Modeling Integrated 

Biomass Gasification 

Business Concepts 

Peter J. Ince

Edward M. (Ted) Bilek

Mark A. Dietenberger

United States

Department of

Agriculture

Forest Service

Forest

Products

Laboratory

Research

Paper

FPL–RP–660
June 2011

Ince, Peter J.; Bilek, Edward M. (Ted); Dietenberger, Mark A. 2011. 

Modeling integrated biomass gasification business concepts. Research 

Paper FPL-RP-660. Madison, WI: U.S. Department of Agriculture, Forest 

Service, Forest Products Laboratory. 36 p.

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Abstract

Biomass gasification is an approach to producing energy 

and/or biofuels that could be integrated into existing forest 

product production facilities, particularly at pulp mills. 

Existing process heat and power loads tend to favor integration at existing pulp mills. This paper describes a generic 

modeling system for evaluating integrated biomass gasification business concepts over a range of production scales and 

process options, including process models and a discounted 

cash-flow model, all of which are contained in a Microsoft® 

(Redmond, Washington) Office Excel workbook (available 

from the authors). The process models encompass biomass 

preparation, gasification, heat recovery, and syngas cleanup, 

along with the options of converting syngas to biofuel feedstocks via a Fischer–Tropsch gas-to-liquid (GTL) process, 

the use of syngas or GTL tail gas for process heat energy, 

and the option of added process energy equipment, including turbines and generators. The cash-flow model computes 

measures of financial performance for incremental investment in gasification business concepts, including net present 

value and internal rate of return. We also describe stochastic 

simulation methods for financial risk assessment, and we 

present results of sensitivity analysis and stochastic simulation for investment in a biomass-to-liquid-fuel concept at 

an existing pulp mill, based on plausible but hypothetical 

process data and stochastic price projections. The results, as 

reinforced by the sensitivity analysis and risk assessment, 

suggest an investment may be attractive from a financial 

standpoint.

Keywords: Biofuels, biomass gasification, economic 

feasibility, forest biorefinery model

Acknowledgments

The authors gratefully acknowledge the advice and assistance of those who contributed expertise, information, and 

helpful suggestions as we developed the generic process 

model and prepared this report, including representatives of 

ThermoChem Recovery International, Inc. (TRI), Flambeau 

River Biofuels LLC, Emerging Fuels Technologies (EFT), 

and the U.S. Department of Energy National Renewable 

Energy Laboratory (NREL). We also gratefully acknowledge the contributions of research collaborators, including 

Professor Ross Swaney, University of Wisconsin–Madison, 

who contributed the prototypical process design and cost 

estimate for the Fischer–Tropsch component of the biorefinery concept (Appendix C), and Dr. John Zerbe (retired) of 

the Forest Products Laboratory who provided estimates of 

process parameters and costs for biomass drying equipment.

Contents

Introduction.......................................................................... 1

Integrated Biomass Gasification Concept............................ 1

Process Models and Parameters........................................... 2

 Base Case Model.............................................................. 3

 Business Case Model....................................................... 5

 Biomass Procurement ...................................................... 5

 Biomass Preparation and Drying ..................................... 6

 Biomass Gasification ....................................................... 7

 Syngas Heat Recovery and Cleanup.............................. 10

 Gas-to-Liquid and Distillation ....................................... 11

 Energy System Upgrades............................................... 13

Cash-Flow Model............................................................... 14

 Annual Operating Costs and Revenue ........................... 15

 Capital Costs.................................................................. 16

 Taxes, Credits, and Incentives........................................ 17

 Economic Assumptions and Financing Options............ 17

 Incremental Cash Flows................................................. 19

 Measures of Performance and Sensitivity...................... 20

Risk Analysis and Stochastic Risk Assessment ................. 22

Multivariate Stochastic Simulation.................................... 23

Summary............................................................................ 26

References.......................................................................... 27

Appendix A—Base Case Process Model 

Relationships...................................................................... 28

Appendix B—Business Case Process Model 

Relationships...................................................................... 30

Appendix C—GTL Plant Prototypical Design and 

Costs................................................................................... 32

 Fischer–Tropsch Component of the Forest Biorefinery 

 Study: Prototypical Process Design and Cost 

 Estimate.......................................................................... 32

 Background and Description.......................................... 32

 Design Basis and Assumptions...................................... 32

 Economics of the Design ............................................... 33

 Analysis and Discussion of Design................................ 34
Modeling Integrated Biomass Gasification 

Business Concepts

Peter J. Ince, Research Forester

Edward M. (Ted) Bilek, Economist

Mark A. Dietenberger, Research General Engineer

Forest Products Laboratory, Madison, Wisconsin

Introduction

The pulp and paper industry is a large consumer of energy 

as well as wood fiber raw materials; therefore, integrated 

biorefining and biomass energy systems are of interest 

within the industry. Thermochemical biorefining, based 

primarily on biomass gasification, is one of the leading platforms for new business concepts in integrated forest product 

biorefining (Connor 2007; Thorp et al. 2008a, 2008b). The 

economic feasibility of the integrated biomass gasification 

concept has also been explored in some previously published studies, such as an assessment of gasification-based 

biorefining at kraft pulp and paper mills in the United States 

developed at the Environmental Institute of Princeton University (Larson et al. 2008, 2009; Agenda 2020 CTO Working Group 2008a, 2008b).

Our research on generic modeling of the integrated biomass 

gasification business concept began in 2006, as the Potlatch 

Corporation (Spokane, Washington) was concluding an exploratory assessment of the concept at a kraft pulp mill located in Warren, Arkansas. The gasification-based biorefining concept looked promising in many ways, but researchers 

recognized that the likelihood of successfully establishing 

a new market for biomass gas-to-liquid products would 

improve if there were multiple entrants. As such, Potlatch 

and other industry representatives encouraged development 

of generic models of the concept so that other potential 

entrants could explore the same concept at other pulp mill 

locations. Our work on modeling the concept was largely a 

response to that need. Meanwhile, since 2006, the concept 

was also rigorously evaluated with the assistance of U.S. 

Department of Energy grants at two other U.S. pulp mill 

locations (at the Flambeau River Paper mill in Park Falls, 

Wisconsin, and at the NewPage paper mill in Wisconsin 

Rapids).

This report describes generic techniques we developed for 

modeling investments in integrated biomass gasification 

business concepts at existing forest product production facilities. We developed generic tools for preliminary analysis of 

the concept over a range of different production scales and 

process settings (e.g., potentially applicable to various mill 

locations). Our generic models are packaged in a Microsoft®

Office (Redmond, Washington) Excel workbook file, which 

makes them portable and accessible to others. Interested 

users may contact the authors of this paper to obtain a free 

copy. The conceptual analysis we present in this report is 

based on hypothetical data and is generally not nearly as 

rigorous as site-specific assessments that have been done at 

mill locations mentioned above. Those assessments were 

developed with additional engineering input and have progressed to fairly advanced stages of analysis. Our generic 

models are designed for preliminary stages of analysis. If 

preliminary results look promising, much greater due diligence and more sophisticated engineering will be needed to 

support subsequent development and investment decisions.

Integrated Biomass Gasification 

Concept

This report describes generic modeling of a business concept called integrated biomass gasification, which could 

include facilities for biomass preparation, biomass gasification, syngas cleanup, heat recovery, gas-to-liquid (Fischer–

Tropsch (FT)) and related energy systems in the context of 

an existing wood pulp mill or other large forest products 

facility. The concept that we describe is similar to that described by Connor (2007), and also similar to the “Phase 1” 

biomass gasification concept as described in a recent article 

by the paper industry’s Agenda 2020 CTO Working Group 

(2008a). This report does not involve the concept of black 

liquor gasification (known as “Phase 2”), which is another 

biorefining concept that has been explored by others 

(Larson and others 2008, 2009).

As defined by the American Forest & Paper Association 

(AF&PA) Agenda 2020 CTO Working Group (2008a) the 

“Phase 1” concept specifically means the installation at a 

pulp mill of a biomass gasification plant to produce syngas. 

The syngas could be used to produce liquid transportation fuels (e.g., diesel fuel) and other co-products (alkanes 

and paraffinic waxes), which would require installation of 

syngas cleanup, cooling and conditioning equipment, plus 

a Fischer–Tropsch gas-to-liquids (GTL) plant. The concept 

also involves production synergies with the pulp mill in the 

form of energy supplied by the gasification process, which 

may involve cogeneration of power and displacement of 

fossil fuels. For example, syngas or residual tail gas from 

the GTL plant can displace natural gas (used chiefly in the 

lime kiln of a kraft pulp mill), and can also be used as fuel 
Research Paper FPL–RP–660

2

to generate steam and electric power. Also, according to 

the Agenda 2020 CTO Working Group (2008a), the “Phase 

1” concept (biomass gasification) appears to be technically 

feasible within the capability of existing commercially 

available equipment and carries somewhat less risk than the 

Phase 2 concept (black liquor gasification).

Figure 1 is a diagram of process elements that are included 

in the integrated biomass gasification concept described in 

this report. Pulp mills vary in design and energy requirements, so optimal design of an integrated biomass gasification system would vary in terms of related pulp mill energy 

systems such as electrical generating facilities or auxiliary 

boilers. There is also the option of producing syngas only 

(e.g., producer gas as a fuel for displacement of natural 

gas), which would not require the GTL process nor as much 

syngas cleanup. That more limited option has been called 

“Phase 1: Option 1,” whereas biomass gasification coupled 

with the Fischer–Tropsch GTL process is called “Phase 1: 

Option 2” (Agenda 2020 CTO Working Group 2008a). Option 2 is, of course, more complex and requires generally 

more capital investment than Option 1. Option 2 would 

also involve the marketing challenges and opportunities of 

introducing to an existing pulp mill a range of new products 

(Fischer–Tropsch biofuels and related co-products). The 

models that we describe in this report can be used to evaluate either “Option 1” or “Option 2” biomass gasification 

concepts.

Biomass could be purchased wood or bark or mill or agricultural residue. Biomass preparation equipment could consist of conventional chippers or hammermills and screens, 

followed by drying, as in a conventional rotary drum dryer. 

Various types of equipment are available for biomass gasification (or reforming) but choice of equipment can influence 

other process options. A wider range of gasification equipment is applicable if Option 1 is the sole objective—production of producer gas for combustion only (e.g., to displace 

natural gas). However, if Option 2 (GTL) is a current or 

future objective, then gasification equipment must be 

capable of producing higher energy syngas to allow efficient 

conversion in the GTL process. Some biomass gasifiers can 

also generate hot flue gases that may be used to provide heat 

for biomass drying. In addition, with Option 2, syngas heat 

recovery and gas cleanup will be employed because the catalytic GTL process operates at lower gas temperatures, and 

impurities must be removed from syngas to avoid fouling of 

catalysts used in the GTL process.

Although not shown in Figure 1, integration of a new biomass gasification system to a pulp mill could also involve 

energy system upgrades, such as additional boiler capacity, 

electrical generating capacity, or steam handling capacity, 

along with possible modification of other existing pulp mill 

facilities such as wood handling or lime kiln operations. The 

process models that we developed allow for simulation of a 

number of alternative process arrangements, including Option 1 or Option 2 arrangements mentioned previously, as 

well as different options in terms of energy system upgrades 

or combined heat and power production.

Process Models and Parameters

The process models consist of mathematical relationships 

that compute process input requirements, overall operating 

revenue, and overall operating costs for a specific forest 

product production facility both before and after introduction of integrated biomass gasification. Computations are 

based on specific parameter values as determined by input 

data. All parameter relationships and input data are contained in the Excel workbook. Changing any input data will 

change parameter values and result in changing the estimated operating revenue or costs and the projected economic 

performance of the production facility.

Two process models are in the Excel workbook: the “base 

case” model, and the “business case” model. The base case 

model describes operating revenue and costs at an existing 

forest product facility before introduction of biomass gasification. The business case model projects operating costs and 

revenue at the same facility after introduction of integrated 

Figure 1. Process elements of biomass gasification concept with gas-to-liquid.
Modeling Integrated Biomass Gasification Business Concepts

3

biomass gasification technology. The difference in estimated 

operating revenue and costs between the two models provides the incremental gain or loss in operating costs and revenue associated with the business concept (i.e., the biomass 

gasification concept). The models have some flexibility 

in specifying alternative scales of production and process 

configurations. The next section outlines the structure of the 

base case model and how input data and process parameters 

can be adjusted to simulate alternative mill configurations 

and scales of production at an existing forest product facility. Subsequent sections outline process parameters, relationships, and input data for the business case model and each 

element of the integrated biomass gasification process.

Base Case Model

The base case refers to an existing forest product production facility that is in operation prior to introduction of integrated biomass gasification technology. The base case model 

describes overall operating costs, revenue, and selected 

material and energy flows at the existing facility. The existing facility may include an existing pulp mill or pulp and 

paper mill that may be operated in conjunction with other 

wood product mills (for example, a sawmill or plywood 

mill). Production of wood products (lumber or plywood) 

can generate wood or bark residue that may be used at the 

pulp mill. Our modeling framework is flexible in that it can 

represent a pulp mill alone, or a pulp and paper mill, which 

may be operated also in conjunction with one or more local 

wood product mills. In the hypothetical sample data that we 

included in this report, we represented a sawmill operating 

in conjunction with a pulp and paper mill.

The base case process model represents the primary material and energy flows likely to be directly affected by introduction of integrated biomass gasification. Thus, the base 

case process model includes pulp, paper, and wood product 

production volumes, costs, product revenue, timber procurement, and disposition of wood and bark residue, process 

steam, and energy flows, including electric power cogeneration, and process fuel inputs including natural gas for the 

lime kiln typically associated with a kraft pulp mill.

Table 1 lists all the model parameters that can be used to 

describe a base case process, which can be varied by scale 

or product output. The table includes hypothetical data values as examples of model inputs. The hypothetical data in 

Table 1 do not pertain to any specific existing mill, but the 

scale of production and data values are thought to be typical for a modern kraft pulp and paper mill facility, at 2008 

price levels. A user can change input data for any parameter 

in Table 1 to represent a different mill situation or different 

market conditions.

The mass flow unit is short tons per day and the energy flow 

unit is megawatt hours per day for consistency with electricity and heating conventions. The engineer or scientific users 

can easily insert conversion of units in the spreadsheet to 

suit their needs, whereas the typical user will benefit from 

consistency of units throughout the process model.

As shown in Table 1, we specified hypothetical input data 

for all parameters of the base case process (in this case a 

kraft pulp and paper mill operating in conjunction with sawmill capacity). The parameters can be assigned different data 

values, and some can be assigned zero values when modeling different types of production facilities. For example, if 

no wood product mill was operating in conjunction with 

the pulp and paper mill, then zero values would be assigned 

to all seven parameters under the subheading of “Wood 

product mill averages,” and all three parameters under the 

subheading of “Wood mill residue by-products.” This would 

effectively exclude wood product capacity from the model 

so that the model would represent a pulp and paper mill 

alone.

Alternative pulp mill technologies can also be represented 

by assigning alternative values to selected parameters. For 

example, if the pulp mill is not a kraft mill then it would 

typically not include a lime kiln, and thus zero values would 

be entered for the three parameters that pertain to the lime 

kiln (Lime Kiln Gas Energy Required, Lime Kiln Power 

Load, and Lime Kiln % Excess Air). Similarly, not all pulp 

mills include soap and turpentine recovery, and in that case 

zero values would be input for the two parameters that pertain to soap and turpentine recovery (Soap & Turpentine Recovery, and Soap & Turpentine Revenue). It is also possible 

to model a pulp mill only without a paper mill; for example, 

a mill that produces only market pulp. In that case, the Mill 

Production and Product Revenue parameters would be assigned values that pertained to market pulp output, the Mill 

Operating Cost would pertain to the pulp mill only, and zero 

values would be entered for other parameters that pertained 

only to papermaking (such as Coatings & Fillers, and Paper 

Mill Power Load).

