporation rate to about 0.0015 m/d (half the attainability rate assumption). Clearly, algal production costs are extremely sensitive to C02 

cost at the attainability configuration. 

Carbon is a vital input to the production of any petroleum substitute and probably the 

most constraining factor for any alternative fuel producing technology. That this is the 

case for microalgae technology should not be viewed as a reason to drop the concept, but 

rather as an important factor in evaluating the ultimate success of this, versus other 

alternative fuel technologies. The United States has vast .C02 reserves that may be 

available at costs compatible with their use in microalgae production. In terms of setting technology performance targets, protecting against high C02 costs suggests maximizing carbon utilization through developing efficient species and careful design of C02 

input systems. The larger question of the cost and availability of C02 is a resource question that must be resolved through further evaluation of options and the evolution of 

CO -based enhanced oil recovery, a factor outside the realm of influence to microalgae 

tec 2 nology development efforts. 

6.4.2 Special Attainability Cases with Low Production Costs 

The previous section examined how resource and economic constraints work to raise the 

technology performance targets. Equally important are situations where more ideal 

parameter conditions are encountered since these situations represent the opportunity 

for a cost-effective system at less stringent levels of PSE and other biological parameters. For example, if conditions exist where a system is cost effective at, say a PSE of 

only 12%, then the opportunity exists to gain valuable commercial experience that will 

speed technology development. The goals chosen to guide program activities should be 

stringent enough to ensure that meeting these goals fulfills the program objective. However, lower interim targets serve to set serial milestones for measuring progress and, if 

they correspond to special conditions of opportunity, their early adoption will complement the development effort. Thus, we reverse the process of the previous section and 

look at how more favorable assumptions about certain parameters lower the required 

PSE. 

Sensitivity cases for cost decrements are identified in Table 6-20 and the mitigating PSE 

levels are illustrated in Figure 6-12. Those factors that had small PSE effects previously, in the case of less favorable levels, also have small impacts in the other direction. Lowering land or water costs by 50% only decreases production costs by one to 

three dollars, not enough to offer opportunities for early commercialization, at least for 

fuel pro uction. The same is true of low salinity source water and C02 costs of only 

$0.08/m 9 . However, if we bundle together a set of favorable conditions, we do find algal 

productions costs significantly below the attainability figure of $1 W/t. 

3 For exayple, assuming costs for land ($500/ha), water ($0.03/m ), carbon dioxide 

($0.06/m ), and favorable environmental conditions such as a long groying season 

(320 days) with higher solar radiation (daily solar constant of 6000 kcal/m ), the algal 

production cost for case L50 biological target is $155/t, a 20% cost reduction that, if 

processed in a small refinery, might yield a gasoline or ester fuel at under $1.50/gal. The 

fuel price projections presented earlier suggest that hydrocarbon fuel prices approach 

$1.50/gal about the turn of the century. Thus, early adoption, at least in limited circumstances, is possible if the L50 case biological targets are achieved. But, for the 
Table 6-20. Sensitivity Cases for Attainable Macroalgae Production Cost: 

Uncertainties that Decrease Costs 

Levels of Change b 

Case Parameter 

Type 

Parameter Attaina- lst Units bility 2nd 3rd 

Dl Resource Carbon dioxide cost $/m3 0.10 0.08 0.06 -- 

D2 Conversion Fuel valueC $/gal 1.75 1.93 2.10 2.28 

D3 Resource Growing season d/yr 300 320 340 -- 

D4 Biological Lipid content % 50 55 60 - - 

D5 Resource Water cost $/m3 0.067 0.045 0.034 -- 

a~ases represent imposition of additional constraints, through higher levels of indicated 

variables, on the attainability microalgae product ion concept. 

b~evels correspond to a second level of indexing of cases; e.g., D 1 1 ( 1 st level), D 1 2 (2nd 

level), Dl3 (3rd level). 

'~asoline or diesel fuel product cost goal for 2010. 

production of fuel, it appears that the PSE target of at least 16% with 50% algal lipid 

content is a necessary condition for the technology to become economically viable in the 

first decade of the 21st century. 

6.4.3 Lipid Sensitivity Cases 

The above sensitivity cases are defined in terms of the PSE increment or decrement 

required to mitigate changes in parameter assumptions. However, it is of interest to 

researchers to examine the tradeoff between PSE and lipid content. To evaluate the 

option between developing productivity (PSE) or lipid content, it is necessary to characterize the trade of functions and then examine the technical feasibility versus the technical requirements. 

To assist in the evaluation of alternative research thrusts, we constructed a sensitivity 

analysis, based on case LSO, where PSE and lipid content we revaried against the constraint that the final fuel production cost did not exceed the 2010 cost goal target. Since 

changing the lipid content changes the value of the raw algal product to the fuel processing step, solution of the tradeoff function requires resolving all steps of the model. 

That is, as we vary the lipid content from 50% level of case L50, we must solve for a PSE a 

that holds the final fuel cost constant given the changes in the relative chemical product 

distribution of the algae. 

A single sensitivity case, involving attainability case L50 but for gasoline as the final 

product, serves to illustrate the tradeoff between PSE and lipid content. For each variation of lipid content, from the 50% attainability, all changes induced in fuel processing 

chemistry and cost calculated and a new gasoline production cost is estimated at the 

16.17% PSE level. Then, the PSE is varied until gasoline production costs are returned to 

the cost goal level and the lipid content PSE pair is plotted. 
The results of this sensitivity analysis, illustrated in Figure 6-13, indicate a relative 

steep tradeoff function between lipid content and PSE. Of special interest is the section 

of the curve between 50% and 60% lipid content as it indicates that the PSE requirement 

could be dropped from the L50 attainability level of 16.17% to about 13% if 60% lipids 

can be achieved. This suggests that careful attention be given to opportunities to 

increase lipid content, rather that PSE, be seriously evaluated before committing to a 

specific research strategy. New techniques to induce increased lipid content may yield 

high returns if PSE reductions can be realized according to Figure 6-13. 

