Industrial Sectors Integrated Solutions (ISIS) Model 

for the Portland Cement Manufacturing Industry 

Ravi K. Srivastava, Ph.D. 

William H. Yelverton, PE 

U.S. Environmental Protection Agency 

Office of Research and Development 

Air Pollution Prevention and Control Division 

Research Triangle Park, NC 27711 

Elineth Torres 

U.S. Environmental Protection Agency 

Office of Air Quality Planning and Standards 

Sector Policies and Programs Division 

Research Triangle Park, NC 27711 

Samudra Vijay, Ph.D. 

Sam Analytic Solutions, LLC 

Morrisville, NC 27560 

August 2010 
Acknowledgements 

A large model such as ISIS could not have been completed without the support and advice of many 

individuals and organizations. We gratefully acknowledge the following contributions. Professor Richard 

Newell of Duke University and Dr. Dallas Burtraw of the Resources for the Future (RFF) engaged in 

many helpful discussions, which helped shape ISIS. Additionally, Dr. Burtraw helped develop the 

formulation of cement imports. Under EPA Contract EP-D-06-118, Mr. Brooks Depro of RTI 

International developed the cost functions and other economic data for the cement industry. Also under

EPA Contract EP-C-07-015, Dr. James Staudt of Andover Technologies developed the technical 

information on controls and energy efficiency measures used in the current version of ISIS cement model. 

Dr. Jayant Sathaye and Dr. Tengfang Xu of Lawrence Berkeley National Laboratory (LBL) provided 

many useful insights on energy efficiency measures for the cement industry. Portland Cement Association 

(PCA) provided helpful comments on ISIS formulation, engaged in many fruitful discussions with the 

ISIS development team, and provided some needed data. The ISIS cement model was peer reviewed by a 

number of experts. These experts included Dr. Dallas Burtraw of RFF, Dr. Hendrik G. van Oss of the 

U.S. Geological Survey, Mr. Charles Drummond, Mr. Chris Nichols, and Mr. Sikander Khan of the U.S. 

Department of Energy, Mr. Hector Ybanez of Holcim Cement, Mr. Kevin Culligan of EPA, Dr. Fereidun 

Feizollahi of California Air Resources Board (Economic Studies Section), Mr. Eric Smith of EPA, and 

Dr. Ernst Worrell of LBL. Mr. Charles Fulcher and Mr. Tom Walton of EPA provided many quality 

assurance checks on the ISIS cement model. Finally, Dr. Wojciech Jozewicz, Kim Egler, and the staff at

ARCADIS Inc. compiled the many comments received from peer reviewers and helped with development 

of this documentation under EPA Contract EP-C-09-027. 
i 

Table of Contents 

List of Tables ...............................................................................................................................................iii

List of Figures.............................................................................................................................................. iv

Acronyms, Initializations, and Abbreviations............................................................................................... v

Conversion Table – English Units to SI Units...........................................................................................viii

Chapter 1 Introduction............................................................................................................................1-1

1.1 The US Cement Industry..................................................................................................................1-1

1.1.1 Cement Types and Categories ..............................................................................................1-1

1.1.2 Overview of the Cement Manufacturing Process .................................................................1-2

1.1.3 Kiln Types and Their Use.....................................................................................................1-5

1.1.4 Portland Cement Production in the U.S................................................................................1-7

1.1.5 Imports of Portland Cement in the U.S.................................................................................1-8

1.1.6 Cement Demand Centers ......................................................................................................1-9

1.2 Emissions from the U.S. Cement Industry and Applicable Regulations..........................................1-9

1.3 Overview of ISIS............................................................................................................................1-10

References for Chapter 1 .........................................................................................................................1-13

Chapter 2 ISIS Mathematical Framework............................................................................................2-1

2.1 Indexes, Sets, and Mappings............................................................................................................2-3

2.2 Objective Function...........................................................................................................................2-4

2.3 Supply ..............................................................................................................................................2-7

2.4 Production Capacity and Supply Costs ..........................................................................................2-10

2.5 Emissions .......................................................................................................................................2-14

2.6 Controls and Costs .........................................................................................................................2-16

2.7 Costs of Energy Efficiency Measures ............................................................................................2-23

2.8 Policy Options................................................................................................................................2-26

2.9 Optimization and Post-Processing .................................................................................................2-28

References for Chapter 2 .........................................................................................................................2-29