Figure 2 is the material and energy flow diagram for the 

base case process model, with computed values determined 

by data inputs in Table 1. The base case process diagram 

and computed material and energy flows are included in the 

Excel workbook. As illustrated in Figure 2, the paper mill, 

pulp mill, and other wood product mill(s) such as sawmill 

and chip mill(s) will typically have interrelated material and 

energy flows. For example, in the hypothetical case illustrated in Figure 2, the paper mill consumes pulp that is 

produced at the pulp mill, which in turn consumes pulpwood 

chips obtained from a chip mill and also wood residue chips 

from a sawmill. The sawmill output and lumber recovery 

efficiency determine the quantities of pulpwood residue chip 

and bark residue outputs. The chip mill satisfies remaining 

needs for pulpwood chips and also produces bark residue. 

Bark residues are burned in a wood/bark boiler, which provides high-pressure steam for cogeneration of electric power 

in steam turbines and generators, and lower pressure steam 

for pulp and paper mill process steam requirements. The 
Research Paper FPL–RP–660

4

pulp mill is assumed to be a kraft mill, so energy flows for 

the lime kiln are included. The pulp and paper mill steam 

and electric power requirements are net energy needs (after 

taking account the energy that may be generated by combustion of black liquor at the pulp mill).

The process parameters illustrated in Figure 2 can be 

modified to reflect a variety of alternative configurations at 

existing production facilities. For example, as mentioned 

earlier, if the facility does not include any wood mill(s), then 

the wood mill production and wood mill residue and energy 

flows can be deleted by assigning zero values to the wood 

mill and wood mill residue parameters. Similarly, the lime 

kiln or the soap and turpentine recovery can be deleted if 

not present in the existing production facility. There is also 

Table 1—Model parameters and input data that describe base case process 

Parameter description Unit of measure 

Hypothetical 

data

Pulp and paper mill averages 

Mill production (average) Machine-dry tons/day 1,725 

Mill operating costs $/ton (excluding wood cost) $350 

Product revenue (average) $/ton $750 

Coatings and fillers content 

(including recycled fiber 

and market pulp fiber) (%) of finished sheet weight 

17%

Machine-dry moisture (%) total weight of finished product 4% 

Pulp yield (%) on oven dry total weight basis 46% 

Pulp chip moisture content (%) total weight basis 50% 

Bark from logs removal (%) total weight basis 15% 

Bark from logs moisture content (%) total weight basis 42% 

Soap and turpentine recovery Gallons/green ton pulp chips 4.0 

Soap and turpentine revenue $/gallon $0.50 

Pulpwood price $/green ton delivered $32.50 

Pulp mill power load MWH (average net load)/ton green chips 0.012 

Paper mill power load MWH (average net load)/ton paper output 0.209 

Chip mill power load MWH (average net load)/ton green chips 0.007 

Process steam required MWHth/ton dry pulp 0.472 

Lime kiln gas energy required MWHth/ton dry pulp 0.341 

Lime kiln power load MWH/ton dry pulp 0.01 

Lime kiln (%) excess air Excess over stoichiometric 4% 

Dry-basis boiler efficiency Steam heat output per fuel HHV 84% 

Bark boiler power load MWH/green ton bark 0.007 

Chip mill bark directed to boiler (%) total weight basis 100% 

Wood product mill averages 

Wood mill(s) production 103

 bf/d (lumber) 360 

Wood mill operating costs $/103

 bf (excluding wood cost) 125 

Product price $/103

 bf (average) 300 

Lumber recovery factor 103

 bf board tally/ft3 log input 7.7 

Board foot log scale/ft3 Board foot/ft3

 (unit conversion) 4.79 

Log price $/103

 bf (average) 390 

Wood mill(s) power load MWH/green ton logs 0.007 

Wood mill residue by-products 

Clean pulp chips Green tons/103

 bf log scale 0.56 

Bark residue Green tons/103

 bf log scale 0.57 

Wood mill bark directed to boiler (%) total weight basis 100% 

Energy-related parameters 

Cogenerated electricity revenue $/kWh 0.05 

Purchased electricity price $/kWh 0.07 

Purchased boiler fuel price $/MWHth $27.00 

Purchased natural gas price $/MWHth $27.00 

Air for biomass combustion Tons air (stoichiometric)/MWHth biomass 1.208 

Methane in natural gas Tons methane/MWHth NG 0.079 

Air for methane combustion Tons air (stoichiometric)/MWHth natural gas 1.369 

Steam enthalpy in water removal MWHth/ton water removal 0.624 

Recoverable high-pressure steam Water tons per day/steam tons per day 0.950 

Maximum steam turbine output MWH/MWHth high-pressure steam 0.357 

High-pressure steam enthalpy MWHth/ton superheated high-pressure steam 0.700 

Process steam enthalpy MWHth/ton process steam 0.825 
Modeling Integrated Biomass Gasification Business Concepts

5

the option of including additional power boiler, gas turbine, 

power generator, or steam recovery capacity, as indicated in 

Figure 2, although such additional capacity is not needed in 

this case. The wood/bark boiler in this base case example 

produces sufficient energy to satisfy net pulp and paper mill 

energy needs, and also produces surplus electric power that 

is sold to the local utility power grid. We note that all processing units continue to provide mass and energy balances 

so that they remain scalable and adaptable with respect to 

each other in spite of the changes to the parameter values by 

the user.

The Excel workbook contains mathematical formulas that 

compute the base case process material and energy flows. 

Computed values shown in the process diagram (Fig. 2) are 

based on the input data shown in Table 1. Appendix A provides some examples of base case model formulas and computations with reference to the sample data inputs in Table 1 

and computed values in Figure 2.

Business Case Model

The business case model represents integration of biomass 

gasification and related equipment at the facility represented 

in the base case model and projects operating revenue and 

costs at the facility after integration of biomass gasification 

and biofuel production. Thus, the business case model 

retains many features and parameters of the base case model, but also introduces other process elements that were not 

included in the base case. The additional elements that may 

be included in the business case model are additional biomass procurement, biomass preparation and drying capacity, 

biomass gasification, syngas cleanup and heat recovery capacity, syngas-to-liquid (Fischer–Tropsch) biofuel synthesis 

and distillation capacity, and energy system upgrades. The 

following sections discuss each of these elements.

Biomass Procurement

As shown in Figure 1, biomass procurement is the first element of the integrated biomass gasification process. Procurement may include purchase of biomass and also use of 

available residual biomass. We designed the generic process 

model to allow a range of sources, including purchased logging residue from timber harvest, other purchased forest or 

farm residue (agricultural biomass), and bark residue from 

the existing wood mill(s) or chip mill(s). Procurement from 

any combination of those sources can be represented in the 

business case model. After procurement, the biomass goes 

to biomass preparation and drying (prior to gasification), 

whereas a share of the biomass can also be directed to the 

wood/bark boiler(s) for additional process steam and energy 

generation.

Figure 2. Base case process diagram with material and energy flows corresponding to input data in Table 1. 

MBF is 103 board feet.
Research Paper FPL–RP–660

6

Model parameters and data inputs for biomass procurement 

include prices and quantities of additional purchased biomass, the shares of biomass directed to the wood/bark boiler 

(versus the share used for biomass gasification), the fractions of wood, bark, and agricultural biomass, and the moisture contents and heat energy values of the various biomass 

fractions. Table 2 summarizes model parameters related to 

biomass procurement for biomass gasification, and also includes hypothetical input data values for each parameter.

Figure 3 is the material flow diagram for biomass procurement in the business case model. The Excel workbook 

contains mathematical formulas to compute material flows, 

and Figure 3 shows computed values based on hypothetical 

input data in Table 2. Appendix B gives some examples of 

computational formulas in the model with reference to values in Figure 3 and hypothetical data in Table 2.

Biomass Preparation and Drying

As shown in Figure 1, biomass preparation and drying is 

the next step in the integrated biomass gasification process 

following biomass procurement. Raw biomass materials 

can vary from larger tree logs to smaller stems, to sawmill 

slabs or edgings, whole-tree chips, or agricultural residue 

such as corn stover. The moisture content of raw biomass 

can also vary and is typically fairly high (e.g., 50% moisture 

for “green” wood on a total weight basis). Efficient biomass 

gasification will generally require some biomass preparation 

that includes reducing to smaller uniform particles (such 

as the size of wood chips used at pulp mills) and drying to 

lower moisture content (e.g., 10%). Particle sizing and drying may be less crucial if the only objective is producing 

syngas for direct combustion (Phase 1, Option 1), but more 

important if the objective is to produce higher energy syngas 

for biofuels (Phase 1, Option 2). Parameters such as energy 

inputs and moisture content can be adjusted in the model to 

reflect different processing requirements and different 

characteristics of biomass raw materials.

Equipment used for biomass preparation and drying would 

typically consist of conventional wood chippers and bark 

hammermills, with chip screening devices to remove 

Table 2—Model parameters and input data related to biomass 

procurement 

Biomass procurement averages 

Parameter description Unit of measure 

Hypothetical 

data

Logging residue biomass purchased Green tons/day 1,100 

Other biomass purchased Green tons/day 500 

Purchased biomass to wood/bark boiler (%) total weight basis 0 

Logging residue price (average) $/green ton $26.50 

Logging residue bark fraction (%) total weight basis 15 

Other biomass price (average) $/green ton $26.50 

Other biomass wood (%) total weight basis 75 

Other biomass bark (%) total weight basis 25 

Other biomass agricultural residue (%) total weight basis 0 

Average moisture of wood biomass (%) total weight basis 40 

Average moisture of bark biomass (%) total weight basis 42 

Average moisture of agricultural biomass (%) total weight basis 12 

Chip mill bark to wood/bark boiler (%) total weight basis 100 

Wood mill bark to wood/bark boiler (%) total weight basis 0 

Figure 3. Material flows for biomass procurement in business case model.
Modeling Integrated Biomass Gasification Business Concepts

7

oversize particles, followed by drying in a conventional rotary drum dryer. The dried biomass is then typically collected in a surge storage hopper to ensure continuous flow into 

the biomass gasifier when biomass preparation or drying is 

interrupted because of equipment malfunctions or weather. 

We assumed that the drum dryer would be heated by flue 

gases from the biomass gasifier or other resident combustion systems. A general regulatory concern in heat drying 

of wood or biomass is minimizing atmospheric release of 

volatile organic compounds (VOCs). Thus, we assumed the 

dryer would be operated with a fairly low discharge temperature (e.g., below 280° F/137 °C) to limit release of VOCs 

and promote entrainment of volatiles along with biomass 

into the biomass gasifier, but data inputs for biomass drying 

can be varied. In a typical facility, a flare is used to assist 

in cold startup in which unusable raw syngas, unusable tail 

gas, and the VOCs from the dryer can be combusted and 

exhausted to the atmosphere. When the facility is at full 

running condition in which all processing units are functioning, the need for a flare may decline to the point where a 

regenerative thermal oxidizer (RTO) or a wet electrostatic 

precipitator may be used for pollution control. Although 

several different pieces of equipment and storage units may 

be needed, particularly for a very large facility, they are all 

grouped into the processing unit and labeled for biomass 

preparation and drying in the generic business case model.

Model parameters and data inputs for biomass preparation and drying include biomass moisture content following drying, heat energy requirements for biomass drying, 

estimated VOC emissions, and electrical power load for 

biomass preparation and drying equipment. Table 3 lists 

model parameters related to biomass preparation and drying 

and also includes hypothetical input data for each parameter. 

Separate data values can be input for each principal category 

of biomass (wood, bark, and agricultural biomass), and the 

model will compute weighted average values. Figure 4 is the 

material and energy flow diagram for biomass preparation 

and drying. The Excel workbook model contains mathematical formulas to compute the material and energy flows, and 

Figure 4 shows computed values based on the hypothetical 

input data in Table 3. Appendix B gives some examples of 

computational formulas in the model with reference to values that appear in Figure 4 and data parameters in Table 3.

Biomass Gasification

As shown in Figure 1, biomass gasification to raw syngas 

is the next step in the integrated biomass gasification process following biomass preparation and drying. In biomass 

gasification (sometimes referred to as biomass reforming), 

the dried biomass is exposed to high temperatures and rapid 

heat transfer, resulting in rapid pyrolysis and volatizing of 

the biomass to gases that include the principal components 

of syngas, carbon monoxide, and hydrogen. A number of 

Table 3—Model parameters and input data for biomass preparation and 

drying 

Parameter description Unit of measure 

Hypothetical 

data

Biomass preparation and drying averages 

Dried biomass moisture content Dry weight basis 10% 

Biomass drying energy required MWHth/ton water removal 1.001 

VOC emissions Dry weight basis 0.0010 

Power load for preparation and drying MWH/green ton biomass 0.037 

Figure 4. Material and energy flows for biomass preparation and drying.
Research Paper FPL–RP–660

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different gasification technologies are available, ranging 

from technologies that admit air in the process to technologies that mostly exclude air (such as steam reforming). 

Technologies that admit significant amounts of air may have 

lower costs but may also yield lower energy syngas because 

inert nitrogen is introduced by the air and because carbon 

dioxide is formed from reaction of carbon with oxygen in 

the air. Lower energy syngas may be acceptable for direct 

combustion (Phase 1, Option 1), but may not be suitable for 

efficient conversion of syngas to biofuels or chemicals via 

the Fischer–Tropsch process (Phase 1, Option 2). Thus the 

choice of gasifier technology may determine or constrain the 

options for syngas use.

Steam reforming is an example of biomass gasification 

technology that has potential to produce high energy syngas. In steam reforming, the dried biomass is exposed to 

superheated steam, while admission of air into the process 

is minimized (for example, biomass can be fed into a steam 

reformer with a plug screw that minimizes entrained air). As 

described by Connor (2007), the superheated steam (H2O) 

has an endothermic reaction with the carbon in the biomass, 

consuming heat from an external fuel source and producing 

hydrogen (H2) and carbon monoxide (CO), principal ingredients of synthesis gas or syngas. This reaction of superheated steam with carbon is called steam reforming:

H2O + C + Heat → H2 + CO (1)

Likewise, some measure of pure oxygen (O2) can be used in 

oxygen reforming of carbon particles and char (or known as 

carbon trim):

O2 + 2C → 2CO + Heat (2)

O2 + 2CO → 2CO2 + Heat (3)

The water-gas shift reaction also occurs simultaneously with 

the steam and oxygen reforming reaction to yield additional 

hydrogen and carbon dioxide, also known as the water-gas 

shift reaction:

H2O + CO → H2 + CO2 + Heat (4)

Other intermediate reactions also occur, such as the formations of methane from carbon and hydrogen, carbon 

monoxide from carbon and carbon dioxide, hydrogen and 

carbon monoxide from methane with water vapor or carbon 

dioxide, and several other reactions. Most of these reactions are reversible, depending on reactant concentrations, 

temperature, and directions of energy flows. At the highest 

temperatures only the simplest gases are left, making the 

water gas shift the dominating reaction. In the end, steam, 

pure oxygen, biomass, and energy inputs can be modulated 

to yield a 2:1 molar ratio of hydrogen and carbon monoxide 

in syngas, which is ideal for the Fischer–Tropsch reaction. 