The shape of the response or tradeoff curve between lipid content and PSE will vary 

according to the specific set of parameter assumptions. For fuel products less dependent 

(than gasoline) on the lipid content for cost effectiveness will demonstrate a less elastic 

response, and changing the basic input assumptions will shift the curve across the 

response surface. Specific cases require respecification of the model inputs and are 

beyond the scope of the analysis. However, we have demonstrated the general concept, 

and specific evaluations can be conducted to fit the questions of interest in this important area of fuels from microalgae evaluation. 

6.5 Summary 

Although numerous options for the production of fuels from microalgae have been proposed, our analysis indicates that only two qualify for extensive development--gasoline 

and ester fuel. In developing the comparisons that support this conclusion, we have identified the major areas of microalgae production and processing that require extensive 

development. Technology success requires developing and testing processes that fully 

utilize the polar and nonpolar lipids produced by microalgae. Process designs used in 

these analyses were derived from fragmented, preliminary laboratory data. These results 

must be substantiated and integrated processes proposed, tested, and refined to be able 

to evaluate the commercial feasibility from microalgae. 

The production of algal feedstocks for processing to gasoline or ester fuel requires algae 

of high productivity and high lipid content that efficiently utilize saline waters. Species 

screening and development suggest that algae can achieve required standards taken individually, but algae that can meet the integrated requirements still elude researchers. 

Effective development of fuels from microalgae technology requires that R&D be 

directed toward meeting the integrated standards set out in the analysis. As technology 

analysts, it is inappropriate for us to dicate how the R&D effort should proceed to meet 

these standards. We end our role by noting that alternative approaches to meeting the 

feasibility targets have been identified, and it is now the task of program managers and 

scientists to choose the appropriate approach to assure the greatest likelihood of 

realizing a commercially viable technology. 


7.0 CONCLUSIONS AND RECOMMENDATIONS 

This technology assessment suggests that gasoline and diesel fuels could be produced 

from mass cultured microalgae at prices that will be competitive with conventional 

fuels. Aggressive research is needed to fulfill the performance requirements defined by 

the analysis, but the required improvements are within the bounds of attainability and 

have been closely approached under controlled conditions. Based on the achievement of 

these research goals, liquid fuels that are potential direct substitutes for conventional 

hydrocarbon fuels can be produced from microalgae for $1.60-$2.00/gal. Conclusions 

from the analysis and recommendations for future research fit into three categories: fuel 

products, resource and environmental considerations, and the biological and engineering 

aspects of fuels from microalgae. 

7.1 Fuel Products from Microalgal Feedstocks 

Analysis of a number of fuel conversion options for microalgal biomass has demonstrated 

that the promise of microlgae for fuel production is best realized through the utilization 

of conversion processes based on cellular lipids. The ability to produce lipids that can 

constitute 30% or more of the total biomass is a distinguishing characteristic of microalgae and makes them uniquely attractive candidates for conversion to liquid fuels. Conversion of this feedstock to other fuels that fail to take advantage of these unique characteristics, such as fermentation to ethanol or anaerobic digestion to methane, is much 

less promising. 

While microalgal lipids represent the premium energy product, the energy trapped in the 

other biomass constituents can also be utilized. The cell residue after lipid extraction 

can be anaerobically digested for the production of methane and carbon dioxide. Analysis has shown that conversion of residues in this manner can contribute to improved process economics. 

The two most promising fuel conversion options explored here were transesterification to 

produce fuels similar to diesel fuels, and catalytic conversion to produce gasoline. However, since only limited performance data are available for these conversion processes, 

the characterization and optimization of these processes with microalgal lipids must be a 

subject for continued research. Other unique opportunities for the utilization of microalgal lipids should also be explored. 

Research Recommendations. The immediate research priority in microalgal fuel product 

assessment is a detailed characterization of the fuel properties of microalgal lipids. 

Attention must be directed toward (I) the identification of techniques by which these 

lipids can be extracted on a large scale and (2) a detailed description of the characteristics of these lipids as they relate to their suitability as feedstocks for fuel conversion 

processes. Ultimately, conversion processes specifically tailored to the characteristics 

of microalgal lipids must be developed, either through the optimization of existing techniques or through the development of innovative conversion technologies. Such research 

activities will require the production of algal biomass samples on a scale suitable for 

extraction and fuels characterization. Samples from a number of promising species 

should be included, since there are strong indications that the character kt ics of lipids 

vary widely between taxa. 
7.2 Resources and Environmental Considerations 

One of the most attractive features of the microalgal fuels concept is its use of 

resources for which there are few competing demands. The use of arid lands and saline 

waters that are not suitable for conventional agriculture will permit the development of 

microalgae mass culture without detrimental effects on other important plant crops. 

Adequate suitable land is available in the southwestern United States to provide for a 

microalgal technology that could make significant contributions to national energy needs. 

The availability of saline water will be an important factor in determining the ultimate 

scale of technology based on this resource. Due to high evaporation rates, water 

demands for uncovered cultures in this region will be extremely high. While saline 

aquifers are found throughout the Southwest, the total volume these aquifers can supply 

on a sustained basis has not been determined. Since very little information is available 

on the saline water resources of the desert Southwest, the quantification of this resource 

is a high priority activity. 

In addition, we recommend that the concept of microalgal culture for fuels be expanded 

to include the use of coastal seawater and irrigation runoff water. The scope of the concept should include the consideration of areas where available land with high insolation 

rates can be identified in coastal regions. The use of irrigation water may also provide 

an opportunity for an expanded contribution of microalgal fuels to the nation's energy 

needs. These runoff waters contain large amounts of dissolved inorganics that threaten 

to pollute clean waters. If these waters can be diverted to microalgal facilities where 

they contribute to the production of valuable fuels, then microalgal fuel production 

might be able to contribute another valuable by-product to the economy. 

The acquisition of sufficient quantities of carbon dioxide should not impose constraints 

on the ultimate scale of the rnicroalgal fuels technology. Existing and proposed power 

plants will produce carbon dioxide in excess of the quantity required for microalgal production. Competing demands from enhanced oil recovery (EOR) are not anticipated, 

since most oil fields in the area are expected to be depleted by the year 2000, which is 

the expected date of emergence of an extensive microalgal mass culture technology for 

liquid fuel production. If methods were developed for the recovery of carbon dioxide 

previously injected into oil wells, these large reservoirs could supply additional abundant 

quantities of low-cost carbon dioxide. 