Chapter 3 Cement Data...........................................................................................................................3-1

3.1 Data Requirements...........................................................................................................................3-1

3.2 Cement-Specific Data ......................................................................................................................3-1

3.2.1 Industry, Fuel, and Emissions...............................................................................................3-1
ii 

3.2.2 Control Technologies and Emission Abatement Approaches ............................................3-13

3.2.3 Policy and Economic Parameters .......................................................................................3-13

References for Chapter 3 .........................................................................................................................3-24

Chapter 4 Model Calibration..................................................................................................................4-1

4.1 Calibration Methodology .................................................................................................................4-1

4.2 Data for Calibration..........................................................................................................................4-1

4.2.1 Cement Prices in USGS Districts .........................................................................................4-2

4.2.2 Cement Production in USGS Districts..................................................................................4-2

4.2.3 Cement Imports by Import Districts .....................................................................................4-2

4.3 Results of Calibration.......................................................................................................................4-2

4.4 Recommendations..........................................................................................................................4-10

4.5 References for Chapter 4................................................................................................................4-11

Chapter 5 An Example Policy Analysis .................................................................................................5-1

5.1 An Example Analysis of Potential Reductions in CO2 Emissions from the U.S. Cement 

Industry ............................................................................................................................................5-1

5.2 Summary and Thoughts for Future Work ........................................................................................5-8

5.3 Disclaimer ........................................................................................................................................5-9

References for Chapter 5 .........................................................................................................................5-10
iii 

List of Tables 

Table 1-1. Typical Average Heat Input by Cement Kiln Type ...........................................................1-5

Table 1-2. Number of Kilns by Kiln Type in the U.S. in 2005 and 2009 ...........................................1-6

Table 1-3. Largest Hydraulic Cement and Clinker Import Custom Districts in the U.S. in 2005.......1-8

Table 3-1. Summary of Kilns Modeled in the ISIS Cement Industry Model......................................3-1

Table 3-2. Portland Cement Demand in Millions of Metric Tons.......................................................3-4

Table 3-3. Bulk Shipment Costs (Cents per Metric Ton per Mile).....................................................3-6

Table 3-4. Portland Cement and Clinker Imports in Million Metric Tons, by Customs District in 

2005...................................................................................................................................3-7

Table 3-5. Specific Fuel Consumption and Total Exhaust Gas Flow Rate (wet) for Various Kiln 

Types .................................................................................................................................3-9

Table 3-6. Estimated Uncontrolled NOX Emission Intensities for Cement Kilns .............................3-10

Table 3-7. Average SO2 Emissions for Each Kiln Type in Each State .............................................3-11

Table 3-8. Approximate CO2 and H2O Produced from Combustion of Fuels...................................3-12

Table 3-9. NOX Control Technologies for Cement Kilns..................................................................3-15

Table 3-10. SO2 Control Technologies for Cement Kilns...................................................................3-18

Table 3-11. CO2 Control Technologies for Cement Kilns ..................................................................3-19

Table 3-12. HCl, Hg, and THC Control Technologies for Cement Kilns...........................................3-20

Table 3-13. Multimedia Impacts of Process Capacity Replacement on Cement Kiln Operation1......3-20

Table 3-14. Energy Efficiency Measures for Raw Materials Preparation...........................................3-22

Table 3-15. Energy Efficiency Clinker Making Measures..................................................................3-22

Table 3-16. Energy Efficiency Measures for Finish Grinding ............................................................3-23

Table 3-17. Energy Efficiency Plant-wide Measures..........................................................................3-23

Table 4-1. Cement Prices ($/short ton) for USGS Districts ................................................................4-4

Table 4-2. Cement Production (short tons) for Various USGS Districts ............................................4-5

Table 4-3. Cement Imports (except from Canada and Mexico) for Import Districts (short tons).......4-6

Table 4-4. Reported and Calculated Cement Prices in USGS Districts (2005)...................................4-7

Table 4-5. Reported and Calculated Cement Prices in USGS Districts (2006)...................................4-8

Table 4-6. Reported and Calculated Cement Prices in USGS Districts (2007)...................................4-9

Table 4-7. Aggregate production (reported and modeled) for 2005-2007 ..........................................4-9

Table 5-1. CO2 Caps Modeled in this Analysis...................................................................................5-2
iv 

List of Figures 

Figure 1-1. Schematic of the Wet Cement Process...............................................................................1-3