After the moisture and trace gas removals, syngas compositions may be obtained at molar percentages that are typical 

for biomass, such as 44.6% H2, 22.3% CO, 26.4% CO2, 

4.5% CH4, 1.0% C2H4, and 1.2% N2.

Some steam-based gasifiers may be designed for the simpler, but less efficient and costlier, staging of these reactions 

into separate reaction vessels to obtain the targeted molar 

ratio of hydrogen and carbon monoxide. It is likely that 

varying levels of steam, carbon dioxide, nitrogen, and trace 

gases exist among these gasifiers even if their molar ratio 

of hydrogen and carbon monoxide is 2:1. The complexities 

and variations among the gasifiers and reformers lead us to 

identify basic parameters consistent with other processing 

units in the generic business case model without necessarily 

involving detailed engineering analysis of gasification reactions or revealing proprietary data.

Table 4—Model parameters and input data related to biomass gasification 

Hypothetical data 

Parameter description and unit of measure Wood Bark 

Agricultural 

residue Average 

Feedstock higher heating value (HHV) 4.707 5.227 3.894 4.829 

(MWHth/dry ton biomass feed) 

Gasification thermal energy required 1.096 1.096 1.594 1.096 

(MWHth/dry ton biomass feed) 

Reforming steam energy required 0.432 0.541 0.410 0.458 

(MWHth/dry ton biomass) 

Energy content of gasifier producer gas (or raw syngas) 2.606 2.606 2.606 2.606 

(MWHth/ton producer gas) 

Flue gas recoverable energy 0.490 0.909 0.797 0.588 

(MWHth/dry ton biomass) 

Ash and char residue 0.031 0.026 0.159 0.030 

(tons/dry ton biomass) 

Power load 0.131 0.131 0.131 0.131 

(MWH/dry ton biomass) 

Oxygen feed for char gasification 0.237 0.245 0.186 0.239 

(Tons/ton of dry biomass) 

Excess air in reformer fuel combustion 20 

(% excess over stoichiometric requirement) 
Modeling Integrated Biomass Gasification Business Concepts

9

Regardless of technology, biomass gasification in general 

involves use of thermal energy to gasify biomass and reform 

it into syngas (carbon monoxide and hydrogen) plus other 

gases. Model parameters for biomass gasification include 

the combustion higher heating values of the dried biomass 

feedstocks, thermal energy required for biomass gasification, steam energy associated with the low-pressure steam 

mass required for steam reforming, energy content of producer gas or raw syngas, recoverable energy in gasifier flue 

gases, yield of ash and char residue, combustion heating 

value of char and ash residue, power load to operate gasifier 

and auxiliary equipment such as oxygen generators, oxygen 

needed for char gasification, and excess air in combustion of 

reformer fuel (tail gas or natural gas). Table 4 summarizes 

the model parameters related to biomass gasification and 

provides hypothetical data values for each parameter.

The hypothetical data in Table 4 are generalized conceptual 

estimates for steam-based gasification and are not considered empirical data. In the end, experimental techniques and 

operational experience are the only way to obtain precise 

values for these parameters. Equipment vendors may also 

help provide data estimates. Some wood and bark heating 

value data can also be found in reference publications, such 

as Ince 1979, in which Table 1 provides higher heating values for some wood and bark species that were reported in 

other publications.

The model computes “average” data values (last column of 

Table 4) as weighted averages based on input data for wood, 

bark, and agricultural residue, weighted by tonnage shares 

of each in the total biomass feedstock. Thus, if a particular 

category of biomass is not used (e.g., agricultural biomass is 

not used in this hypothetical case as shown earlier in Table 

2), then its parameter values will not be factored into the 

averages (hence “average” values in Table 4 in this case are 

weighted averages for wood and bark only, not agricultural 

residue). Any input value can be changed, but as indicated in 

the hypothetical data in Table 4, some typical characteristics 

of wood, bark, and agricultural residue must be considered 

in making adjustments to input data. For example, wood 

and bark typically have higher combustion heating values 

but lower ash content than agricultural residue. Different 

feedstock characteristics also influence gasification thermal 

energy requirements, recoverable flue gas energy, heating 

value of ash char, and oxygen feed for char gasification and 

oxidation trimming of raw syngas. As a result, the biomass 

gasification unit has become the most complex model feature with the most parameters, and we expect that these 

parameters will be determined independently via empirical 

data or through detailed engineering analysis. 

Figure 5 is the material and energy flow diagram for biomass gasification. The Excel workbook model contains 

mathematical formulas to compute the material and energy 

flows, and Figure 5 shows computed values based on hypothetical input data in Table 4. The biomass gasification 

processing unit in the Excel workbook also shows an energy 

loss of 633 megawatt-hour thermal (MWHth, a measure 

of energy similar to Btu and expressed in terms of thermal 

energy) per day for its energy balance, but the loss can be 

reduced to zero by adjusting the raw syngas higher heating 

value to a slightly higher value.

As shown in Figure 5, the model assumes in this case that 

the primary fuel required for biomass gasification or reforming is tail gas from the Fischer–Tropsch gas-to-liquid (GTL) 

process, but the model is flexible in allowing for a supplemental or alternative fuel, specifically purchased natural 

gas (as shown in Fig. 5). In this hypothetical example, tail 

gas satisfies the fuel requirements for indirect heating of 

the steam reforming stages of biomass gasification, but if 

insufficient tail gas is available (or if a GTL process is not 

included) the model will use purchased natural gas to make 

up for the deficit fuel. Other computed inputs to biomass 

gasification include combustion air for tail gas or natural 

Figure 5. Material and energy flows related to biomass gasification.
Research Paper FPL–RP–660

10

gas, electric power for gasifier equipment, oxygen for char 

gasification and any syngas oxidation trimming, and steam 

input. The model assumes that flue gas from combustion of 

tail gas or natural gas is used for biomass drying, but that 

may be supplemented or replaced by flue gas from a gas turbine (from a power boiler) as an optional arrangement.

To reconfigure the steam-based gasification to just an airstarved gasifier with the intent to produce low energy gas 

for heat and power applications, the parameters for indirect 

combustion heating can be set to zero (thereby eliminating 

this cost component), whereas the oxygen mass parameter 

and its heating value can be adjusted for partial combustion 

of raw syngas with air. Considerable empirical data should 

be available due to many installations of the air-starved 

gasifiers.

Syngas Heat Recovery and Cleanup

As shown in Figure 1, syngas heat recovery and cleanup is 

the next step in the integrated biomass gasification process 

following biomass gasification. Heat recovery and syngas 

cleanup will be employed if the objective is to produce biofuels or chemicals via the Fischer–Tropsch process (Phase 

1, Option 2), because the catalytic Fischer–Tropsch reaction 

occurs optimally at much lower temperatures and higher 

pressures than biomass gasification, and impurities such as 

tars and sulfur will foul the catalysts unless they are substantially eliminated.

An effective typical sequence of syngas cleanup and heat 

recovery could involve multiple stages, including particulate removal, heat recovery, removal of trace gases, and gas 

filtration. A series of cyclones and a venturi scrubber could 

be used to remove particulates, which would then be eliminated in the carbon trim unit or the gasifier, or would be 

flared. Heat recovery with a heat steam recovery generator 

(HSRG) unit may reduce syngas temperatures to the point 

where tar is condensed, which may also be eliminated in the 

carbon trim unit or the gasifier, or would be flared. In some 

syngas cleanup schemes, the tar is cracked or reformed before the heat recovery to reduce or avoid tar condensation. 

The syngas may go through another heat recovery with an 

HSRG unit to condense the syngas steam, with the resulting 

effluent cleaned and disposed of. Trace gas removal may go 

through various steps, including a hydrogen sulfide scrubber at atmospheric pressure, and then pressurization for 

efficient scrubbing stages of ammonia, H2S, and COS, and 

zinc polishing and filtering of micron-sized particles to very 

low concentration levels (e.g., less than one part per million) 

as may be needed before direct insertion into the Fischer–

Tropsch catalytic reactor unit (to avoid reduced efficiency 

or fouling of catalysts). Other cleaning and heat recovery 

schemes are possible, but are not necessary to detail here, 

as multiple equipment arrangements are generally implied 

and grouped into the heat recovery and cleanup stage of the 

model.

If the objective is only to produce syngas clean enough and 

suitable for direct combustion, the initial removal of particulates, tar, steam, and much of H2S can be done with lower 

cost technology at atmospheric pressure, and in that case 

may not require the further pressurized cleaning needed in 

the Fischer–Tropsch reactor unit. So, if the only objective 

is to use the syngas for direct combustion (Phase 1, Option 

1), then syngas heat recovery or cleanup requirements may 

be reduced or eliminated. The model is flexible in that parameters related to syngas heat recovery and cleanup can be 

modified (or eliminated) to represent varying process objectives and requirements.

Model parameters related to syngas heat recovery and 

cleanup include the steam energy recovery rate (assuming 

that steam energy is recovered in the heat recovery process), 

the electrical power load for heat recovery equipment, the 

yield of clean syngas from the raw producer gas following 

gas cleanup, and the electrical power load for gas cleanup 

equipment. Table 5 summarizes the model parameters for 

syngas heat recovery and cleanup and provides hypothetical data values (conceptual values) for each parameter. The 

model computes “average” parameter values (Table 5) based 

on input data values for wood, bark, and agricultural residue 

weighted by the tonnage shares of each in the total biomass 

feedstock. Agricultural biomass is not used in this hypothetical case, so the “average” values in Table 4 are weighted 

Table 5—Model parameters and input data for syngas heat 

recovery and cleanup 

Hypothetical data 

Parameter description and unit of measure Wood Bark 

Agricultural

residue Average 

Steam energy recovery factor 0.273 0.279 0.265 0.274 

(MWHth/producer gas ton) 

Power load for heat recovery 0.005 0.005 0.005 0.005 

(MWH/producer gas ton) 

Clean syngas yield 0.740 0.733 0.733 0.738 

(Syngas ton/producer gas ton) 

Power load for gas cleanup 0.001 0.001 0.001 0.001 

(MWH/syngas ton) 
Modeling Integrated Biomass Gasification Business Concepts

11

averages for wood and bark only and not the agricultural 

residue.

Figure 6 is the material and energy flow diagram for syngas 

heat recovery and cleanup. The model contains mathematical formulas to compute material and energy flows, and 

Figure 6 shows computed values based on the hypothetical 

input data in Table 5. Note that the HHV of 4,695 MWHth/

day for the clean cold syngas (Fig. 6) indicates a cold gas 

energy recovery efficiency of 95% in relation to the dried 

biomass feed’s HHV of 4,954 MWHth/day (Fig. 5). More 

recent detailed engineering analysis via TRI proprietary 

work suggests this cold gas efficiency may be more like 

90%, so parameters in Tables 4 and 5 may need further 

adjustments. Results with that adjustment were reported recently at the TC Biomass 2009 conference (see information 

at the following Web site: http://media.godashboard.com//

gti/TCBiomass2009_Poster_MDietenberger.pdf).

Gas-to-Liquid and Distillation

As shown before in Figure 1, GTL and distillation are final 

steps in the integrated biomass gasification process (following syngas heat recovery and cleanup). The process 

model includes a representation of the material and energy 

flows for a Fischer–Tropsch GTL plant, including the option of a distillation column for primary fractionation of 

Fischer–Tropsch products, such as fractionation into lighter 

and heavier fractions, or naphtha, diesel, and paraffinic wax 

fractions. We do not include more complex refining (such 

as hydro-cracking technology used in larger scale petroleum refineries) mainly because the larger operating scale 

and capital requirements of such technology would make it 

economically prohibitive in the relatively smaller scale context of a pulp mill biorefinery. Thus, in our process model, 

there are essentially two options for production of Fischer–

Tropsch (FT) products, either FT crude, mainly consisting of mixed alkanes that would be shipped to a chemical 

refinery (e.g., a petroleum refinery) for further fractionation 

and processing, or the option of using a distillation column 

for primary fractionation to separate potentially saleable 

primary products such as diesel and naphtha, while shipping 

remaining fractions (e.g., paraffinic waxes) to a refinery for 

further processing.

Fischer–Tropsch (FT) synthesis is regarded as a catalyzed 

polymerization reaction involving the carbon monoxide 

(CO) and hydrogen (H2) reactants from syngas (Niemantsverdriet 2007). The initiation step of the polymerization 

reaction is catalytic adsorption and dissociation of carbon 

monoxide and molecular hydrogen. The propagation reaction on a metal catalyst consists of carbon coupling reactions that involve some CHX species, and the termination 

steps involve hydrogenation and desorption to yield alkanes, 

or simply desorption to yield olefins (alkenes). Different 

metal catalysts can be used, including even elemental iron 

or nickel, but with differing levels of reactivity. Cobalt or 

cobalt alloys are more expensive but much more efficient 

and are the preferred catalysts.

The precise series of carbon coupling events in the catalytic 

propagation reaction are not known with certainty (Niemantsverdriet 2007), but direct carbon-to-carbon coupling 

to form C2 is ruled out because it would be highly endothermic, and hence the coupling reactions are thought to involve 

CHX species. Studies of hydrogenation of carbon to methane 

suggest that once the first hydrogenation step occurs to form 

CH (methylidyne), it is also likely that CH2 (methylene) and 

to a lesser extent CH3 (methyl) are formed because activation energy barriers of the elementary hydrogenation steps 

are very similar. Thus, the carbon coupling reactions could 

in theory involve any or perhaps all of the CHX species 

(Niemantsverdriet 2007). Because of uncertainties about 

reactions, and also for simplicity, we do not model the FT 

synthesis reaction per se, but rather our model is simply 

based on parameter estimates regarding input requirements 

and conversion efficiencies. Appendix C provides more information on prototypical GTL plant design and costs.

Note also that Fischer–Tropsch liquids have superior properties such as much lower impurities (e.g., little or no sulfur 

or other inorganic chemicals) and the absence of benzene 

or other aromatic carbon compounds relative to analogous 

petroleum-based products. The FT products are mostly 

straight-chain (paraffinic) alkanes, and generally appear as 

crystal clear liquids or waxes similar to food grade paraffin wax. According to the U.S. Department of Energy, FT 

biodiesel is reported to have near zero sulfur content, very 

Figure 6. Material and energy flows in syngas heat recovery and 

cleanup.
Research Paper FPL–RP–660

12

high cetane, near-zero aromatics, almost wholly n-paraffin 

content, and low density. Those are properties that may allow FT products to be sold at a premium relative to similar 

conventional crude oil or petroleum distillate products.

Table 6 lists model parameters related to GTL and distillation and hypothetical input data for each parameter. Parameters related to GTL include maximum practical yield 

of FT crude, operational FT yield (percentage of practical 

maximum), steam energy recovery for GTL plant, higher 

heating value and weight of FT crude per barrel, water separation factor in GTL process, and electric power load for the 

GTL plant. Model parameters related to distillation include 

recovery rates for naphtha, diesel, and wax per barrel of FT 

crude, and the electric power load for distillation equipment. 