While the supply of carbon dioxide will be more than adequate to support the microalgal 

technology, this raw material is the largest single contributor to the cost of liquid fuels 

derived from microalgae. Improvements in the techniques for carbon dioxide recovery 

from flue gas, or the identification of sources of inexpensive C02 such as EOR residuals, 

would contribute significant additional improvements to the cost competitiveness of 

rnicroalgal fuels. 

No major regional climatic effects are anticipated from the introduction of large 

uncovered areas of water in this otherwise arid region. The technology will produce two 

major waste streams--concentrated brines from the blowdown stream and digester sludge 

from the anaerobic digesters (or similar biomass residues). These waste streams can present either an opportunity for utilization at an economic advantage, or introduce a cost 

for disposal. Neither possibility has been considered in the economic analysis. 

Research Recommendations. Both the microalgal production model and the advice of 

experts in the field have emphasized the significance of saline groundwater as a potential 
limiting resource for the development of a large-scale microalgal technology in the 

desert Southwest. To assess the potential scale of this technology, it is critical that 

accurate estimates of the sustainable yields of the saline aquifers in the area be determined. Factors of interest are total aquifer volume, recharge rates, and potential 

changes in water composition with prolonged pumping. In addition, the effects of extensive pumping of saline aquifers on potable water aquifers in the same region will have to 

be assessed. Beyond an assessment of the saline groundwater resource, we also recommend an assessment of the magnitude of the possible contribution of coastal seawater 

and agricultural runoff to a microalgal fuels technology. 

7.3 B iolo~ical and Engineering Asaects of Feedstock Production 

The major issues to be resolved in mass culture technology are biological. In order for 

this technology to become cost competitive, the biological productivity of these systems 

must be improved. The production analysis indicates that photosynthetic efficiencies on 

the order of 16% must be attained, and that about 50% of the biomass produced must be 

in the form of lipids. If higher lipid contents were achieved, lower photosynthetic efficiency would be acceptable, and vice versa. 

Lipid content of 50% or more can be obtained routinely in the laboratory, but program 

activities to date have only achieved photosynthetic efficiencies on the order of 

10%- 1 3%. Nevertheless, these photosynthetic efficiencies represent significant 

improvements over those previously obtained, and can be attributed to improvements in 

both the selection of species for cultivation and in culture management strategy. With 

appropriate research in these areas, continued progress can be expected. It is critical, 

however, that both biomass and lipid productivity requirements be achieved simultaneously at sustained rates. That is, the allocation of carbon between lipids and other cell 

constituents must be shifted in favor of the lipids without excessive loss of photosynthet ic efficiency. 

In addition to displaying improved productivity, species must demonstrate environmental 

tolerance characteristics that make them suitable for outdoor culture in arid regions. 

The productivity analysis has identified species salinity tolerance as a particularly significant aspect of environmental tolerance, since the operation of cultures at high salinities is necessary to control water requirements through a minimization of water 

consumption for blowdown. Additional reduction in water and nutrient use requires 

mechanisms for control of evaporation and nutrient outgassing. Conventional plastic 

covers are too expensive, so new techniques are required to improve resource 

productivity. 

The basic engineering needs for microalgal culture have been identified and form the 

basis of a significant portion of the production analysis. Considerable additional 

engineering research and development will be required, but such activities cannot be 

undertaken until the basic requirements of suitable microalgal species can be established. The harvesting of microalgae, however, is an engineering issue that will require 

continued attention. The aspects of microalgae that affect their harvestability have 

been characterized adequately to permit the continued exploration of different harvesting options. In addit ion, an understanding of the perf orrnance of various harvesting 

processes will help to determine desirable species characteristics for harvestability. 
Research Recommendations. The identification or develo~ment of microaka1 strains 

that will meet the performance criteria of high productivity; high lipid content; and wide 

ranges of environmental tolerance is the single most critical research requirement for 

the-economic viability of a microalgal fueG technology. The ability to meet these 

requirements must first be established in closely controlled experimental cultures, then 

confirmed under conditions that more closely approximate outdoor mass culture conditions. The ultimate success of the fuels-from-microalgae concept is critically dependent 

on t h e r a t e and degree of species improvement. 

Continued investigation of harvesting techniques is recommended, as is an emphasis on a 

characterization of the performance of different species in various harvesting systems. 

The identification of species characteristics that determine their harvestability is a critical input to the selection and development of microalgal species for use in outdoor production systems: a strong interaction between species development and harvester 

engineering activities will be critical to the development of species that meet perf ormance criteria and yet can be harvested economically. Finally, it is recommended that 

mechanisms for control of evaporation and nutrient outgassing be explored to evaluate 

the potential for minimizing the cost of these resources. 
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Radmer, R., and 0. Kok, 1977, "Photosynthesis: Limited Yields, Unlimited Dreams," Bioscience, Vol. 27, p. 599. 

Raphael Katzen Associates, 1980, "Grain Motor Fuel Technical and Economic Assessment 

Study," in Large and Small Scale Ethyl Alcohol Manufacturing Processes from Agricultural Raw Materials, 3. K. Paul, ed., Park Ridge, NJ: Noyes Data Corp. 

Reynolds A., 1982, Projected Costs of Electricity from Nuclear and Coal-fired Power 

Plants, DOEIEIA-035612, U. S. Energy Information Administration. 

Ryan, T. W., 111, L. G. Dodge, and T. J. Callahan, 1984, The Effects of Vegetable Oil 

Properties on Injection and Combustion in two Different Diesel Engines," J. Am. Oil 

Chem. Soc.,Vol. 61 (lo), p. 1610. 

Saga, N., M. Bolne-Fuller, and A. Gibor, 1986, "Protoplasts from Seaweeds: Production 

and Fusion," in Algal Biomass Technologies, W. Barclay and R. P. McIntosh, eds., 

Beihefte rur Nova Hedwigia, Vol. 83, pp. 37-43. 