Figure 1-2. Schematic of the Dry Cement Process with Cyclone Preheater.........................................1-4

Figure 1-3. Trends in Cement Kiln Type and Capacity in the U.S. (1995 to 2008) .............................1-6

Figure 1-4. Portland Cement Plant Locations.......................................................................................1-7

Figure 1-5. Imports of Clinker and Cement from 1998 to 2008 (PCA, 2009a)....................................1-9

Figure 1-6. Portland Cement Facilities and O3 NAA, PM2.5 NAA, and Class 1 Areas ......................1-11

Figure 1-7. An Integrated View of Pollution Generation Pathways, Emissions Abatement 

Approaches, and Multimedia Impacts for an Industrial Sector.......................................1-12

Figure 2-1. Consumer and Producer Surplus in a Market ....................................................................2-2

Figure 2-2. Broad Modules and Associated Information Flows Utilized in the ISIS Framework........2-2

Figure 2-3. Stepwise Calculation of the Demand Curve ......................................................................2-5

Figure 3-1. Distribution of Cement Kilns in the United States as of 2009...........................................3-3

Figure 3-2. Commercial Fuel Use Profile by U.S. Cement Industry in 2005.......................................3-9

Figure 5-1. Projected CO2 Allowance Prices........................................................................................5-2

Figure 5-2. Projected Revenue for the U.S. Cement Industry over the Horizon 2013-2020................5-3

Figure 5-3. Projected Average Cement Prices in 2013 for the U.S. Cement Industry .........................5-4

Figure 5-4. Projected Demand for Cement...........................................................................................5-4

Figure 5-5. Projected Imports for the U.S. Cement Industry over the Horizon 2013-2020..................5-5

Figure 5-6. Projected Capacity Retirement/Mothballing......................................................................5-6

Figure 5-7. Projected Emissions of NOX, SO2, and CO2 from the U.S. Cement Industry over the 

Horizon 2013-2020............................................................................................................5-6

Figure 5-8. Projected CO2 Emissions and Reductions from the U.S. Cement Industry Under the 

BAU and the 10-percent CO2 Reduction Cases. ...............................................................5-7

Figure 5-9. Projected Control Technology Applications in the U.S. Cement Industry Under the 

10-percent CO2 Reduction Case........................................................................................5-8

Appendix A 

Andover Technology Partners and RTI International Memos
v 

Acronyms, Initializations, and Abbreviations 

ACI Activated Carbon Injection 

AEO Annual Energy Outlook 

APCA American Portland Cement Alliance 

ARB Air Resources Board (see CARB) 

ASD Adjustable Speed Drives 

ASR Alkali-Silica Reactivity 

ASTM American Society for Testing and Materials 

AVC Average Variable Cost 

AZ Arizona 

BACT Best Available Control Technology 

BAU Business As Usual 

BFS Blast Furnace Slag 

BLS Bureau of Labor Statistics 

CA California 

CARB California Air Resources Board 

CCS Carbon capture and sequestration 

CEMBUREAU European Cement Association based in Brussels 

CGC Convert to Reciprocating Grate Cooler 

CIF Cost, Insurance & Freight 

CKD Cement Kiln Dust 

CO Colorado 

CSI Combustion System Improvement 

DE Delaware 

DLI Dry Lime Injection 

EGFW Exhaust Gas Flow Rate (wet) 

EIA Energy Information Administration 

EMCS Energy Management and Control System 

EMD Efficient Mill Drives 

EMPC Energy Management and Process Control 

EPA U.S. Environmental Protection Agency 

ESP Electrostatic Precipitator 

ETS Efficient Transport System 

FL Florida 

FOM Fixed Operation and Maintenance 

GA Georgia 

GAMS General Algebraic Modeling System 

GDP Gross Domestic Product 
vi 

GHG Greenhouse Gas 

H2O Water 

HAP Hazardous Air Pollutant 

HEC High-Energy Classifiers 

HEM High Efficiency Motors 

HERM High Efficiency Roller Mill 

HPRP High-Pressure Roller Press 

HRPG Heat Recovery for Power Generation 

ID Idaho 

IF Indirect Firing 

IGMBM Improved Grinding Media (Ball Mills) 

IL Illinois 

IPCC Intergovernmental Panel on Climate Change 

IPM International Petroleum Monthly 

ISIS Industrial Sectors Integrated Solutions 

LNB Low NOX Burner 

LWS Limestone Wet Scrubber 

MI Michigan 

MMBtu Million British Thermal Units 

MO Missouri 

MSA Mineralogical Society of America 

MYB Minerals Yearbook (USGS) 