Additional parameters include unit prices (revenue values) 

for naphtha, diesel, wax, and FT crude. Product prices are 

based on 2011 diesel price projections from the reference 

case (mid-level price outlook) of the U.S. Energy 

Information Administration’s 2010 Annual Energy Outlook

(USDOE 2010), with assumptions that naphtha would sell 

at a lower price and paraffinic wax would sell at a higher 

price than diesel. In the hypothetical example, we assumed 

that naphtha, diesel, and paraffinic wax will be recovered 

by distillation of FT crude. Alternatively, the model can be 

structured so that FT crude is the end product (without distillation). FT crude would probably have a lower price than 

distillation products (as suggested in Table 6), but we had no 

actual market data or history of FT crude prices.

Figure 7 is the material and energy flow diagram for the 

GTL and distillation elements of the business case model. 

The Excel workbook model contains mathematical formulas to compute the material and energy flows, and Figure 7 

shows computed values based on the hypothetical input 

data in Table 6.

Table 6—Model parameters and input data related to GTL and distillation 

Parameter description Unit of measure 

Hypothetical 

data

GTL plant averages 

Maximum practical Fischer–Tropsch (FT) 

yield 

Biocrude bbl./clean syngas ton 0.970 

Operational FT yield % maximum FT yield 88 

Steam energy recovery factor MWHth steam/FT liquid bbl. 0.595 

FT liquid higher heating value MWHth/FT liquid bbl. 1.670 

FT liquid weight per barrel Tons/FT liquid bbl. 0.145 

Water separation factor Water tons/FT liquid tons 1.520 

Power load MWH/clean syngas ton 0.156 

Distillation column averages 

Naphtha recovery % FT crude (bbl./bbl.) 6.0 

Diesel recovery % FT crude (bbl./bbl.) 51.0 

Wax recovery % FT crude (bbl./bbl.) 43.0 

Power load MWH/day per bbl. FT crude 0.001 

Estimated average product prices 

Naphtha feedstock price (F.O.B.) $/bbl. (F.O.B. unit price) $125.31 

Diesel product price (F.O.B.) $/bbl. (F.O.B. unit price) $156.63 

Wax product price (F.O.B.) $/bbl. (F.O.B. unit price) $172.00 

FT crude liquid price $/bbl. (F.O.B. unit price) $100.00 

Figure 7. Material and energy flows for gas-to-liquid (GTL) and distillation.
Modeling Integrated Biomass Gasification Business Concepts

13

Energy System Upgrades

An integrated biomass gasification concept may include 

energy system upgrades to take advantage of synergies in 

combined heat and power production. Thus we designed the 

model so that capacity can be added for combined heat or 

power production, specifically added power boiler or combustion gas turbine capacity along with associated power 

generator and steam recovery capacity. Added boiler or gas 

turbine capacity may be based on combustion of surplus tail 

gas from the GTL plant or any type of additional purchased 

boiler fuel (the price of which is specified in Table 1). Added combustion capacity may also increase the output capacity of steam, power, or flue gas available for biomass drying.

The business process model uses conditional logic to allocate combustion and generating capacity, based on requirements for steam, power, and flue gas heat energy in the 

overall system. The model also determines whether added 

gas turbine and power boiler systems will be operated at 

lower or higher flue gas temperatures, the lower flue gas 

temperature (~ 137 °C/280 °F) increasing steam and power 

output, whereas the higher temperature (~ 371 °C/700 °F) 

yields hotter flue gas suitable for biomass drying but reduces 

steam and power output. In addition, the boiler and gas turbine shares of added capacity are specified as data inputs, 

so the model can represent any combination of added boiler 

or gas turbine capacity, with different specified steam and 

energy generating rates.

Table 7 lists model parameters related to energy system 

upgrades (added capacities) and hypothetical input data for 

each parameter. The model includes separate parameters 

for the gas turbine and boiler systems, with rates of steam 

and energy output at low and high flue gas temperatures, 

because any combination of such systems may be employed. 

Weighted average values are based on the specified gas turbine and boiler capacity shares (percentages of total 

turbine and boiler fuel inputs). The hypothetical case in 

Table 7 assumes that gas turbine capacity accounts for 100% 

of any added combustion capacity, so the “weighted average” values for all parameters are the gas turbine parameters. However, the relative percentages of boiler and gas 

turbine combustion capacity could be changed, which would 

change the weighted parameter averages.

Any of the input data can be modified to reflect alternative 

assumptions about steam or power output from the added 

gas turbine or boiler systems. In cases that do not include 

either steam or power-generating capacities, the corresponding parameter values can be set to zero. For example, in the 

hypothetical data (Table 7), the steam production capacity of 

the boiler is positive (at low and high flue gas temperatures), 

but electric power output associated with the boiler was set 

at zero, indicating that the boiler in this case would be used 

only to produce steam (not electricity).

Another energy system upgrade that is evaluated by conditional logic in the model is use of tail gas from the GTL process to substitute for natural gas as fuel in the lime kiln of 

the kraft pulp mill. The primary use of tail gas in the model 

is for fuel in biomass gasification (Fig. 5). However, if additional tail gas is available, then the model will allocate it 

to replace natural gas in the lime kiln, and any surplus beyond that may be used as fuel for added power boiler or gas 

turbine capacity. Lastly, the base case and business case process models compute overall electric power needs and outputs, which determine differences in volumes of purchased 

electric power or power sold to the power grid.

Figure 8 is the material and energy flow diagram for energy 

system upgrades (including added power boiler or gas turbine capacity, lime kiln fuel substitution, and electric power 

volumes). The Excel workbook contains mathematical 

Table 7—Model parameters and input data related to energy system

upgrades 

Hypothetical data 

Parameter description and unit of measure 

Gas

turbine Boiler 

Weighted

average 

Excess combustion air 220 20 220 

(% over stoichiometric air required) 

Flue gas energy at low temperature (280 °F) 0.186 0.130 0.186 

(MWHth flue gas/MWHth fuel input) 

Steam production at low flue temperature 0.459 0.710 0.459 

(MWHth steam/MWHth fuel input) 

Flue gas energy at high temperature (700 °F) 0.423 0.380 0.423 

(MWHth flue gas/MWHth fuel input) 

Steam production at high flue temperature 0.222 0.460 0.222 

(MWHth steam/MWHth fuel input) 

Electric power production 0.214 0.000 0.214 

(MWH/MWHth fuel input) 

Gas turbine and boiler capacity shares 100 0 — 

(% of total turbine and boiler fuel inputs) 
Research Paper FPL–RP–660

14

formulas to compute these material and energy flows using conditional logic, and Figure 8 shows computed values 

based on hypothetical input data in Table 7 and other hypothetical data presented earlier. In this case, there is actually 

no requirement for added power boiler or gas turbine capacity because the model and hypothetical input data do not result in additional needs for flue gas energy, steam, or electric 

power. Therefore, computed values for added boiler and gas 

turbine capacity in Figure 8 are zero, but in alternative cases 

the values could be positive. In this case, there is sufficient 

tail gas to provide all the fuel needed for biomass gasification (Fig. 5) plus replace almost all natural gas required for 

the lime kiln (Fig. 8).

Comparing energy flows in Figure 8 with the base case 

(Fig. 2), the total plant electrical load is larger for the facility 

with integrated biomass gasification (928 MWHth/day versus 506 MWHth/day) because of additional power loads of 

equipment for biomass gasification and biofuel production. 

However, the increase in power load is offset to some extent 

by increased power output, which mainly is due to the steam 

output from syngas heat recovery. Therefore, the overall 

plant remains a net producer of electric power (49 MWH 

sold to the power grid). Although the total power load of the 

facility increases by 422 MWHth/day, the net output of electric power is only reduced by 265 MWHth/day.

Overall waste heat and steam-condensing heat losses are 

higher for the hypothetical facility with integrated biomass 

gasification (1,339 MWHth/day versus 874 MWHth/day), 

mainly because of additional excess steam generated by the 

GTL plant and syngas heat recovery. On the other hand, tail 

gas from the GTL plant provides all the fuel energy required 

for biomass gasification (1,124 MWHth/day), and tail gas 

substitutes for almost all the natural gas that was required 

for the lime kiln (521 MWHth/day). In addition, as shown in 

Figure 7, the GTL plant also produces energy output in the 

form of FT crude (2,210 MWHth/day).

Cash-Flow Model

The Excel workbook includes a cash-flow model to evaluate 

investment in the integrated biomass gasification business 

concept. The model is based on a cash flow model originally 

developed to evaluate capital investments in the logging 

industry (Bilek 2007). The model computes annual gross 

margins from daily operating revenue and cost estimates in 

the process models, and also takes into account specified 

capital costs and annual operating and maintenance (O&M) 

costs for the biomass gasification equipment and added energy equipment represented in the business case model. In 

addition, the cash-flow model includes specified tax rates, 

tax credits, and financing arrangements for the capital investment. The model computes before-tax and after-tax rates 

Figure 8. Material and energy flows related to energy system upgrades.
Modeling Integrated Biomass Gasification Business Concepts

15

of return and net present value of investment in the business 

concept.

Annual Operating Costs and Revenue

The Excel workbook automatically computes projected operating revenue (or cost) impacts of the integrated biomass 

gasification concept by using projected daily revenue and 

operating costs from the process models and a specified 

number of operating days per year. The operating revenue 

(or cost) impact takes into account projected change in the 

gross margin of the overall enterprise that results from introduction of the integrated biomass gasification concept. Thus, 

the annual operating revenue (or cost) impact is the difference between annual gross margins for the business case 

and the base case, minus annual O&M costs for the added 

biomass gasification and energy equipment, and also minus 

any other specified incremental process operating costs 

(Eq. (1)).

Annual Incremental Operating Revenue (or Cost) Impact 

= (Business Case Gross Margin) – (Base Case Gross Margin)

– (O&M Costs for Gasification and Energy Equipment)

– (Other Incremental Process Operating Costs) (1)

Table 8 shows annual operating revenue, operating costs, 

and gross margin for the base case production facility if operating at full capacity, as computed from hypothetical data 

in Table 1 and material and energy flows shown in Figure 2. 

Annual revenue and costs in Table 8 are based on a specified 

assumption of 350 operating days per year, which is typical 

for pulp and paper facilities that normally schedule only a 

couple of weeks per year for maintenance downtime. Annual revenue and costs are generally computed by multiplying 

operating days per year times the daily material flow 

(Fig. 2) and times the unit price of product output or 

material input. For example, paper product revenue of 

$452,812,500 per year (Table 8) is the product of 350 

days per year times average product output of 1,725 tons 

per day (Fig. 2), times the average product price of $750 

per ton (Fig. 2). Likewise, the annual cost for pulpwood 

of $78,294,648 per year is the product of 350 days per 

year times the computed average pulpwood input flow of 

6,883.046 green tons per day (Fig. 2), times average pulpwood price of $32.50 per green ton (Fig. 2). The gross 

margin is the difference between total operating revenue 

and total operating costs.

In general, the gross margin for any business enterprise is 

defined as its revenue contribution after paying for direct 

costs of production (fixed and variable costs). Thus, gross 

margin as shown in Table 8 is based on revenue and costs 

that are directly attributable to production, and does not 

include indirect costs (such as capital costs) or taxes. Gross 

margin is therefore not the same as profit margin, but it is 

useful in the model for computing the incremental revenue 

effect of biomass gasification based on the difference in 

gross margin between the base case and the business case 

with biomass gasification.

Table 9 shows the similarly computed annual operating revenue, operating costs, and gross margin for the business case 

production facility (with integrated biomass gasification) 

if operating at full capacity. The values in Table 9 are computed from the hypothetical input data in Tables 1 to 7, and 

the material and energy flows shown previously in Figures 3 

to 8. In this hypothetical case, the gross margin for the business case concept is about 57 million dollars higher than the 

base case (operating at full capacity and not yet taking into 

account the O&M costs for the additional biomass gasification and energy equipment).

Table 10 shows additional operating and maintenance 

(O&M) costs associated with introduction of integrated 

Table 8—Annual operating revenue, costs, 

and gross margin for base case 

Operating revenue ($) 

Paper products 452,812,500 

Soap and turpentine 4,183,200 

Electric power to grid 5,492,866 

Wood products 37,800,000 

Total 500,288,566 

Operating (variable) costs 

Pulp and paper mill operating costs 211,312,500 

Wood mill operating costs 15,750,000 

Pulpwood 78,294,648 

Logs (for wood products) 30,568,909 

Purchased natural gas 4,921,326 

Total 340,847,383 

Gross margin 159,441,183 

Table 9—Annual operating revenue, costs,

and gross margin for business case 

Operating revenue ($) 

Paper products 452,812,500

Soap and turpentine 4,183,200

Electric power to grid 859,830

Wood products 37,800,000

Naphtha 3,340,423

Diesel 35,491,689

Wax 32,860,824

FT crude (optional) 0 

Total 567,348,466

Operating (variable) costs 

Pulp and paper mill operating costs 211,312,500

Wood mill operating costs 15,750,000

Pulpwood 78,294,648

Logs (for wood products) 30,568,909

Logging residue 10,202,500

Other purchased biomass 4,637,500

Purchased electric power 0 

Purchased boiler fuel 0 

Purchased natural gas 1,755

Total 350,767,812

Gross margin 216,580,654
Research Paper FPL–RP–660

16

biomass gasification and energy system upgrades, based 

on hypothetical cost factors. These O&M costs would include, for example, added labor and maintenance costs for 

new equipment operation (the business case process model 

already accounts for energy and material costs). O&M cost 

factors are specified as data inputs for each principal new 

equipment area, including biomass preparation and drying, 

biomass gasification and gas cleanup, GTL plant and distillation, and combined heat and power systems. As shown in 

Table 10, O&M cost factors are specified per unit of process 

throughput in each area (e.g., per ton of biomass, per ton 

of FT crude output, or per MWH of added energy output 

capacity). The Excel workbook automatically computes annual O&M costs (as shown in Table 10) by multiplying the 

input cost factors times the process throughput volumes (as 

determined by the business case process model). Any other 

projected change in existing facility O&M costs and other 

additional O&M costs can also be specified as annual costs 

to reflect any other changes in O&M costs associated with 

integrated biomass gasification.

In addition to O&M costs in Table 10 (primarily labor and 

maintenance costs for added equipment), other annual direct 

or indirect costs can also be specified for the business case 

model, including other direct biorefining costs, insurance 

costs for added equipment (as a percentage of average capital investment for added equipment), other fixed costs, and 

major periodic operating costs that occur at intervals longer 

than one year (such as major equipment overhauls or maintenance), with the specified time interval for major periodic 

operating costs. These other direct and indirect cost parameters that can be specified for the business case model are 

listed in Table 11, along with hypothetical data inputs.

Capital Costs

The capital costs for the business case model are the initial (year “0”) costs for purchase and installation of new 

integrated biomass gasification equipment, energy system 

upgrades, and related infrastructure. Capital cost parameter 

values are specified for each category of new equipment or 

new capacity, including biomass preparation and drying, 

biomass gasification, heat recovery and gas cleanup, GTL 

and distillation plant, added steam turbine capacity, added 

gas turbine and generator capacity, added facility infrastructure (such as added steam or power load handling capacity), 

added gas power boiler capacity, added wood or bark boiler 

and steam capacity, and added biomass handling capacity 

for the wood/bark boiler. Table 12 lists capital cost parameters of the business case model, along with hypothetical 

data values for each parameter.

Generally the capital cost parameters and input data are 

specified per unit of throughput in each area (such as dollars 

per daily ton of biomass input to the gasifier, or dollars per 

daily ton of FT liquid output). Generally such parameter values will vary with equipment capacity because of economies 

of scale. Capital cost parameter values can be modified to 

reflect alternative equipment assumptions or different scales 

of production.