Samson Re, and A. Leduy, 1983, "Influence of Mechanical and Thermochemical Pretreatments on Anaerobic Digestion of Spirulina maxima Algal Biomass," Biotechnology Letters, Vol. 5 (lo), p. 671. 

Shelef, G., and C. J. Soeder, eds., 1980, Algae Biomass, Production and Use, 

Amsterdam: ElsevierINor th-Holland Biomedical Press, 852 pp. 

Sherbet, G. V., 1978, The Biophysical Characterization of the Cell Surface, NY: Academic Press, 298 pp. 

Shif r in, M. 5. 1 980, Phytoplankton Lipids: Environmental Influences on Production and 

Possible Commercial Applications, Ph. D. Thesis, Massachusetts Institute of Technology, Boston, MA. 
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NY: McGraw-Hill. 

Stumm, W., and J. J. Morgan, 1970, Aquatic Chemistry, NY: W iley-Interscience. 

Tahir, A. B., H. M. Lapp, and L. C. Buchanan, 1982, "Sunflower Oil as a Fuel for Compression Ignition Engines," in Proceedings of the International Conference on Plant and 

i Vegetable Oils as Fuels, American Society of Agricultural Engineers, p. 82. 

Technical Insights, Inc., 1980, Biomass Process Handbook, N3: Fort Lee. 

Terry, K. L., 1982, "Nitrate Uptake and Assimilation in Thalassiosira weissflogii and 

Phaeodactylum Tricornutum: Interactions with Photosynthesis and with the Uptake of 

Other Ions," Mar. Biol., Vol. 69, p. 21. 

Terry, K. L., and L. P. Raymond, 1985, "System Design for the Autotrophic Production of 

Microalgae," Enzyme and Microbial Technology, Vol. 7, pp. 474-487. 

Terry, K. L., J. Hirata, and E. A. Laws, 1985, "Light-Limited, Nitrogen-Limited, and 

Phosphorus-Limited Growth of Phaeodactylum triconutum Bohlin strain TFX-1: 

Chemical Composition, Carbon Partitioning, and the Die1 Periodicity of Physiological 

Processes," J. Exp. Mar. Biol. Ecol., Vol. 86, No. 1, pp. 85-100. 

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of Microalgae with Emphasis on Lipids," Final Report for SERI Subcontract No. XK-2- 

02149-1 (portions also published in Screening for Lipid Yielding Microalgae, Final Subcontract Report SERIISTR-231-2207), Golden, CO: Solar Energy Research Institute. 

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Cultivation, NY: John Wiley and Sons, pp. 367-466. 

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McCandlish, and R. Clark, 1982, "Resource Assessment for Microalgal/Emergent 

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Deserts," Biomass, Vol. 1, p. 145. 

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in the United States, FE-2024-38, Bellaire, TX: Gulf Universities Research 

Consortium. 
APPENDIX A 

This appendix presents the Microalgae Production Model in Fortran 5. The program 

listing is provided for those readers interested in determining specific values as they 

were presented in this report. The program listing is provided as a reference and should 

not be considered as a executable program listing as it is presented. Comments and 

suggestions for improvement to this model are encouraged by the authors of this report. 

Should the public be interested in running this program, copies of this program are 

available on PC floppy disks by calling or writing: 

Donna A. Johnson 

Aquatic Species Program 

Solar Energy Research Institute 

1617 Cole Blvd. 

Golden, CO 80401 
Page 1 

DLine#l 7 

DATA HCONC/10.,10.,150.,70./ 

DATA UHARV/.2,.1,1.2,.88/ 

DATA HCOST/123200.,33000.,625000,184800./ 

DATA HEXP/.8,.8,.8,.87/ 

DATA NITWT/lm21/CARWT/3.67/POTWT/1.20/PHOWT/2.29/ 

DATA VISC/.00131/DENS/1011.0/ 

OPEN(5,FILE= TMICIN.DATr) 

OPEN(6,PILE='TMICOUT.DATf,STATUS= NEW') 

OPEN(8,PILE= TECOIN.DATf) 

C 
C VARIABLE DEFINITIONS 

C 

C EPFHARV = HARVESTER SOLIDS REMOVAL EFFICIENCY 

c EH = SOLIDS CONC. AFTER HARVEST (SYSTEM REQUIREMENT) 

C ATNET = THEORETICAL, LIGHT LIMITED NET PRODUCTION YIELD, 

MT/YR 

C ANET = SYSTEM DEPENDANT NET YIELD, MT/YR 

C FACSIZ = SIZE OF FACILITY, HECTARES 

C MODULE = NUMBER OF MODULES 

Page 2 

FORTRAN77 V3.20 02/84 

MODSIZE = SIZE OF MODULES, HA 

EFFCUL = EF FECTIVE CULTURE AREA, % 

EFPOP = CULTURE AREA DOWN FOR MAINTENANCE, % 

TPI =TOTAL PLANT INVESTMENT, $ 

CIPOND = CAPITAL INVESTMENT FOR POND, $ 

CIHARV = CAPITAL INVESTMENT FOR HARVESTOR, $ 

ENGFEE = ENGINEERING AND FEE COSTS, $ 

CONTG = CONTINGENCY PEES, $ 

CI = TOTAL DEPRECIABLE INVESTMENT, $ 

YB,BASE YEAR FOR CONSTANT DOLLARS 

YT, YEAR FOR INVESTMENT OUTLAY 

YP, YEAR FOR COST INFORMATION 

YCO, YEAR OF FIRST COMMERCIAL OPERATION 

N, SYSTEM OPERATING LIFETIME (BOOK LIFE) 

M, TAX LIFE FOR DEPRECIATION,YEARS 

B1, ANNUAL 'OTHER TAXESf, AS A FRACTION OF CI 

B2, ANNUAL INSURANCE PREMIUMS, AS A FRACTION OF CI 

T, EFf ECTIVE INCOME TAX RATE 

ALPHA, INVESTEMENT TAX CREDIT 

DV, RATIO OF DEBT TO TOTAL CAPITALIZATION 

CV, RATIO Of? COMMON STOCK TO TOTAL CAPITALIZATION 

PV, RATIO OF PREFERRED STOCK TO TOTAL CAPITALIZATION 

KD, ANNUAL RATE OF RETURN ON DEBT 

KC, ANNUAL RATE OF RETURN ON COMMON STOCK 

KP, ANNUAL RATE OF RETURN ON PREFERRED STOCK 

G, GENERAL RATE OF INFLATION 

GC, ESCALATION RATE FOR CAPITAL COSTS 

GO, ESCALATION RATE FOR OPERATING COSTS 

GM, ESCALATION RATE FOR MAINTANCE COSTS 

K, COST OF CAPITAL (OPTIONAL) 