NAA Nonattainment Area 

NAS National Academy of Science 

NATA National Air Toxics Assessment 

NC North Carolina 

NEI National Emission Inventory 

NESCAUM Northeast States for Coordinated Air Use Management 

NESHAP National Emission Standards for Hazardous Air Pollutants 

NM New Mexico 

NOX Oxides of Nitrogen (also Nitrogen Oxides) 

NRC National Research Council 

NV Nevada 

O&M Operations and Maintenance 

OCAS Optimization of Compressed Air Systems 

OGR Optimize Grate Cooler 

OK Oklahoma 

OMB U.S. Office of Management and Budget 
vii 

OR Oregon 

PA Pennsylvania 

PC Pulverized Coal 

PCA Portland Cement Association 

PCVM Process Control Vertical Mill 

PM Particulate Matter 

PRB Powder River Basin 

PSD Prevention of Significant Deterioration 

RMB Raw Materials Blending 

RMC Ready-Mix Concrete 

RMTHEC Raw Materials Transport High Energy Classifiers 

RTO Regenerative Thermal Oxidizer 

SBH Slurry Blending and Homogenization 

SC South Carolina 

SCR Selective Catalytic Reduction 

SFC Specific Fuel Consumption 

SG&A Sales, General and Administration 

SHLR Shell Heat Loss Reduction 

SLC Salt Lake City 

SNCR Selective Non-Catalytic Reduction 

SOX Oxides of Sulfur 

SPE Spatial Price Equilibrium 

SR Seal Replacement 

TDF Tire Derived Fuel 

USGS United States Geological Survey 

UT Utah 

WA Washington 

WBCSD World Business Council for Sustainable Development 

WMCCC Wash Mills with Closed Circuit Classifier 
viii 

Conversion Table – English Units to SI Units 

To Obtain From Multiply by 

m ft 0.3048 

m

2 ft2 9.29 × 10-2 

m

3 ft3 2.83 × 10-2 

°C °F 5/9 × (°F – 32) 

kg lb 0.454 

J/kg Btu/lb 1.33 × 10-4 

m

3

/s cfm 4.72 × 10-4

m

3

/s gpm 6.31 × 10-5

J/kWh Btu/kWh 1055.056 

mills $ 0.001 

kg/m2 in. Hg 345.31 

metric ton short ton 0.907 
1-1 

Chapter 1 

Introduction 

In the National Academy of Science’s 2004 report, “Air Quality Management in the United States,” the 

National Research Council (NRC) recommended to the U.S. Environmental Protection Agency (EPA) 

that standard setting, planning, and control strategy development should be based on integrated 

assessments that consider multiple pollutants, and that these integrated assessments should be conducted 

in a comprehensive and coordinated manner (NAS, 2004). With these recommendations, EPA began to 

move toward establishing multipollutant and sector-based approaches to manage air quality and 

environmental protection. The benefits of multipollutant and sector-based analyses and approaches 

include the ability to identify optimum strategies (considering feasibility, costs, and benefits across all 

pollutant types such as criteria, toxics, and others), while streamlining administrative and compliance 

complexities and reducing conflicting and redundant requirements. 

The development of policy options for managing emissions and air quality can be made more effective 

and efficient through sophisticated analyses of relevant technical and economic factors. Such analyses are 

greatly enhanced by the use of an appropriate modeling framework. Accordingly, the Industrial Sectors 

Integrated Solutions (ISIS) model has been developed at EPA (ARCADIS, 2010). Currently, the ISIS 

model is populated with US cement manufacturing data, and efforts are underway to build representations 

of the US pulp and paper sector and the US iron and steel sector. This document describes the framework 

of EPA’s ISIS model and its application to the US cement manufacturing industry.

1.1 The US Cement Industry 

1.1.1 Cement Types and Categories 

Cement is a finely ground powder which, when mixed with water, forms a hardening paste of calcium 

silicate hydrates and calcium aluminate hydrates. Cement is used in mortar (to bind together bricks or

stones) and concrete (bulk rock-like building material made from cement, aggregate, sand, and water). 

Concrete production uses the majority of cement produced. 