The Excel workbook automatically computes capital costs 

(right hand column of Table 12) by multiplying the parameter values times the throughput volumes as computed by 

the business case process model (e.g., daily tons of dry biomass to gasifier as shown in Fig. 4, daily tons clean syngas 

and FT crude output as shown in Fig. 7). The exception to 

this proportional rule is the economy of scale that is assumed for the biomass gasifier, the wood/bark boiler, and 

the gas boiler. Appendix A describes the scaling formula for 

these exceptions to calculate their capital costs. In addition, 

the Excel workbook is equipped with conditional logic that 

will eliminate capital costs if not required in a particular 

case. For example, although hypothetical parameter values 

are specified for all categories of capital costs, as shown 

in Table 12, the computed capital costs are zero for added 

gas turbine and generator capacity, added gas power boiler 

capacity, added wood or bark boiler capacity, and added 

Table 10—Additional operating and maintenance (O&M) costs for business 

casea

Process area 

Cost

factors 

($)

Annual

O&M costs 

($)

Biomass preparation and drying 4.00/daily tons dry biomass 1,437,720 

Biomass gasification and gas cleanup 6.00/daily tons dry biomass 2,156,579 

Gas to liquid and distillation 5.00/daily bbl. FT crude output 2,221,527 

Combined heat and power 6.00/daily MWH energy output 0 

Change in existing facility O&M costs — 0

Other additional O&M costs — 0

Total annual O&M cost — 5,815,826 

a

Hypothetical input data for cost factors shown in bold text. 

Table 11—Other cost parameters of the business case 

model 

Parameter Hypothetical data 

Other direct biorefining costs $200,000/year 

Annual insurance costs 2.0% of average capital investment

Other fixed costs $4,000/year 

Periodic operating costs $15,000/year 

Periodic operating life 3 years 
Modeling Integrated Biomass Gasification Business Concepts

17

biomass handling capacity for the wood/bark boiler, because 

no added capacity is required for those elements in the hypothetical case (Fig. 8).

Taxes, Credits, and Incentives

Table 13 is a list of various input parameters that can be 

specified in the cash-flow model to control the treatment of 

taxes, tax credits, and other related incentives, along with 

hypothetical inputs for each of the parameters. Any of the 

parameters pertaining to taxes, credits, or incentives can be 

modified to reflect alternative assumptions.

Economic Assumptions and Financing 

Options

The business case cash-flow model includes a number of 

parameters that pertain to basic economic assumptions 

and investment financing options. The basic economic 

assumptions include the economic life of the investment, 

projected inflation rates for costs and revenue over the economic life, and the facility operating rate during the first 

year. The model assumes the facility may operate at less 

than full capacity in the year that the integrated biomass 

gasification system is installed but will ramp up to full capacity in the second year of operation.

The model is quite flexible with regard to financing options. Model parameters related to financing options include 

the deposit interest rate, risk premium on invested capital, 

initial gearing ratio (percentage of initial capital cost that is 

financed with borrowed capital), required number of loan 

and deposit payments per year, the structure of the financing 

(a conventional loan or a custom loan), the loan term, which 

can be shorter than the project’s economic life, and the loan 

interest rate, which can be specified as a fixed or variable 

interest rate. A conventional loan is repaid with fixed 

Table 12—Capital cost parameters and hypothetical data for business case 

Capital cost category 

Parameters 

and hypothetical values 

Capital 

costs 

($)

Biomass preparation and drying $22,393/daily ton dry biomass to gasifier 22,973,327 

Biomass gasification $72,569/daily ton dry biomass to reformer 73,974,979 

Heat recovery and gas cleanup $13,776/daily ton clean syngas output 20,482,527 

GTL and distillation plant $220,286/daily ton FT liquid output 40,547,903 

Added steam turbine capacity $4,075/daily ton high pressure steam 1,417,050 

Added gas turbine and generator $15,873/daily MWHth gas turbine fuel 0 

Facility infrastructure $33,460/daily ton dry biomass to gasifier 34,327,135 

Added gas power boiler capacity $3,379/daily MWHth boiler fuel input 0 

Added wood/bark boiler capacity $6,757/daily MWHth boiler fuel input 0 

Added biomass handling for boiler $35,149/daily dry ton added biomass 0 

Total capital costs — 193,722,922 

a

Hypothetical input data for cost factors shown in bold text. 

Table 13—Parameters and hypothetical inputs for taxes, credits, and incentives 

Parameter 

Hypothetical 

inputs

Federal effective income tax rate 35% 

State effective income tax rate 10% 

Treatment of tax loss Flow through 

(Flow through, carry forward, or none) 

Renewable electricity production tax credit (PTC) $0.010 per kWh 

Years that PTC remains applicable 5 

Ad valorema

 (property) tax mill rate 30 

Ad valorem (property) tax valuation basis Average capital invested (ACI) 

(Average capital invested, straight-line value, or custom) 

Income tax depreciation method Declining balance (DB) 

(Declining balance, straight line general depreciation system (GDS), 

 straight line alternative depreciation system (ADS), or custom) 

Declining balance factor 200% 

(Either 200% or 150%) 

Special first-year depreciation allowance (%) 0 

GDS life (years) 7 

ADS life (years) 10 

Development grant(s) total $ value 0 

Taxability of development grant (Y/N) Yes 

a

Ad valorem is Latin for “according to value.”
Research Paper FPL–RP–660

18

Table 14—Parameters pertaining to economic and financing 

assumptions 

Parameter 

Hypothetical 

inputs

Economic life of business case investment 15 years 

Revenue inflation rate 1.86% per year 

Cost inflation rate 1.86% per year 

Facility operating rate in first year ramp up to full capacity 75% of full capacity 

Index biorefining revenues to inflation (Y/N) Yes 

Index biorefining costs to inflation (Y/N) Yes 

Index salvage estimate to inflation (Y/N) Yes 

Annual operating days per year 350 

Initial gearing ratio (% of total capital cost financed by loan) 40 

Loan term 8 years 

Loan and deposit and payments per year 12 

Deposit interest rate (APR) 3.00% 

Expected risk premium on invested capital 9.00% 

Loan interest rate 7.00% 

Loan interest type (fixed/variable) Fixed 

Figure 9. Cash flow tableau for incremental business case investment.
Modeling Integrated Biomass Gasification Business Concepts

19

payments at the specified loan interest rate over the specified 

term. A custom loan can be structured any way the analyst 

wishes with interest payments and principal repayments 

entered for each year. Using the custom loan option, the 

analyst can explore the effect of different financing patterns 

(e.g., an interest-only loan for a specified number of years 

with a refinancing into a conventional loan).

Table 14 lists the parameters pertaining to economic and 

financing assumptions that can be specified in the cash-flow 

model, along with hypothetical inputs for each parameter. 

Note that the revenue and cost inflation rates in the sample 

data are based on inflation rate projections of the U.S. Energy Information Administration’s 2010 Annual Energy Outlook (USDOE 2010). Also, if user selections mean that some 

parameters are not needed, then these cells will receive gray 

shading. For example, if the loan repayment schedule is 

“Custom,” then the loan term and the parameters relating to 

the loan interest rate will be grayed out.

Of course, any of the parameter values can be modified to 

reflect alternative assumptions. Users will probably want to 

pay particular attention to the interest rate, financing, and 

risk premium assumptions.

Incremental Cash Flows

The cash-flow model in the Excel workbook automatically 

computes projected annual incremental cash flows for the 

business case investment, based on data inputs and process 

model computations. The projected cash flows are displayed 

in a tableau that provides a complete pro-forma accounting 

of all annual cash flows over the economic life of the investment in nominal dollars (after taking into account specified 

inflation rates). The annual cash flows include projected 

incremental revenue impacts, fixed costs and capital costs, 

cash flows related to financing, net cash flows before tax, 

tax adjustments, and after-tax cash flows.

Figure 9 shows the annual cash flow tableau for the incremental investment as computed by the cash-flow model in 

Figure 9. (con.) Cash flow tableau for incremental business case investment.
Research Paper FPL–RP–660

20

the Excel workbook taking into account specified inflation 

rates. Cash flows shown in Figure 9 are based on the hypothetical input data, process computations, and other financial 

information displayed previously (in Tables 1 to 14 and 

Figs. 2 to 8). Of course, changing any of the model parameter values or financial assumptions will likely change some 

of the projected incremental cash flows for the business case 

investment.

The cash flow tableau (Fig. 9) presents cash flows as end-ofyear values. Input data on prices and costs should pertain to 

values at the end of the first year of the investment (end of 

year “1”), while all capital cost factors should pertain to the 

beginning of year “1” (or end of year “0”). Thus, the first 

year displayed in the tableau is year “0” (or specifically the 

end of year “0”), which is when the initial capital investment outlays are assumed to be made and establishment of 

initial financing accounts such as the loan principal are assumed to occur. Note that the total capital investment cost 

(year “0” value) is $193,722,922, which is identical to the 

total capital costs shown previously in Table 12, whereas 

the loan principal amount is $77,489,169, which is exactly 

equivalent to a gearing ratio of 40% as specified in Table 14.

Incremental annual revenue and cost cash flow impacts such 

as the impacts on annual operating revenue, fixed annual 

costs, financing payments, and net cash flows are all displayed in the tableau as end-of-year values for each 

year over the specified economic life of the investment 

(15 years in the hypothetical case example). For example, 

the incremental process operating revenue cash flow for 

year 1 ($42,854,603 shown in Fig. 9) is the difference in 

gross margin between the business case gross margin (Table 

9) and base case gross margin (Table 8), or $57,139,471, 

multiplied times the specified facility operating rate during 

the first year of operation, or 75% (Table 14). The specified 

revenue and cost inflation rates (Table 14) are applied to the 

cash flows to compute their end-of-year values in each subsequent year. For example the incremental process operating 

revenue at full capacity in year 2 is $58,202,265, which is 

the $57,139,471 difference in gross margins, adjusted for the 

specified annual revenue inflation rate of 1.86% (Table 14).

Similarly, the incremental process operating and maintenance (O&M) cost for year 1 ($4,361,869 in Fig. 9) is the 

total annual O&M cost from Table 10 ($5,815,826) multiplied times the specified facility operating rate during the 

first year of operation, or 75% (Table 14). Likewise, the 

incremental process operating and maintenance (O&M) cost 

in year 2 ($5,924,000 in Fig. 9) is the total annual O&M 

cost from Table 10 adjusted for the cost inflation rate of 

1.86% as specified in Table 14. Revenue and costs for subsequent years are similarly adjusted for the specified inflation rates.

The net cash flow each year (as shown in Fig. 9) is the sum 

of the total operating revenue or cost impacts minus all 

fixed costs. Tax adjustments for annual depreciation and taxable gain (or loss) on salvage are then deducted to arrive at 

the taxable cash flow, which is used to compute taxes due, 

based on the combined income tax rate as derived from the 

specified state and federal income tax rates (Table 13). The 

combined income tax rate formula is the sum of the state 

and federal rates, minus the product of those rates (or 41.5% 

in the hypothetical case, based on specified state and federal 

rates of 10% and 35%, respectively). The after-tax cash flow 

is then computed as the net cash flow minus taxes due. Note 

that tax credits are automatically highlighted in yellow 

background (as shown for the tax credits of years 1 to 5 

in Fig. 9) as a warning that they would have to be carried 

forward if there is insufficient taxable income to which the 

credits can be applied. If the user chooses “flow through” as 

the tax loss treatment method, then the model assumes there 

is sufficient taxable income such that the credits can be applied immediately. The “carry forward” method carries tax 

losses forward until there is sufficient income to offset them.

Measures of Performance and Sensitivity

The before-tax and after-tax cash flows are combined with 

capital investment costs and financing assumptions to compute standard measures of economic performance for the 

business case investment, including discounted net present 

value (NPV) and internal rate of return (IRR).

The discount rates used in the model for the NPV calculations are a combination of the deposit interest rate and the 

expected risk premium from Table 14. The deposit interest rate is used as a proxy for the risk-free rate. The rate is 

entered as an annual percentage rate. The model adjusts it 

for the number of payments per year to convert it into an 

equivalent annual interest rate.

The expected risk premium on invested capital is added to 

the adjusted deposit interest rate to arrive at the nominal 

before-tax and finance required rate of return on invested 

capital. This discount rate is used to calculate the before-tax 

and finance net present value. The loan interest rate and the 

nominal before-tax and finance required return on invested 

capital are weighted by the gearing ratio and the equity investment ratio, which is one minus the gearing ratio, to determine the before-tax nominal required return on invested 

capital. This discount rate is used to calculate the before-tax 

net present value. The loan interest rate is adjusted for the 

effect of taxes to arrive at the after-tax nominal required return on invested capital. This discount rate is used to calculate the after-tax net present value. The required returns on 

invested capital are shown in Figure 10.

Generally, the most relevant measures of performance for 

an investor are those computed after taxes on the basis of 

invested equity capital (i.e., the portion of investment capital 

that is not borrowed). However, in the model the NPV and 

IRR measures are computed on an equity basis both before 

and after taxes, and also on a total capital basis (before taxes 
Modeling Integrated Biomass Gasification Business Concepts

21

and without financing). In addition, IRR values are computed on the basis of nominal cash flows (which include inflation, as shown in Fig. 9) and on a real dollar basis (without inflation adjustments). Figure 10 shows the projected 

measures of performance and other financial information as 

computed and displayed in the Excel workbook.

As shown in Figure 10, the hypothetical business case 

investment has a projected after-tax net present value of 

$97,752,652, which means the discounted present value of 

projected after-tax cash flows from year 1 to year 15 exceeds the amount of equity invested (year “0” after-tax cash 

flow) by $97.8 million. The after-tax internal rate of return 

on the equity is projected to be 21.4% based on nominal 

cash flows (with inflation), and 19.2% on a real dollar basis 

(without inflation). These may seem like attractive results, 

but changing any of the model input data or financial assumptions will of course change the projected financial 

performance results. The projected financial performance 

measures shown in Figure 10 were computed on the basis of 

hypothetical input data, computed process energy and material flows, and projected cash flows described previously 

(Tables 1 to 14 and Figs. 2 to 9).

Given the likelihood of uncertainty about many specific 

model inputs, it is useful to be able to evaluate sensitivity 

of model results to changes in various model input data or 

model assumptions. Sensitivity analysis is readily performed 

with the Excel workbook by varying any of several sensitivity factors in the cash flow parameters table 

(Table 15).

By simply varying the sensitivity factors from 120% to 

80%, the sensitivity analysis shows in this case that returns 

are more sensitive to changes in revenue than to similar 

changes in costs. Also, changing variable operating costs has 

a larger impact in this case on overall returns than changing 

fixed operating costs.

A more detailed sensitivity analysis can be performed by 

varying model input values and observing corresponding 

changes in performance measures. Table 16 provides results 

of such sensitivity analysis, showing sensitivity of after-tax 

Figure 10. Measures of financial performance and other financial information for the business case investment.

Table 15—Sensitivity of after-tax net present value (NPV) and 

nominal internal rate of return (IRR) to change in the 

operating cost and revenue sensitivity factors 

NPV 

(×106

 $) 

Nominal IRR 

(%)

Base performance levels 97.8 21.4 

Sensitivity of NPV 

and IRR to changes 

in data inputs 

+20% –20%

Model parameters 

NPV 

(×106 $)

IRR

(%)

NPV

(×106 $)

IRR

(%)

Fixed operating costs sensitivity factor 92.1 20.8 103.4 22.1 

Variable operating costs sensitivity factor 87.2 20.2 108.3 22.6 

Revenue sensitivity factor 156.3 27.9 39.2 14.3 
Research Paper FPL–RP–660

22

net present value (NPV) and nominal after-tax internal rate 

of return (IRR) to +20% and –20% changes in initial data 

input values for a selected set of model parameters and input 

assumptions.