CRP,CAPITAL RECOVERY EACTOR(OPTIONAL) 

FCR, FIXED CHARGE RATE (OPTIONAL) 

LIPCON, FRACTIONAL LIPID CONTENT OF ALGAE 

CARBCON, FRACTIONAL CARBOHYDRATE CONY ENT OF ALGAE 

ICON = FRACTIONAL NON-SPECIFIED CONTENT OF ALGAE 

BBLS, NET LIPID OIL PRODUCED, BBLS/YR 

NUT = NUTRIENTS, $ 

DL = DIRECT LABOR, $ 

OH = OVERHEAD, $ 

HCONC(TH) = CONCENTRATION RATIO FOR HARVESTER TYPE IH 

HEXP(TH) = EXPONENT FOR CAPITAL COST OF HARVESTOR 

EPFPUMP = MIXING PUMP EFFICIENCY 

EFEDRIVE = MIXING DRIVE EFFICIENCY 
DTIME = DETENTION (OR DOUBLING) TIME, DAYS 

DEPTH = TROUGH DEPTH, METERS 

X = MODULE WIDTH, NORMALIZED 

Y = MODULE LENGTH, NORMALIZED 

LENGTH = MODULE LENGTH, METERS 

NORM = NORMALIZING FACTOR FOR MODULE DIMENSIONS 

CHAN = DESIRED CHANNEL WIDTH (METERS) 

MIXVEL = MIXING VELOCITY, METERSISEC 

KWHCOST = COST OF ENERGY, $/KWH 

VOL = MODULE VOLUME, M3 

FLRATE = REQUIREDFLOW RATE INTO HARVESTER, CUBIC METERSIMIN 

QHARV = MASS PLOW RATE INTO HARVESTER, GPMIMODULE 

QRECYC = MASS FLOW RATE OF RECYCLED MEDIA, GPMIMODULE 

QMAKEUP = MASS FLOW RATE OF MAKEUP WATER, GPM/MODULE 

ERATE = EVAPORATION RATE, CM/YR 

QEVAP = EVAPORATION LOSSES, GPMIMODULE 

Page 3 

DLine#l 7 Microsof t 

FORTRAN77 V3.20 02/84 

C UPHARV = PUMPING POWER REQUIREMENT INTO HARVESTER, HP 

C UPRECYC = PUMPING POWER REQUIREMENT FOR RECYCLED MEDIA, HP 

C UPMUP = PUMPING POWER REQUIREMENT FOR MAKE UP WATER, HP 

C UPTOT = TOTAL PUMPING POWER REQUIREMENT, HP 

C UPKWH = UTILITY REQUIREMENT FOR TOTAL PUMPING, KWH/YR 

C HL = HEAD LOSS FOR MIXING, METERS 

C UMIX = MIXING POWER REQUIREMENT, HP 

C UMKWH = UTILITY REQUIREMENT FOR MIXING,KWH/YR 

C UHARV(IH) = HARVESTOR TYPE IH ENERGY REQUIREMENT, KWH/M3 

C UHKWH = UTILITY REQUIREMENT FOR HARVESTING, KWH/YR 

C UTOT = TOTAL ENERGY REQUIREMENT FOR PUMPING, MIXING, AND 

HARVESTING, 

C UT = COST OF ENERGY FOR PUMPING,MIXING, AND HARVESTING, $/YR 

C MNT = OPERATION AND MAINTENENCE, $ 

C OP = TOTAL VARIABLE COSTS, $ 

C RH = HYDRAULIC DIAMETER, METERS 

C RE = REYNOLDS NUMBER 

C RF = RFACTOR 

C QF =INTERIM 

C OT = INTERIM 

C DCI = DEPRECIABLE CAPITAL INVESTMENT 

C NDCI = NON DEPRECIABLE CAPITAL INVESTMENT 

C CP = CAPACITY FACTOR, DAYS/YR 

C DSC = DAILY SOLAR CONSTANT, KCAL/SQ M-DAY 

C EFPPHOT = PHOTOSYNTHETIC EFFICIENCY, % 

C H = IH, INDEX FOR HARVESTOR TYPE (1 THRU 3) 

C ALC INDEX FOR SECOND HARVESTOR SYSTEM 

C NITCOST, PRICE FOR NITROGEN SOURCE, NH3, $/MT 

C CARCOST, PRICE FOR CARBON SOURCE, C02, $/MT 

C POTCOST, PRICE FOR POTASSIUM SOURCE, K02, $/MT 

C PHOCOST, PRICE FOR PHOSPHORUS SOURCE, CAP205, $/MT 

C NUTC =TOTAL COST FOR CARBON, $/YR 

C NUTN =TOTAL COST FOR NITROGEN, $/YR 

C NUTK =TOTAL COST FOR POTASSIUM, $/Y R 
C NUTP =TOTAL COST FOR PHOSPHORUS, $/YR 

c LAND, COST OF LAND, $/HECTARE 

C LCOST, TOTAL LAND COST, $ 

C NITWT, MOLECULAR/ATOMIC WEIGHT FOR NITROGEN SOURCE 

C CARWT, MOLECULAR/ATOMIC WEIGHT FOR CARBON SOURCE 

C POTWT, MOLECULAR/ATONIIC WEIGHT FOR POTASSIUM SOURCE 

C PHOWT, MOLECULAR/ATOMIC WEIGHT FOR PHOSPHORUS SOURCE 

C NITCOMP, FRACTIONAL NITROGEN CONTENT OF ALGAE YIELD 

C CARCOMP, FRACTIONAL CARBON CONTENT OF ALGAE YIELD 

C POTCOMP, FRACTIONAL POTASSIUM CONTENT OF ALGAE YIELD 

C PHOCOMP, FRACTIONAL PHOSPHORUS CONTENT OF ALGAE YIELD 

C DENSITY, DENSITY OF FLOW INTO HARVESTOR, MG/LITER 

C DENS, CULTURE SPECIFIC GRAVITY, KG/M3 

C VISC, CULTURE VISCOSITY, KG/SEC-METER 

C SYSEFF, SYSTEM DEPENDANT EPPECIENCY, % 

C 

DO 10 f=1,86 

READ(5,lOO) AA(I),ATExT(I) 