Portland cement and blended cement are used in concrete production, but portland cement is by far the 

most common type of cement used for concrete production. By modifying the raw material mix and, to 

some degree, the temperatures utilized in manufacturing, slight compositional variations can be achieved 

to produce portland cements with slightly different properties. In the U.S., the different varieties of 

portland cement are denoted per the American Society for Testing and Materials (ASTM) Specification 

C-150. The ASTM standard C-150 recognizes eight types of portland cement: 

• Type I is for use in general construction (e.g., buildings, bridges, floors, etc.). 

• Type IA is similar to Type I with the addition of an air-entraining agent. 

• Type II generates less heat at a slower rate and has a moderate sulfate-attack resistance. 

• Type IIA is similar to Type II with the addition of an air-entraining agent. 
1-2 

• Type III is used when concrete must set and gain strength rapidly. 

• Type IIIA is similar to Type III with the addition of an air-entraining agent. 

• Type IV has low heat of hydration and slow strength development. 

• Type V is used when concrete must resist high sulfate concentration in soil and groundwater. 

Portland cements are usually gray, but a more expensive white portland cement (generally within the 

Type I or II designations) can be obtained by processing only raw materials with very low iron and 

transition-elements content. In addition to the eight types of portland cement listed above, small volumes 

of specialty cements are also manufactured. Other types of specialty cement include blended cement 

(portland cement mixed together with blast furnace slag or other pozzolan), pozzolan-lime cement, 

masonry cement, and aluminous cement (PCA, 2008). The common industry practice, and that of the US 

Geological Survey (USGS), includes, within the portland cement designation, a number of other cements 

not within ASTM C-150, which are composed largely of portland cement and are used for similar 

applications (e.g., concrete) (USGS, 2005). These concretes include blended cement, block cement, 

expansive cement, oil well cement, regulated fast setting cement, and waterproof cement. Plastic cements 

and portland-lime cements are being grouped within the masonry cement designation. Because portland 

cement accounts for approximately 95 percent of the cement industry’s total production (van Oss, 2008), 

and because the costs and trends of this industry sector can be adequately captured by describing the 

market processes associated with the production, distribution, and use of portland cement, in this work the 

focus is on portland cement. In 2006, portland cement’s market share in the U.S. was 94 percent, while

masonry cement’s market share comprised the remaining 6 percent (USGS, 2007a). 

1.1.2 Overview of the Cement Manufacturing Process 

Portland cement is produced from raw materials such as limestone, chalk, shale, clay, and sand. These 

raw materials are quarried, crushed, finely ground, and blended to the correct chemical composition. 

Small quantities of iron ore, alumina, and other minerals may be added to adjust the raw material 

composition. The fine raw material is fed into a large rotary kiln (cylindrical furnace) where it is heated to 

extremely high temperatures (about 2640 °F [about 1450 °C]). The high temperature causes the raw 

material to react and form a hard nodular material called “clinker”. Clinker is cooled and ground with

approximately 5-percent gypsum and other minor additives to produce portland cement. The main steps in

the cement manufacturing process are illustrated in Figures 1-1 and 1-2 showing the wet process and the 

dry process with cyclone preheater. The schematic for a precalciner kiln would be very similar to that

shown in Figure 1-2, with the addition of a calciner vessel. 

The heart of the clinker production process is the kiln, which can be rotary or vertical shaft designs. 

Rotary kilns are commonly used in the U.S. and elsewhere. These kilns are 6-8 m in diameter and 60 m to 

well over 100 m long. The kilns are set at a slight incline and rotate at 1 to 3 revolutions per minute. The 

kiln is fired at the lower end and the feed materials move toward the flame as the kiln rotates. The 

materials reach temperatures between 1400-1500 °C in the kiln. Three steps occur with the raw material

mixture during pyroprocessing. First, all moisture is driven off from the materials. Then the calcium 

carbonate in limestone decomposes into CO2 and calcium oxide (free lime) during calcination. Finally, 

the lime and other minerals in the raw materials react to form calcium silicates and calcium aluminates, 

the main components of clinker. 
1-3 

Classifier

Stack

Coal

Feeder

Classifier

Figure 1-1. Schematic of the Wet Cement Process Source: CEMBUREAU, 1999 
1-4 

Classifier

Coal

Feeder

Classifier

Figure 1-2. Schematic of the Dry Cement Process with Cyclone Preheater Source: CEMBUREAU, 1999 
1-5 

1.1.3 Kiln Types and Their Use 

Rotary kilns are broadly categorized as dry- and wet-process kilns, depending on how the raw materials

are prepared. Wet-process kilns are fed raw material slurry with moisture content ranging between 30 and 

40 percent. A wet-process kiln needs additional length to evaporate the water contained in the raw 

material feed. Nearly 33 -percent additional kiln energy is consumed in evaporating the water in the 

slurry. 