The sensitivity analysis reveals that financial results are 

more sensitive to changes in some parameter values and 

somewhat less sensitive to others. Relative sensitivity to 

different parameters can be viewed also in a sensitivity 

chart such as Figure 11, which plots the sensitivity results 

for NPV from Table 16. Figure 11 shows the sensitivity of 

after-tax nominal NPV to changes of plus or minus 20% in 

selected process parameters. The NPV results in this case 

for example are more sensitive to +/– 20% changes in diesel 

product price, FT yield, total capital costs, and purchased 

biomass price, but somewhat less sensitive to the same 

percentage changes in thermal energy required for biomass 

gasification, energy required for biomass drying, or paper 

production.

By doing a “worst case scenario” (e.g., fixed operating and 

variable operating costs up by 20% and revenue down by 

20%), it is possible to get a feeling for the likelihood of 

losing money on the venture. Using the sample data and 

increasing the fixed and variable operating costs to 120%, 

while reducing revenue to 80% yields an after-tax NPV of 

$23.0 million and a nominal IRR of 12.2%. A more explicit 

understanding of the risks involved can be achieved by conducting a more comprehensive stochastic risk assessment.

Risk Analysis and Stochastic Risk 

Assessment

Risk analysis in business generally refers to techniques 

that seek to identify and analyze various risk factors that 

could jeopardize the success of a business enterprise. Risk 

management refers to evaluation and prioritization of risks 

followed by coordinated efforts to minimize or control their 

negative impacts.

Investment managers also may be concerned with financial 

risks at a broader scale, not just the business risks of one 

particular business project. For example, investment risks 

are generally regarded as a composite of financial exposure 

risk and the inherent business risks. Financial exposure risk 

can include the scale of an investment outlay, financial liabilities in relation to assets, and liquidity of an investment. 

On the other hand, the business risks relate primarily to 

predictability or uncertainty regarding the business performance as influenced by both internal and external risk 

factors.

For investments in production facilities, internal risk is 

most often associated with the engineering performance 

and efficiency of the production processes, while external 

risk usually pertains to economic factors outside of the business enterprise such as global commodity or energy prices. 

Sensitivity analysis (as described in the preceding section) 

reveals that both internal and external business risks may be 

Table 16—Sensitivity of after-tax net present value (NPV) and nominal 

internal rate of return (IRR) to change in selected data inputs 

NPV 

(×106

 $) 

Nominal IRR 

(%)

Base performance levels (Fig. 10) 97.8 21.4 

Sensitivity of NPV 

and IRR to changes 

in data inputs 

+20% –20%

Model parameters (and initial inputs) 

NPV 

(×106

 $)

IRR

(%)

NPV 

(×106 $) 

IRR

(%)

Paper mill production 99.3 21.6 92.1 20.7

(1,725 tons per day (Table 1)) 

Purchased biomass price 82.6 19.7 113.0 23.2

($26.50 per green ton (Table 2)) 

Biomass drying energy required 87.8 20.3 97.8 21.4

(1.001 MWHth/ton water removal (Table 3)) 

Gasification thermal energy required 86.9 20.2 100.0 21.5

(1.096 MWHth/dry ton biomass feed average 

(Table 4)) 

 

Maximum practical FT yield 131.8 24.7 42.1 14.7

(0.970 biocrude bbl./clean syngas ton (Table 6)) 

Diesel product price (F.O.B.) 134.1 25.5 61.4 17.1

($156.63/bbl. F.O.B. unit price (Table 6)) 

Total capital costs 69.2 16.7 126.3 28.1

($193,722,922 (Table 12)) 
Modeling Integrated Biomass Gasification Business Concepts

23

associated with investment in a biomass gasification business concept. The sensitivity analysis revealed, for example, 

that internal risk such as uncertainty about maximum practical FT yield and external risk such as variability or uncertainty in diesel product price are both potentially important 

sources of risk to financial performance of the concept.

Stochastic risk assessment is an adjunct to risk analysis and 

risk management. It generally refers to determination of the 

level of risk associated with a recognized hazard. Sensitivity analysis can be a first step in assessing risks inherent in 

biomass gasification business concepts, helping to identify 

production parameters or economic variables that most influence financial performance of the business concept. With 

a more rigorous and time-consuming sensitivity analysis for 

example, it may be possible to assess financial performance 

risks associated with expected variability among each of the 

production parameters and economic variables in the business concept model.

However, a drawback or limitation of such sensitivity analysis is that it will not project the combined or overall distribution of financial performance associated with simultaneous 

variability among multiple parameters or all of the parameters together. On the other hand, multivariate stochastic simulation is a recognized technique that can be used to project 

the overall performance risk associated with uncertainty or 

variability among many parameters simultaneously.

Multivariate Stochastic Simulation

Multivariate stochastic simulation is described in this section, which shows how simulation can be used to assess the 

overall business risk related to multiple parameters simultaneously. We introduce hypothetical assumptions about 

projected ranges or probability distributions of selected 

parameters of the process and cash-flow models to demonstrate how a projected distribution of financial outcomes can 

be simulated based on the projected distributions of the underlying model parameters. The distributional assumptions 

in this case are not based on scientific observations, but are 

hypothetical estimates for illustrative purposes. In general, 

projected parameter distributions may be based on subjective perceptions of risk or uncertainty as well as objective 

variability determined by engineering experience or scientific data. Although objective data are always preferred for 

obvious reasons, subjective estimates of risk in parameter 

values may be used in early stages of feasibility analysis to 

model the influence of variability in multiple parameters on 

the financial results.

Multivariate stochastic simulation is useful for assessing 

overall business risk that stems from uncertainty or variability among multiple parameters simultaneously. This is 

especially useful in cases where business performance is 

determined by multiple parameters, many of which may 

present difficulty in their predictability, but where at least 

some projected range or projected distribution of likely 

parameter values can be specified. In stochastic simulation, 

a set of parameter values is randomly selected from the 

specified distributions of parameter values and investment 

performance is computed for that random set of parameters. 

Random selection and computation are repeated many times 

to produce a projected distribution of financial outcomes. 

This type of multivariate stochastic simulation is one form 

of a general technique known as Monte Carlo simulation. 

Figure 12 is a conceptual view of this technique as applied 

to production process and cash-flow models used in investment analysis.

Figure 11. Sensitivity of after-tax NPV to ± 20% change in parameters.
Research Paper FPL–RP–660

24

Various computer programs are available to assist in performing Monte Carlo or multivariate stochastic simulation, 

and various commercially available software packages can 

perform multivariate stochastic simulations on models built 

in Excel workbooks. We used one such program, @RISK 

version 4.5, which is distributed by the Palisade Corporation 

(Ithaca, New York). We used @RISK to run multivariate 

stochastic simulations of investment performance based on 

our Excel model of the integrated biomass gasification business concept. The @RISK software allows the user to specify different types of distributional assumptions for model 

input parameters, ranging from uniform data distributions to 

various types of statistical distributions. After the user specifies distributional assumptions for input parameters, the user 

can run a Monte Carlo simulation with the Excel model to 

compute the projected distribution of model results (financial performance measures) based on all of the distributional 

assumptions for input parameters. As illustrated conceptually in Figure 12, the simulation translates distributional 

assumptions about input parameters into the projected distribution of investment performance.

To provide an example of risk assessment based on multivariate stochastic simulation, we developed hypothetical distributions of values for selected parameters in the 

integrated biomass gasification models. To compare risk 

assessment with sensitivity analysis, we focused the risk assessment on the same set of parameters we used to illustrate 

sensitivity analysis (Table 16). Of course, any other set of 

model parameters may be selected as the focus of risk assessment, not just those that we selected here.

Table 17 lists model parameters selected for stochastic simulation, along with hypothetical distributions that we specified for each parameter. We specified a uniform distribution 

for projected paper mill production, with a range from 1,600 

to 1,700 tons per day of average output volume. This range 

of projected production is somewhat less than the mill capacity (1,725 tons per day) on the assumption that variable 

market demands would reduce average mill output volume 

to something less than mill capacity on average (here the 

range is from about 93% to 99% of capacity). The uniform 

distribution means average production volume over the life 

of the investment could assume any value within that range 

at a uniform or equal likelihood of occurrence.

For many of the other model parameters, we specified a normal distribution, which tends to be a reasonable assumption 

for many economic or engineering parameters (although of 

course other distributional assumptions can be used if more 

accurate or better supported by actual data). The normal distributions are defined by simply specifying their projected 

mean values and standard deviations. The width of a 95% 

confidence interval is approximately two standard deviations on either side of the mean value for a normally distributed parameter. For example, for purchased biomass price 

we specified a hypothetical mean value of $26.50 per ton 

and a standard deviation of $2.50 per ton, which means that 

average biomass price over the life of the investment (before 

inflation) is projected to have approximately a 95% likelihood of being within a range from around $21.50 to 

$31.50 per ton (with a central tendency or higher likelihood 

of being closer to the mean value). For most of the variables 

that were assumed to be normally distributed, the hypothetical standard deviation was specified to be 10% of the mean 

value. However, in the case of diesel product price, the 

mean value and standard deviation were based on energy 

price projections from the 2009 Annual Energy Outlook 

(Energy Information Administration, U.S. Department of 

Energy).

It is possible, of course, to project other alternative distributions, including uneven distributions, such as the Weibull 

distribution. For example, we specified a Weibull distribution for total capital costs, using a distributional adjustment 

with Weibull parameters of α = 1.5 and β = 0.5, and minimum value at 70% of the initial capital cost. This resulted 

in a skewed distribution for projected capital costs, ranging 

from 30% below the initial capital cost estimate to a wider 

range of values above that estimate. This type of skewed 

distribution could be used, for example, to reflect a subjective perception that actual capital costs would likely escalate 

(or would have been underestimated) rather than vice versa. 

Under the Weibull distribution assumptions, the mean value 

Figure 12. Conceptual view of simulation applied to investment analysis.
Modeling Integrated Biomass Gasification Business Concepts

25

of projected capital costs becomes $224 million, and the 

95% confidence interval extends from $149 million (or 77% 

of initial capital cost) to $337 million (174% of initial capital cost).

Figure 13 illustrates the hypothetical probability density 

charts and shows the 95% confidence intervals for each of 

the parameters, as specified by the distributional assumptions in Table 17.

Using the @RISK software, we ran a multivariate Monte 

Carlo stochastic simulation of investment performance 

based on our Excel model of the integrated biomass 

gasification business concept along with all of the distributional assumptions for selected parameters as specified 

in Table 17 and Figure 13. The Monte Carlo simulation 

was run with 1,000 iterations. Figure 14 shows simulation 

Table 17—Selected model parameters and hypothetical distributions 

Model parameters (and initial values) Hypothetical distributions 

Paper mill production Uniform Range: 1,600 – 1,700 

(Tons per day (Table 1)) 

Purchased biomass price Normal Mean: 26.50, St. Dev.: 2.50 

($ per green ton (Table 2)) 

Biomass drying energy required Normal Mean: 1.001, St. Dev.: 0.10 

(MWHth/ton water removal (Table 3)) 

Gasification thermal energy required Normal Mean: 1.096, St. Dev.: 0.11 

(MWHth/dry ton biomass feed average 

(Table 4)) 

 

Maximum practical FT yield Normal Mean: 0.970, St. Dev.: 0.10 

(Biocrude bbl./clean syngas ton (Table 6)) 

Diesel product price (F.O.B.) Normal Mean: 156.63, St. Dev.: 30.91 

(($) per bbl. F.O.B. unit price (Table 6)) 

Total capital costs Weibull α = 1.5, β = 0.5, shift = 70% 

($194 million (Table 12)) 

Figure 13. Probability density charts and 95% confidence intervals for hypothetical parameter distributions.
Research Paper FPL–RP–660

26

results, specifically a histogram of simulated after-tax net 

present value (NPV). As illustrated by the histogram, the 

simulation sample suggests a 13% chance (12.67%) that 

the simulated NPV will be less than zero, an indicator 

of risk to NPV associated with all of the parameter 

distributions.

Note also that the simulated distribution of projected NPV is 

somewhat skewed (the distribution has a “tail” that extends 

to the left of the mean value). The skewed nature of the outcome distributions is mainly a result of the uneven Weibull 

distribution assumption for total capital costs. The low end 

tail of the simulated NPV distribution (Fig. 14) corresponds 

to the high end tail of the total capital cost distribution 

(Fig. 13).

Figure 15 shows additional results of the Monte Carlo simulation, specifically a histogram of the simulated after-tax 

internal rate of return (IRR). As illustrated in Figure 15, the 

results suggest a roughly 38% risk that IRR will be less than 

15% (but less than 1% risk that the IRR would be less than 

zero).

Unlike results of the sensitivity analysis that show effects 

of variability or uncertainty for one selected parameter at a 

time (Table 16 and Fig. 11), the multivariate stochastic simulation results (Figs. 14 and 15) provide a more comprehensive assessment of overall investment risks that arise from 

variability or uncertainty among all selected parameters 

simultaneously. In other words, the simulation results 

(Figs. 14 and 15) incorporate all the distributional assumptions for the selected model parameters (Table 17 and 

Figure 13).

Of course, different distributional results for NPV and IRR 

will be obtained with different distributional assumptions 

for the underlying data parameters. The tools presented here 

provide a generic approach to evaluation of economic benefits and risks of investments in integrated biomass gasification energy systems.

Summary

This report describes generic models we developed for preliminary analysis of investment opportunities in integrated 

biomass gasification business concepts at existing forest 

product production facilities (focused primarily on pulp 

mills). Our generic models were packaged in a Microsoft®

(Redmond, Washington) Office Excel workbook file, which 

makes them portable and accessible to others. Interested 

users may contact the authors to obtain a free copy. Our 

generic models and approach were designed for preliminary 

stages of analysis. If preliminary results look promising, we 

suggest that much greater due diligence and more sophisticated engineering input will be needed to support further 

development or investment decisions.

In this report we presented also a hypothetical analysis of 

an incremental investment in the integrated biomass gasification business concept at a pulp and paper mill, based 

on hypothetical input data. We applied sensitivity analysis 

and multivariate stochastic simulation to demonstrate techniques that can support risk assessment of such business 

concepts. Results of our hypothetical analysis should not 

be viewed as generally applicable because expected equipment performance and mill operating conditions could vary 

substantially from the hypothetical assumptions we used for 

illustrative purposes in this report. The hypothetical results 

do, however, suggest that biomass gasification business 

Figure 14. Histogram of simulated after-tax net 

present value (NPV).

Figure 15. Histogram of simulated after-tax 

internal rate of return (IRR).
Modeling Integrated Biomass Gasification Business Concepts

27

concepts could be economically attractive, and we recommend due diligence and careful quantitative evaluation of 

risks and investment performance to assess the concept in 

specific locations.

References

Agenda 2020 CTO Working Group. 2008a. Adding biofuel/

bioproduct capacity to existing U.S. mills; Part 1: Options. 

Norcross, GA: Paper 360° (TAPPI Press) Vol. 3, No. 4. 

(April 2008): 33–37.