10 CONTINUE 

100 F0RMAT(E15.8,4X,A60) 

W RITE(6,200) (AA(I),ATEXT(I),I=1,86) 

200 FORMAT(lH1,'INPUT LISTING',//,(E~~.~,~X,A~O)) 

IH=H 

IALC=ALC 

C 

C CALCULATE SYSTEM DEPENDANT YIELDS 

ANET=(DEPTH/DTIME)*DENSITY *CF*.O~*PACSIZ*EFFCUL*(~-EPFOP) 

CARBP = ANET*CARBCON 

BBLS = ANET*LIPCON*7.153 

PRCON = 1-ACON-ICON-LIPCON-CARBCON 

PROP = ANET*PRCON 

C 

C CALCULATE SYSTEM PRODUCTIVITY AND TEST FOR PMAX,UMAX 

C 

P=(DEPTH DTIME) * DENSITY 

u = (P/(DENSITY * EPFHARV)) * (UDEPTH) 

IF ( U .GT. UMAX) U = 0.0 

C 

C 

C 

C CALCULATE SYSTEM-DEPENDANT PHOTOSYNTHETIC EFFICIENCY 

C 

SYSEFF = (P*(LIPCON*LEC+CARBCON*CEC+ICON*IEC+PEC*PRCON)) 

&/(DSC * .42) 

IF ( SYSEFF .GT. EFPPHOT ) SYSEPF=O.O 

W RITE(6,lOG) ANET,P, U,BBLS,SY SEEF 

106 FORMAT(//, 

&lOX,'ANET, SYSTEM DEPENDANT NET YIELD, MT/YR =', 

dcE15.8,/,1DX,'P,PRODUCTIVITY, G/M2-D =',E15.8,/,10X, 

&W,SPECIEIC G.ROWTH RATE, 1/DAY =1,E15.8,/,10X, 

B'BBLS, SYSTEM-DEPENDANT NET YIELD, BBLS/Y R = ',E15.8,/, 

hlOX,'SYSTEM DEPENDANT PHOTOSYNTHETIC EFFICIENCY = ',E15.8) 

C 
C CALCULATE CHANNEL DIMENSIONS,EFFECTIVE VOLUME AND FLOW RATES 

C 

MODSIZE= (EPFCUL*FACSIZ)/MODULE 

WIDTH = SQRT((MODSIZE*lOOOO)/Y) 

LENGTH = (MODSiZE*lOOOO)/WIDTH 

W RITE(6,lOl) WIDTH,LENGTH,MODULE,MODSIZE 

101 FORMAT(1OX,fWIDTH,METERS=t,F10.2,5X,tLENGTH,METERS=f,F10.2,/ 

&lOX,WUMBER OF MODULES = ',E8.2,5X,'MODWLE SLZE,HA = ',F8.2) 

C 

C CALCULATE WATER BALANCE FOR FACILITY (M3/MIN)(.000694=1/HR*MIN) 

C 

QMAKEUP = ((SALTOL*ERATE)/(SALTOL-SALZN))*EACSIZ* 

&10000*EFFCUL*(l-EPFOP)*.OOO6944 

WATLOSS =((SALIN*ERATE)/(SALTOL-SALIN))*FACSIZ* 

&10000*EFFCUL*(l-EF FOP)*.OOO6944 

WATPRO =((P*FACSIZ*lOOOO*EPFCUL*(l-EFl?OP)*0.0006944)- 

&(W ATLOSS*DENSITY *(I-EFEHARV)))/((EH*10000)- 

&DENSITY *(I-EFPHARV)) 

WATDISP = WATLOSS - WATPRO 

QHARV = ((P/(DENSITY *EFFHARV)) 

&*PACSfZ*10000*EFFCUL*(l-EFFOP)*.OOO6944)- 

&(w ATDISP*((J.-EFFHARV)/EPFHARV)) 

QRECY = QHARV - WATLOSS 

IF ( WATDISP .LT. 0.0) QHARV = 0.0 

C 

C 

C 

C CALCULATE HARVEST SYSTEM REQUIREMENTS 

C 

C 

NHARV = INT(QHARV/13.3+0.5) 

QHARVB =((QHARV-(WATDISP*(~-EFFHARV)))*DENSITY)/(HCONC(IH)) 

Page 5 

FORTRAN77 V3.20 02/84 

IF ( QHARVB .LT. WATPRO ) QHARV = 0.0 

HARVR = (HCONC(IALC)*(DENSITY *HCONC(IH)))/~OOOO 

NHARVB = INT(QHARVB/3.8+0.5) 

IF (NHARVB .LT. 1.0) NHARVB = 1.0 

C 

C 

C CALCULATE HYDRAULIC DIAMETER, REYNOLDS NUMBER, HEAD LOSS 

C 

RH = (CHAN*DEPTH)/ (CHAN + (Z*DEPTH)) 

RE =(DENS * MIXVEL * RH*Z)/ VISC 

HL =(MIxVEL**~)*(ROUGH**~)*(LENGTH*INT(WIDTH/C HAN+O.5)) 

& /(RH**1.3333) 

C 

C CALCULATE PUMPING HORSEPOWER REQUIREMENTS(MS/M) 

C 

UPHARVB = 1.04 * QHARVB 

UPHARV = .21 * QHARV 

UPWATD = 1.55 * WATDISP 

UPRECYC = 1.55 * QRECY 
UPMUP = 1.55 * QMAKEUP 

UPTOT =UPHARV + UPRECYC + UPMUP + UPHARVB + UPWATD 

C 

C CONVERT TO ENERGY REQUIREMENT (.7457 KW/HP) 