Agenda 2020 CTO Working Group. 2008b. Adding biofuel/

bioproduct capacity to existing U.S. mills; Part 2: The Business Case. Norcross, GA: Paper 360° (TAPPI Press) Vol. 3, 

No. 6. (June/July 2008): 24–28.

Aspen Plus®. 2000. Aspen Plus User Guide, Version 10.2. 

Cambridge, MA: Aspen Technology, Inc. 936 p.

Bilek, E.M. (Ted). 2007. ChargeOut! Determining machine 

and capital equipment charge-out rates using discounted 

cash-flow analysis. Gen. Tech. Rep. FPL–GTR–171. Madison, WI: U.S. Department of Agriculture, Forest Service, 

Forest Products Laboratory. 33 p. 

Connor, E.J. 2007. The pathway to our bio-future. Cohasset, 

MA: PaperAge. (March/April 2007): 40–43.

Ince, P.J. 1979. How to estimate recoverable heat energy in 

wood or bark fuels. Gen. Tech. Rep. FPL–GTR–29. Madison, WI: U.S. Department of Agriculture, Forest Service, 

Forest Products Laboratory. 6 p. 

Larson, E.D.; Consonni, S.; Katofsky, R.E.; Iisa, K.; Frederick, W.J., Jr. 2008. An assessment of gasification-based 

biorefining at kraft pulp and paper mills in the United States, 

Part A: Background and assumptions. TAPPI Journal. Vol. 7, 

No. 11. (November 2008): 8–14.

Larson, E.D.; Consonni, S.; Katofsky, R.E.; Iisa, K.; Frederick, W.J., Jr. 2009. An assessment of gasification-based 

biorefining at kraft pulp and paper mills in the United States, 

Part B: Results. TAPPI Journal. Vol. 8, No. 1. (January 

2009): 27–35.

Niemantsverdriet, J.V. 2007. Modeling Fischer-Tropsch synthesis using density functional theory. Eindhoven,The Netherlands: Technical University of Eindhoven, Department of 

Chemical Engineering and Chemistry. (http://www.onderzoekinformatie.nl/en/oi/nod/onderzoek/OND1315127/). 

(Accessed December 15, 2010).

Thorp, B.A.; Thorp, B.A., IV; Murdock-Thorp, L.D. 2008a. 

A compelling case for integrated biorefineries (Part I). Norcross, GA: Paper 360° (TAPPI Press) Vol. 3, No. 3. (March 

2008): 14–15.

Thorp, B.A.; Thorp, B.A., IV; Murdock-Thorp, L.D. 2008b. 

A compelling case for integrated biorefineries (Part II). Norcross, GA: Paper 360° (TAPPI Press) Vol. 3, No. 4. (April 

2008): 20–22.

Ulrich, G.D.; Vasudevan, P.T. 2004. Chemical engineering 

process design and economics: a practical guide. 2nd ed. 

Durham, NH: Process Publishing. 

USDOE. 2010. Annual Energy Outlook 2010. Report #: 

DOE/EIA-0383. Washington, D.C.: U.S. Department of 

Energy. 221 p.

Zerbe, J.; Arola, R.; Wahlgren, H.G.; Rowell, R. 1976. The 

feasibility of utilizing forest residues for energy & chemicals. NSF–RA–760013, Contract No. NSF AER–75–19464. 

On file with: USDA Forest Service, Washington, DC.
Research Paper FPL–RP–660

28

Appendix A—Base Case Process 

Model Relationships

Some examples of mathematical relationships in the base 

case process model are described in this appendix, which 

shows examples of how material and energy flows in the 

base case process diagram (Fig. 2) are computed in the Excel worksheet based on sample input data (Table 1).

The average paper mill production (tons/day) and paper 

product revenue ($/ton) are model input parameters 

(Table 1). For those parameters, we specified a hypothetical 

production volume of 1,725 tons/day and average product 

value of $750/ton (corresponding to the hypothetical output 

capacity of a fairly large mill producing printing and writing paper products). If a mill produces a variety of different 

products, the product output volume should be the total of 

all products, and the product revenue should be the weighted 

average for all products. Those parameter values appear also 

in the upper right corner of the base case process diagram as 

outputs of the paper mill (Fig. 2).

The base case model assumes that wood pulp output of 

the pulp mill is determined by on-site wood pulp production requirements of the paper mill, computed by taking 

into account average paper product moisture content and 

paper coatings and fillers content. The “coatings and fillers” content should include any recycled fiber or purchased 

market pulp fiber. The process diagram shows the average 

daily flow of wood pulp in air-dry tons (1,527 air-dry tons 

per day) computed from the specified paper output tonnage, coatings and fillers content, and average machine-dry 

moisture content of the paper product. Hypothetical values 

that are typical for printing and writing paper products were 

specified for the coatings and fillers fraction (17%) and machine-dry moisture content (4%), as shown in Table 1. Unless otherwise noted, pulp, paper, and wood moisture content parameters in the model are expressed on a total weight 

basis (weight of moisture as a percentage of the total weight 

of material and moisture), which is the standard method in 

the pulp and paper industry. The pulp output is computed 

according to the following equation (A-1), which includes 

the constant term (0.9) to take into account that wood pulp 

tonnage is generally measured on a standard “air-dry” basis, 

meaning specifically 10% moisture content:

 Pulp Output = ((1– (Coatings and Fillers %)) 

 × ((Paper Output Tons)

 × (1-Machine-dry moisture %))) 

 ÷ 0.9

The input of clean pulp chips to the pulp mill (5,976 green 

tons/day, as shown in Fig. 2) is computed on the basis of a 

specified pulp yield parameter and pulp chip moisture content (Table 1). Pulpwood inputs such as pulp chips at pulp 

mills are commonly measured on a “green” tonnage basis, 

meaning the total weight of the wood including moisture 

content. The term “clean” is a term used in the industry 

that generally means the pulpwood has had bark removed 

and has been screened for removal of dirt, stones, or other 

debris. As shown in Table 1, a hypothetical value was specified for pulp yield (46%) that is typical for pulp yield at a 

kraft pulp mill producing bleached kraft pulp for printing 

and writing paper. Pulp yields actually vary by pulping process and the type of paper product being produced, and may 

range from less than 50% for bleached chemical pulps to 

over 90% for mechanical pulps. By standard convention in 

the pulp industry, pulp yield refers to the recovery ratio of 

pulp per unit of wood input on a dry weight basis (excluding 

moisture) and is expressed as a percentage, the ratio of dry 

weight of pulp output to the dry weight of wood input to the 

pulping process. Some pulp mills may have multiple pulping lines with different yields, and in that case the pulp yield 

parameter for the model should be specified as the weighted 

average pulp yield. A hypothetical value of 50% moisture 

content was specified for the pulp chip moisture content 

(Table 1). That moisture content level is commonly applied 

as an average assumption for the moisture content of pulpwood, although wood moisture content can naturally vary 

by season, region, and wood species. The tonnage of clean 

pulp chips that are input to the pulp mill is computed by the 

following equation (A-2), which includes the constant term 

(0.9) to account for wood pulp tonnage being measured on a 

standard “air-dry” basis, meaning specifically 10% moisture 

content:

Clean Pulp Chips = ((Pulp Output) × 0.9 / (Pulp Yield))

 ÷ (1 – (Pulp Chip 

 Moisture Content)) 

Recoverable wood soap, turpentine, and other resin extractives are typical by-products of wood pulp mills. Soaps are 

the product of reaction between pulping chemical alkali and 

fatty acids from wood, whereas turpentine and other extractives may be recovered prior to pulping in the pre-steaming 

of wood chips. Recovery potential can vary by wood species 

and by type of pulping process. The daily soap and turpentine outputs and related revenue of the pulp mill are computed on the basis of the tonnage of clean pulp chip inputs 

and specified average recovery and average revenue per ton 

of soap and turpentine. The soap and turpentine recovery 

and average revenue are model parameters that refer to total 

soap, turpentine, and other recoverable extractives. In the 

hypothetical input data, we specified a soap and turpentine 

recovery of 4.0 gallons per green ton of clean pulp chips and 

a soap and turpentine revenue of $0.50 per gallon recovered 

(Table 1).

Other aspects of the base case process are likely to change 

upon introducing the integrated gasification to biofuel business case process. One is the elimination of the wood/bark 

boiler when all or part of its biomass feedstock is directed to 

the gasification plant for the alternate generation of electricity and steam needed by the pulp mill. In some instances it 

(A-1)

 (A-2)
Modeling Integrated Biomass Gasification Business Concepts

29

might be more advantageous to merely increase boiler ca-

pacity, particularly if selling electricity and steam becomes 

more profitable, or the new technology such as the so-called 

super boiler is implemented. The wood/bark boiler charac-

teristics of fuel heating values, boiler efficiency, steam pro-

duction, and excess air for combustion as a function of resi-

due moisture content are based on the 1976 Forest Service 

report to the National Science Foundation on “The Feasibil-

ity of Utilizing Forest Residues for Energy & Chemicals” 

(Zerbe and others 1976). Also found in the document are the 

1976 capital and annual costs as a function of the equipment 

capacity (in 1,000 lbs steam/h) that we extrapolated to the 

year 2008. The capital cost scale factor was derived to be n = 0.75 as incorporated in the Equation A-3 generic 

formula:

 Processing Unit Capital Cost = Cunit Mreference 

 

× (Mscale / Mreference)

n

 = Cunit (Mreference)(1–n)

 × (Mscale)n 

The steam turbine is assumed to be similar to a 16-stage GE 

extraction steam turbine with a rated capacity of 47-MW 

for a Certified HIPAA Professional (CHP) case study for the 

Kimberly Clark Mill in Everett, Washington, as described 

in an on-line DOE publication (http://files.harc.edu/Sites/

GulfCoastCHP/CaseStudies/EverettWAKimberlyClark.pdf). 

At stand-alone conditions, or when parasitic load is zero, the 

electrical efficiency on the basis of steam’s HHV is 35.7%. 

When the parasitic load (300 psig steam output) is at 74% of 

the steam’s HHV, this turbine generator electrical efficiency 

drops to 19.4% on the basis of HHV. Obviously, when the 

parasitic load is 100% of the steam’s HHV, then electrical 

efficiency is zero. This variation of electrical efficiency as a 

function of parasitic load was fitted with a third degree poly-

nomial. The capital cost scale factor is assumed unity with 

the cost parameter given in Table 12.

These are just some examples of base case process as-

sumptions and mathematical relationships in the base case 

process model. The user can view other base case process 

relationships and formulas in the Excel workbook and also 

contact co-author Mark Dietenberger for additional expla-

nation of process model relationships (Mark was primarily 

responsible for the process model relationships).

(A-3)
Research Paper FPL–RP–660

30

Appendix B—Business Case 

Process Model Relationships

Some examples of mathematical relationships in the business case process model are described in this appendix, 

which serves to explain how material flows in the business 

case process diagrams are computed on the basis of the sample input data. The following text explains as an example 

the process assumptions and mathematical relationships for 

biomass procurement in the business case process model.

Material flows for biomass procurement in the business case 

model (shown in Fig. 3) are derived from the model parameters and input data presented in Table 2. Parameters that 

principally control quantities of biomass procurement for 

gasification include quantities of logging residue biomass, 

other purchased biomass (including forest and agricultural 

biomass), and the quantities of bark residue output from the 

chip mill(s) and wood mill(s) that are directed to the wood/

bark boiler (the remaining bark is directed to biomass gasification). Average daily procurement tonnages and prices 

of logging residue and other purchased biomass as shown in 

Figure 3 are precisely the same as the input data values in 

Table 2 (1,100 green tons of logging residue and 500 green 

tons of other purchased biomass, both at $26.50 per green 

ton).

A specified percentage of the purchased biomass (logging 

residue and other purchased biomass) may be directed to the 

wood/bark boiler, and the remaining fraction is all directed 

to biomass gasification. In the sample data (Table 2) this 

percentage is set at 0% (purchased biomass to wood/bark 

boiler), and thus as shown in Figure 3 all purchased biomass 

is directed to biomass preparation and drying for subsequent 

biomass gasification. However, the model is flexible in that 

a different value can be specified for the percentage of purchased biomass directed to the wood/bark boiler (and conversely to biomass gasification).

The wood and bark fractions refer to the weight percentages of wood and bark materials in the purchased biomass. 

The percentages can be varied. The bark fraction of logging 

residue as shown in Figure 3 is based on the logging residue 

bark fraction parameter (15% as indicated for the hypothetical data in Table 2). The wood fraction of logging residue is 

computed as the remaining percentage (e.g., 85%), according to the following equation (B-1).

Logging Residue Wood Fraction = (1 – (Logging 

 Residue 

 Bark Fraction))

The other purchased biomass can consist of any combination of wood, bark, or agricultural biomass, with weight 

fractions of each (% of total weight) determined by the parameters for other biomass % wood, other biomass % bark, 

and other biomass % agricultural residue (Table 2).

The quantity of purchased biomass that is input to gasification (e.g., hypothetical value of 1,600 tons per day shown in 

Fig. 3) is based on the total quantity of purchased logging 

residue plus other purchased biomass, minus the specified 

fraction of purchased biomass that is directed to the wood/

bark boiler, according to the following Equation (B-2).

Purchased Biomass Input to Gasification 

 = (Logging Residue Biomass Purchased 

 + Other Biomass Purchased)

 × (1 – (Purchased Biomass to Wood 

 and Bark Boiler))

Conversely, the quantity of purchased biomass that is directed to the wood/bark boiler (hypothetical value of 0 tons 

per day shown in Fig. 3) is the total quantity of purchased 

logging residue plus other purchased biomass times the fraction of purchased biomass that is directed to the wood/bark 

boiler.

The average moisture content of the purchased biomass 

(e.g., hypothetical value of 40%, as shown in Fig. 3) is computed as the weighted average of the moisture contents of all 

purchased biomass fractions, including the wood and bark 

fractions of the logging residue, other purchased biomass, 

and purchased agricultural residue. The values of any of the 

parameters that describe biomass fractions or their moisture 

content (Table 2) may be varied to more accurately reflect 

local biomass characteristics or supply in different situations. The data inputs in Table 2 are purely hypothetical 

examples.

For the gas turbine, we input an efficiency parameter for 

electricity production, and we set the nominal value of that 

parameter at 21.4%. This is based on a report that characterized performance of gas turbine CHP systems at scales 

similar to those of our model; see report by Energy Nexus 

Group (“System 3” in Table 1 of the report, at http://stsm.

ir/resources/10301-09101387135242Technology%20Characterization%20Gas%20Turbines.pdf). Rather than lowering the electrical output efficiency to achieve the higher 

temperature flue gas (371 °C/700 °F), we assume that the 

steam output of the steam generator is reduced, and we use 

the data shown in Figure 7 of the report to reduce the steam 

output to just 22.2% of the input energy (see Table 7), which 

corresponds to flue gas temperature of > 371 °C (700 °F). 

We retained the 42.3% parameter for recoverable energy in 

the flue gas. This leaves 14% as the implicit energy remaining for the parasitic load of air compressors of the gas turbine. We also assume an excess air of 220% for combustion 

in the gas turbine, which corresponds to data in Table 1 of 

the report from Energy Nexus Group. The capital cost information was also obtained from the Nexus report.