C 

UPKWH = CF*24.*.7457 * UPTOT 

C 

C CALCULATE MIXZNG POWER REQUIREMENTS 

C 

UMIX =(HL * DEPTH * CHAN * MIXVEL*3600 

&* DENS )/(367000*EEFPUMP * EFEDRIV) 

C 

C CONVERT TO ENERGY REQUIREMENT (.7457KW/HP) 

C 

UMKWH = 24. * UMIX * CCP * MODULE 

C 

C CALCULATE ENERGY REQUIREMENT FOR HARVESTING 

C 

UHKWH =(QHARV*60.* UHARV(IH) * CP)+(QHARVB*60 

&*UHARV(IALC)*CE) 

C 

C CALCULATE TOTAL ENERGY REQUIREMENT FOR PUMPING, MIXING, AND 

C HARVESTING 

C 

UTOT=UPKWH + UMKWH + UHKWH 

C 

C CALCULATE THE UTILITY COST, $/YR 

C 

UT = KWHCOST * UTOT 

C 

C CALCULATE NUTRIENT MASS BALANCE AND SYSTEM REQUIREMENTS 

C 

NCON = (PRCON * RPROT)/NPROT 

IF ( NCON .LE. QN ) NCON = 0.0 

NUTN =((((P*NCON)+NAG)*EACSIZ*(I-EPPOP)*EPPCU((NAR* 

&W ATLOSS)-(QMAKEUP*~~ W))*1440))*CF/1000000* NITCOST * NITWT 

NUTCAR = ((((P*CCON)+CAG)*PACSIZ*(~-EFFOP)*EPFCUL*~OOOO)+(((CAR* 

&W ATLOSS)-($MAKEUP*CW))*~~~~))*CE/~~~~~~~*CARWT 

NUTK = (((P*KCON)*EACSIZ*(~-EFPOP)*EFFCUL*~OOOO)+(((KAR* 

&WATLOSS)-(QMAKEUP*KW))*~~~O))*CF/~OOOO~~ * POTCOST*POTWT 

Page 6 

FORTRAN77 V3.20 02/84 

NUTP = (((P*PCON)*FACSIZ*(~-E~FOP)*EPFCUL*~OOOO)+(((PAR* 

&WATLOSS)-(QMAKEU~*PW))*1440))*CE'/1000000 * PHOCOST*PHOWT 

IF (NUTN .LT. 0.0 ) NUTN = 0.0 

IF (NUTK .LT. 0.0 ) NUTK = 0.0 

IF (NUTP .LT. 0.0 ) NUTP = 0.0 

C 

C 

C 

C ESTIMATE C02 OFESITE COSTS FOR DELIVERED C02 

C 17.3 MSCF=lMT 

C 
CARB = NUTCAR * 17.3 *(l/CF)*.001 

CARCOST = ((4.683 *(CARB**(-0.228))) 

&+(4.096*(CAR~**(-0.58)))*(COKM/160)) 

NUTC = CARCOST * NUTCAR * 17.3 

C 

C ESTIMATE NUTRIENT AND OTHER OPERATING COSTS 

C 

C 

NUT = NUTN+NUTP+NUTC+NUTK 

DL = 1345. * PACSIZ 

OH = .75 * DL 

TOTWAT = WATCOST * QMAKEUP * 60 * 24 * CF 

C 

C 

W RITE(~,~~~)HCONC(IH),HCONC(IALC),UHARV(IH), 

&UHARV(~ALC),HCOST(IH),HCOST(IALC),NHARV,NHARVB, 

&HARVR 

208 PORMAT(//,SX,'HARVESTOR SYSTEM(S) PARAMETERS1 

&,/,lOX,fHCONC,CONC.RATIO FOR HARVESTOR', 

&lX,1=T,2X,F8.2,4X,P8.2,/,10X, 

dcfUHARV,ENERGY REQUIREMENTf,1X,t=',2X,P5.2,4X, 

&F5.2,1X,'K WH/M3',/,1OX, 

&'HCOST,NOMALIZED COST FOR HARVESTOR' 

&,lX,'=',2X,F10.2,4X,F10.2,1X,'$1,/,10X, 

&'NUMBER OF FIRST HARVESTORS REQUIRED',lX, 

&P10.2,/,10X, 

&'NUMBER OF SECOND HARVESTORS REQUIREDr,4X, 

&Pl0.2,/,10X,'DESIGN SOLIDS CONC.%',2X9P10.2,/) 

C 

C 

C CALCULATE CAPITAL INVESTMENT FOR THE POND 

C 

EARPON=8452*FACSIZ 

LINE = 5000*FACSIZ*EFPCUL 

PBERM= 2*(WIDTH + LENGTH)*3.02*MODULE 

SBERM=((WIDTH/CHAN)-~)*LENGTH*~~*MODULE*~~~~ 

AROAD=(LENGTH + WIDTH)*I.II*MODULE 

PONEX=(PBERM+SBERM+AROAD)*3.27 

MIXCC=2500*PACSIZ*EFFCUL 

WANUT=~~~~~~*((FACSIZ*EFPCUL)/~~~)**O~~ 

&+21400*(MODSIZE/40m5)**0.7 * (MODULE-1) 

C02CC=541300*((FACSIZ*EEFCUL)/810)**0.7 

&+4900*(MODSIZE/40.5)**Om7*(MODULE-1) 

CIBUfL=(EARPON+MIXCC+WANUT+CO2CC+PONEX)*O.O2 

CIELE=~EARPON+WANUT+MIXCC+CO~CC+CIB~IL+P~NE~~*~.~ 

CIPOND = CIELE+CIBUIL+WANUT+C02CC+PONEX+MIXCC+LZNE 

C 

C CALCULATE CAPITAL INVESTMENT FOR THE HARVESTOR 

Page 7 

FORTRAN77 V3.20 02/84 

C CAPACITY = (350J?T2/140FT2)**.796 = 2.07 

CIHARV = HCOST(IH)*2.07*NHARV 
&~.~)**wExP(IALc)))* NHARVB 

CALCULATE THE TOTAL DEPRECIABLE INVESTMENT 

CI = CfPOND + CIHARV + CIHARB 

CALCULATE MAINTENANCE AND TOTAL OPERATING COSTS 

MNT = -07 * CCI 

OPX = -05 * CI 

OP = NUT + UT + DL + OH +TOTWAT + OPX 

CALCULATE ENGINEERING, CONTINGENCIES, AND LAND COSTS 

ENGFEE = (.15*C1)+(.08 "PONDEX) 