These are just some examples of business case process assumptions and mathematical relationships in the business 

case process model. The user can view other business case 

 (B-1)

(B-2)
Modeling Integrated Biomass Gasification Business Concepts

31

process relationships and formulas in the Excel workbook or 

contact co-author Mark Dietenberger for additional explanation of process model relationships (Mark was primarily 

responsible for the process model relationships).
Research Paper FPL–RP–660

32

Appendix C—GTL Plant Prototypical 

Design and Costs

This appendix was contributed by Professor Ross Swaney, 

University of Wisconsin–Madison, Department of Chemical and Biological Engineering. Professor Swaney describes 

his independently developed prototypical design and cost 

estimates for a GTL plant at a range of scales appropriate 

for the Fischer–Tropsch component of integrated forest 

product biorefinery concepts. As indicated in his text, Professor Swaney benefited from discussions with personnel of 

the U.S. Department of Energy, National Renewable Energy 

Laboratory (NREL), and ThermoChem Recovery International (TRI), Inc., a leading developer of biomass gasification technology.

Fischer–Tropsch Component of the Forest 

Biorefinery Study: Prototypical Process Design and Cost Estimate

Ross Swaney

Department of Chemical and Biological Engineering

University of Wisconsin–Madison

April 10, 2009

Background and Description

The Forest Biorefinery Study under development at the 

USDA Forest Products Laboratory considers the integration 

of biorefining processes into the operation of a paper mill. 

Among the several components of this integration model is 

a biomass gasification path to produce syngas followed by 

Fischer–Tropsch (FT) conversion.

While much is known about FT technology, the capital cost 

for an FT process unit of the type and scale suitable for the 

Forest Biorefinery model is not readily obtainable from 

publicly available resources. First, the capital cost depends 

on the application scenario. Both the process scale and the 

particulars of integration with the upstream and downstream 

components of the overall scheme are significant and influence process equipment requirements to the extent that 

extrapolation from other scenarios would be very unreliable. 

Second, while one or more commercial entities exist that 

offer to provide such a process, cost information from such 

vendors is held as proprietary.

The purpose of the design study reported here is to develop 

a prototype process design for the Forest Biorefinery scenario and provide a corresponding capital estimate. Secondarily, operating cost information is generated along with 

utility consumption and generation.

Design Basis and Assumptions

The prototype design was based upon the assumptions outlined below. Conventionally obtainable process equipment 

was used throughout.

Syngas Feed

The feed syngas composition is given in Table C-1. This 

composition was constructed based on discussions held in 

June 2008 with NREL personnel knowledgeable about atmospheric biomass gasification. These values are believed 

to be reasonably consistent with an earlier discussion held 

with TRI, although their information did not provide a 

complete composition. Note that significant changes in the 

syngas composition can strongly affect the design and the 

economic results.

The syngas feed is considered to be after post-gasification 

cleanup processing; e.g., particulate and tar removal or 

catalytic conversion. It is assumed to be delivered at nearambient temperature and pressure.

Flowsheet Scheme

The following were assumed about the general process 

configuration:

1. No shifting of syngas. The H2/CO ratio is greater 

than 2, with substantial CO2 content.

2. No recovery and selective distribution of H2.

3. No recycle of H2 or CO from intermediate streams 

or the tail gas.

4. Two reaction states with interstage H2O removal.

5. No selective removal of CO2.

A credible process flow diagram (PFD) scheme was possible under these assumptions. NREL provide a reasonable 

base case to use as a starting point in more detailed process 

studies. Further study may determine that other options are 

advantageous. For example, with the given syngas composition, liquid product yields could be increased by 25% to 

30% by shifting the CO2.

FT Synthesis Reactors

The design uses fixed catalyst beds of cobalt supported on 

alumina. This catalyst technology is available and adequately mature. The choice of cobalt favors more gasoline-range 

products, whereas the classic iron catalyst technology favors 

more diesel-range heavier products.

1. Catalyst

(a) Cobalt supported on alumina

(b) Cobalt dispersion: 7.5%

(c) Particle size: 0.8 mm

(d) Egg-shell catalyst with 20% cobalt active shell 

thickness of 0.15 mm

2. Flow configuration

(a) Tube diameter: 1.0 in.

(b) Tube lengths: 6.0 and 4.5 ft

(c) Multiple parallel shells

3. Heat removal: Steam generation
Modeling Integrated Biomass Gasification Business Concepts

33

Catalyst characteristics are estimates assembled from meetings with EFT and TRI in combination with standard literature information. These parameters lead to a feasible design 

that is roughly correct. Analysis does indicate that these values, while workable, are not optimized (under the available 

kinetic model), and that further optimization of these reactor 

parameters is warranted. The effect of such optimization on 

the economics should be observable but not large.

Modeling of Kinetics and Thermodynamic Behavior

Reactor yields and space time were determined by onedimensional plug flow integration through the catalyst beds. 

Results were determined using a kinetic model developed to 

represent reported conversion rates and product yield distributions found in the literature for this type of catalyst. The 

model tracks 36 product species covering carbon numbers 

from 1 to 18 plus a lumped tail.

The SRK-Kabadi-Danner (SRKKD) method as implemented in AspenPlus (Aspen Technology, Inc., Cambridge, 

Massachusetts) was used to model the vapor-liquid-liquid 

phase equilibrium behavior (Aspen 2000). No adjustments 

to the equation of state interaction parameters were made for 

this study.

PFD of Prototype Process

The process flow diagram developed for the prototype design is attached to this report. Corresponding detailed mass 

and energy balance calculations were computed using AspenPlus. The flow diagram is not fully annotated as this is a 

preliminary design. However, a full set of stream conditions, 

compositions, and flows were obtained and can be consulted 

if further specific inquiries arise.

The PFD shows two reactor beds in series with intermediate 

removal of water and liquid products by condensation. Heat 

released in the synthesis reactors is removed by generating 

steam at about 16 bar gauge. The steam is superheated in a 

fired heater and passed through a turbine to generate electricity before being exported at 1.1 bar gauge.

The separation configuration is complicated by the presence 

of significant amounts of unconverted light gases in the reactor effluent. A refrigerated preflash scheme was devised 

employing three countercurrent energy recovery/rectification stages. This scheme significantly reduces the losses of 

C5s and C6s from the gasoline product that would otherwise 

be stripped into the fuel gas stream. The gasoline-range 

product is degassed (RVP ≈ 14 psi) but may need to be 

passed through a stabilizer column by the blender customer. 

The diesel and the heavier range materials are fractionated 

into separate product streams.

Economics of the Design

This section presents the capital and operating costs and 

product slates determined for the prototype design. Results 

are developed for plant nameplate capacities of 1,000, 

2,000, and 3,000 bbl./d expressed as the total of liquid products produced. Typical operating reliability for established 

operation could be taken on the order of 350 d/y.

Capital Cost Estimate

Capital cost estimates were developed for the prototype 

design using an update of Guthrie’s method as presented in 

Ulrich and Vasudevan (2004). This is a factored estimate 

approach based on the characteristics of the major equipment items. This level of estimate incorporates sizing of 

individual items and accounts for relevant effects of operating conditions and estimated materials of construction. 

Accuracy is perhaps similar to a Class 30 estimate (roughly 

± 30%), assuming that the equipment definitions are solid. 

This level of estimate is the best that can be obtained for the 

level of design detail available here. The cost data source 

is public domain, and while it is believed to be reasonably 

reliable, such estimates can never be accepted with complete 

certainty.

The estimates presented are based on 45 different equipment 

items corresponding to the attached PFD plus 3 product 

storage tanks, using individual sizing estimates for each of 

the three capacity cases. The estimate covers equipment 

from the syngas feed to the tankage. The estimate assumes 

the unit will be built in available space as an addition to an 

established operating site. It does not include off-site facilities. A cooling water system, boiler feedwater supply, electrical supply, and typical plant service and support facilities 

are assumed to be available. No new product loading facilities are included.

The largest cause of uncertainty would be a possible change 

in an essential feature of the process design, for example the 

incorporation of a RWGS reaction step. Such changes would 

alter the PFD, requiring different process equipment items, 

and alter the main flows and operating conditions. The second largest cause would be a significant change in the feed 

syngas pressure of composition. Lesser design modifications might also be expected with further optimization of 

the scheme presented. These might reduce the capital cost 

somewhat, but perhaps not by more than 10–20%.

Table C–1. Syngas 

composition 

Element

Mole 

(%)

H2 45

N2 1

CO 15 

CO2 25

CH4 6

C2H6 2 

H2O 6 
Research Paper FPL–RP–660

34

Costs were adjusted to December 2008 levels using the most 

recently available CEPCI index. Table C-2 presents a summary of the resulting capital cost estimates.

Operating Cost

Table C-3 summarizes the utility usages, periodic catalyst 

cost, and syngas federate needed for continuous operation at 

nameplate capacity. With applicable unit costs, the operating 

cost can be determined. Unit costs for cooling water use are 

site dependent in lieu of other information, a rough estimate 

of 0.009-0.12 $/m3 might be assumed for the cost. The principle operating cost is the electricity to power the feed compressors, partly offset by the power generated by the turbine. 

Fuel gas needs are debited from the fuel gas product of the 

process. The cost of supplying the syngas feed must be assessed from global analysis of the integrated operation.

Products

Table C-4 summarizes the product production rates. The 

FT liquids contain α-olefins and are otherwise paraffinic 

and unbranched. The value of these materials will require a 

market analysis. While the wax-oil might be marketable as a 

fuel oil blend stock with little change, the gasoline and diesel range materials most likely would be marketed to a refiner as blending stocks. The gasoline range material would 

be subjected to upgrading processing by the refiner.

The fuel gas contains a significant amount of CO2 but 

should be serviceable as a partial or total replacement for 

natural gas. Minor adjustments to combustion equipment 

operation would be required, and the reduced heating value 

possibly would cause a slight reduction in combustion thermal efficiency.

The value placed on the low-pressure steam should be taken 

net of the cost to supply the boiler feed water (assumed at 

43 °C/109 °F here).

Analysis and Discussion of Design

For the purposes of the Forest Biorefinery Study, the values 

generated for the prototypical design should be adequate for 

incorporating the economics of a FT processing unit into the 

integrated processing scheme. Note that the capital requirements and the operating costs are considerable relative to 

the product value (Fig. C–1).

The uncertainties in the values determined should be borne 

in mind. The capital estimation procedure has an intrinsic 

uncertainty, whereas the operating costs are known fairly 

accurately for the particular design developed. However, 

the greatest effect on the economic analysis will come from 

other factors. Major factors are mentioned here in order of 

their potential impact:

Incorporation of Reverse Water-Gas Shift Reaction 

(RWGS). Performing RWGS on the syngas prior to the FT 

synthesis would increase liquid products by perhaps 25% 

Table C–2. Capital cost estimates for 1,000, 2,000, and 

3,000 bbl./d production capacities

×106

 ($) 

(December 2008) 

Bbl./d 1,000 2,000 3,000

Bare module capital a CBM — — — 

Compressors 27.3 52.1 76.1 

Heat exchangers 2.4 3.9 5.4 

Reactors, catalyst 1.8 3.6 5.5 

Columns 0.3 0.3 0.4 

Other vessels 0.7 1.2 1.7 

Refrigeration 2.5 3.4 4.1 

Turbine, generator, superheater 1.8 2.7 3.6 

Other equipment 0.6 0.9 1.2 

Total bare module capital a CTBM 37.4 68.3 97.9 

Contingency @ 0.15 × a CTBM 5.6 10.2 14.7 

Contractor fee @ 0.03 × a CTBM 1.1 2.0 2.9 

Fixed capital (total module capital CTM ) 44.1 80.6 115.5 

Working capital @ 0.10 × fixed capital 4.4 8.1 11.6 

Total capital 48.5 88.6 127.1 

Table C–3. Operating costs for 1,000, 2,000, and 3,000 bbl./d 

production capacities 

Demand

Input requirement Units 

1,000

(bbl./d)

2,000

(bbl./d)

3,000 

(bbl./d) 

Electricity — — — — 

Compressors MW 15.196 30.393 45.589 

Refrigeration MW 1.078 2.157 3.235 

Pumps MW 0.028 0.028 0.028 

Turbine MW –3.464 –6.928 –10.393 

Electricity total net MW 12.838 25.649 38.460 

Fuel gas (debited from product) MW (GHV) 4.071 6.420 9.631 

Cooling water m3

/s 0.272 0.543 0.815 

Catalyst replacement (annual) $/y 193,465 386,930 580,395 

Syngas (composition in Table C–1) kmol/s 1.109 2.217 3.326 
Modeling Integrated Biomass Gasification Business Concepts

35

to 30%. While fuel gas output would be decreased, the relatively higher economic value of the liquids should justify 

the modest increase in capital that would be required.

This change would entail integrating another catalytic reactor upstream of the FT reactors. Incorporation would necessitate redesign of the entire process. The synthesis reactors 

would become somewhat larger, while the separation flow 

scheme is likely to change. With significantly less CO2 and 

unconverted H2 in the separation, refrigeration can possibly 

be avoided with attendant savings in capital and operating 

costs.

The present results strongly indicate that this option may be 

preferred over the basic design developed here and should 

be investigated in future work.

Changes in Syngas Composition or Delivery Pressure and 

Temperature. The nature of the upstream gasifier greatly 

impacts the economic parameters of the FT process. It is 

essential to remember that the gasifier and synthesis process 

must be designed together to work with each other. The 

design developed and analyzed here is contingent upon the 

syngas composition and pressure assumed. Changes in the 

H2/CO/CO2 ratios could substantially affect product yields 

Table C–4. Products slates for 1,000, 2,000, and 3,000 bbl./d 

cases 

Product Units 

Rate

1,000

(bbl./d)

2,000

(bbl./d)

3,000 

(bbl./d) 

FT gasoline stock bbl./d 669.5 1,339.0 2,008.5 

FT diesel stock bbl./d 187.1 374.3 561.4 

FT wax-oil bbl./d 143.3 286.7 430.0 

Fuel gas (net of process usage) MW (GHV) 9.119 18.237 27.356 

mol/s 400.6 801.1 1,201.7 

Steam @ 2.1 bar, 125 °C kg/s 12.460 24.920 37.380 

(FW supplied at no cost) — — — — 

Figure C–1. Process flow diagram for prototypical 1,000 barrel per day Fischer-Tropsch section of integrated forest 

biorefinery (Dr. Ross Swaney, University of Wisconsin).
Research Paper FPL–RP–660

36

and the process design, as well as the desirability of the 

RWGS option.

Clearly both capital and operating costs of the FT process 

are dominated by feed compressors. Use of a high-pressure 

gasifier, while increasing costs of the gasifier, would greatly 

reduce the costs of the FT unit. This strong interaction must 

be considered when examining process options.

Other Factors. Several additional aspects may also have 

some impact:

(a) As presented, the PFD does not incorporate a power recover turbine on the fuel gas. Further analysis may indicate 

that this option is advantageous, providing a significant 

electrical power credit. This would probably involve three 

stages with pre/reheat integrated with E-1 and E-2.

(b) The usability/value of the low-pressure stream and the 

fuel gas need to be verified. If needed, the stream can be 

supplied at higher pressure by sacrificing some or all of the 

power obtained from the turbine.

(c) Newer catalyst fabrications, e.g., monoliths, may reduce 

reactor costs somewhat.

(d) The physical modeling methods should be further validated and refined if so indicated to the extent that relevant 

data are available. The kinetic model used does not account 

for the effects of reactor temperature on the product yield 

distribution. Also, the catalyst run lifecycle will require a 

progression of reactor operating conditions and the yield 

distribution will shift to some degree. The VLLE prediction 

methods should be further validated.

Further technical studies should address the above. Additionally, the market values of the FT liquids will need to be 

assessed.