CONTG = .~*(cI +ENGFEE) 

LCOST = LAND * FACSIZ 

CALCULATE DEPRECIABLE INVESTMENT 

DCI = CI 

NDCI= ENGFEE + CONTG + LCOST + EARPON 

WRITE(6,201) LINE,MD(CC,PONEX,WANUT,COPCC,CIBUIL,CIELE, 

&CIPOND,CIHARV,CIHARB,DCI,EARPON,ENGFEE,CONTG,LCOST,NDCI 

&/,lOX,'CAPITAL COST OF 

&/,lOX,'CAPITAL COST OF 

&/,lOX,lCAPITAL COST OF 

&/,lOX,'CAPITAL COST OF 

&/,lOX,'CAPTIAL COST OF 

&/,lOX,'CAPTIAL COST OF 

&/,lOX,'CAPITAL COST OF 

&/,lOX,'CAPITAL COST OF 

&/,lOX,'CAPITAL COST OF 

MIXING SYSTEM $',E15.8, 

CULTURE SYSTEM $ ',E15.8, 

WATER/NUTRIENT SYSTEM, $',E15.8, 

CARBON DIOXIDE SYSTEM, $',E15.8, 

BUILDINGS, $',E15.8, 

ELECTRICAL SYSTEM, $',E15.8, 

TOTAL CULTURE SYSTEM, $',E15.8, 

1STHARVESTOR SYSTEM, $',E15.8, 

2ND HARVESTOR SYSTEM, $',E15.8, . - 

&/,lOX,rTOTAL DEPRECIABLE INVESTMENT, $ =',E15.8, 

&//,lOX,'COST OF SITE PREP-AND SURVEY, $',E15.8, 

&/,lOX,'ENGINEERING FEE, $ =',E15.8, 

&/,lOX,fCONTIGENCIES, $ =',E15.8, 

&/, lOX,'LAND COST, $ =',E15.8, 

&/,lOX,'TOTAL NON-DEPRECIABLE INVESTMENT, $ =',E15.8) 

w R1~E(6,204) RE,QHARV,UPHARV,QHARVB,UPHARVB,QRECY, 

&UPRECYC,WATDZSP,UPWATD,QMAKEUP,UPMUP,UPTOT,UPKWH, 

&UMIX,UMK WH,UHK WH,UTOT 

204 PORMAT(/,lOX,'RE,MUDULE REYNOLDS NUMBER = 'J15.8, 

&/,lOX,'QHARV,M3/MIN =t,E15.8,5X,tUPHARV,HP,= ',El5.8, 

&/,lOX,'QHARVB,M3/MrN =',El5.$,5X,'UPHARVB,HP,= ',E15.8, 

&/,lOX,'QRECY,M3/MIN, =',El5.8,5X,WPRECYC,HP, =',E15.8, 

&/,lOX,'WATDISP,M3/MIN, =',E15.8,5X,'UPWATD,HP, =',E15.8, 

&/,lOX,'QMAKEUP,M3/MIN, =t,E15.8,5X,'UPMUP,HP, =',E15.8, 

&/,lOX,'UPTOT,HP, =',El5.8,5X,'UPKWR,KWH, =',E15.8, 

&/,IOX,'UMIX,HP, =',E15.8,5X,'UMKWH, KWH, =',E15.8, 

&/,lOX,'UHKWH, KWH = ',E15.8,/,10X,'UTOT,KWH, =',E15.8) 
Page 8 

FORTRAN77 V3.20 02/84 

202 PORMAT(//,lOX,'DIRECT LABOR, $=t,E15.8,/,10X,10VERH~AD, 

&$ =',E15.8, /,lOX,'UTILITY COSTS, $',E15.8,//,10X, 

&'CARBON REQUIRED',4X,E15.8,2X,'MMSCFDT,/,1OX, 

&'ATr,2X,E15.8,T $ PER MSCPT,/, 

&lOX,'CARBON, $ =',E15.8,/, lox, 

&'NITROGEN, $ =',E15.8,/,10X,'POTASSIUM, $ =I, 

&El5.8,/,10X,'PHOSPHORUS, $ =',E15.8,//, lox, 

&'TOTAL NUTRIENTS, $ =',E15.8,///,10X, 

&'TOTAL WATER COSTS, $ =',E15.8,//,10X, 

&'OTHER OPERATING COSTS, $=',E15.8,//,10X, 

&'MAINTENANCE COSTS, $=r,E15.8,//,10X, 

&'TOTAL OPERATING COSTS, $ =',E15.8,/) 

C 

C 

C WRITE INPUT FILE FOR DOANE METHODOLOGY 

C 

C 

C 

499 ZMNT = 0.0 

W RITE(~,~OO)(AA(I),ATEXT(I),I=BO,~~),DCI, NDCI,OP,M NT, 

&ANET,BBLS,CARBP,PROP 

500 FORMAT(27(E15.8,4X,A60,/),El5.8,4X, 

&'DCI, DEPRECIABLE CAPITAL INVESTMENT, $',/,E15.8,4X, 

PNDCI, NON DEPRECIABLE CAPITAL INVESTMENT, $ ',/,E15.8,4X, 

&'OF, ANNUAL OPERATING COSTS, $ ',/,E15.8,4X, 

dclMNT, ANNUAL MAINTENANCE COSTS, $ ',/,E15.8,4X, 

&'ANET,ANNUAL SYSTEM ALGAL YIELD,MT/YR ',/,E15.8,4X, 

&'BBLS,ANNUAL SYSTEM LIPID YIELD,BBLS/YR ',/,E15.8,4X, 

&'CARBP,CARBOHYDRATE YIELD MT/YR ',I, 

&E15.8,4X,'PROTEIN YIELD, MT/Y R',/) 

STOP 

END