In dry-process kilns, raw material is fed as dry powder. There are three major variations of dry-process 

kilns in operation in the U.S.: long dry kilns, preheater kilns, and preheater/precalciner kilns. In preheater 

kilns and preheater/precalciner kilns, the early stages of pyroprocessing occur before the materials enter 

the rotary kiln. Preheater and preheater/precalciner kilns have higher production capacities and greater 

fuel efficiency compared to other types of cement kilns. Table 1-1 shows heat input in terms of millions 

of British Thermal Units (MMBtu)/ton1 of clinker for various types of kilns. As the data clearly 

demonstrate, preheater/precalciner kilns provide greater fuel efficiency. The replacement of wet and 

(certain) dry process kiln capacity with modern kiln processes can yield, theoretically, substantial 

reductions in fuel use due to fuel efficiency gains. As the industry moves toward more efficient processes, 

replacement of wet and long dry process capacity with more efficient kiln process technologies is 

expected. 

Table 1-1. Typical Average Heat Input by Cement Kiln Type 

Kiln Type 

Heat Input, 

MMBtu/ton of clinker 

Wet 6.0 

Long Dry 4.5 

Preheater 3.8 

Preheater/Precalciner 3.3 

Source: EPA, 2007 (Table 3-3) 

As expected, a recent trend in the cement sector has shown the replacement of lower capacity, inefficient 

wet and long dry kilns with bigger and more efficient kilns. This trend is expected to continue. In the 

U.S., the overall number of kilns decreased by 11 percent from 1995 to 2004. During the same period, 

total clinker production capacity increased by 18.6 percent. The Portland Cement Association (PCA) data 

show that average kiln capacity also increased by 27 percent, from 405,000 to 556,000 tons per year 

between 1995 and 2004. The number of kilns operating in the U.S. in 2005 compared with the number of 

kilns in operation in 2009 is shown in Table 1-2. The trend in kiln design and average kiln capacity is 

shown in Figure 1-3. 

 

1 PLEASE NOTE: for the purposes of this document, short tons will be referred to simply as “tons” and 

represents 2000 lbs. A unit conversion table is provided in the front matter of this document.
1-6 

Table 1-2. Number of Kilns by Kiln Type in the U.S. in 2005 and 2009 

Kiln Type Number of Kilns (2005) Number of Kilns (2009) 

Wet 50 42 

Dry 48 32 

Preheater 36 29 

Precalciner 47 58 

Source: PCA, 2006 and PCA, 2009a 

0

100

200

300

400

500

600

0

25

50

75

100

125

150

175

200

225

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Average Capacity, thousands of metric 

tons/year

Number of kilns

Dry Kilns Wet Kilns Average Kiln Capacity

Figure 1-3. Trends in Cement Kiln Type and Capacity in the U.S. (1995 to 2008) 

Note: The PCA Plant Information Summary became biennial in 2004. The 2005 data were obtained by 

personal communications between EPA and the PCA. 

As reported by the USGS, in 2005, the U.S. portland cement industry produced approximately 87 million 

metric tons of clinker, of which 13.5 percent was produced by plants with wet process kiln systems while 

81 percent was produced by plants operating only dry kilns of all three types (long-dry, preheater and

preheater/precalciner). The remaining 5.5 percent was produced by plants that operate both wet and dry

kilns (USGS, 2007b: Table 7). 
1-7 

1.1.4 Portland Cement Production in the U.S. 

The cement industry remains a vital industry in the U.S. and throughout the world. In the U.S., portland 

cement is a $12 billion industry (van Oss, 2008). In 2005, the U.S. consumed a record 128 million metric 

tons of portland cement (USGS, 2007b: Table 1). 

In 2005, portland cement was produced at 107 plants in the U.S., including 37 states and Puerto Rico. The 

locations of the clinker-producing portland cement plant facilities in 2005 are shown in Figure 1-4. The 

cement manufacturing sector in the U.S. is concentrated among a relatively small number of companies, 

many of which are owned by or are subsidiaries of foreign companies (USGS, 2007b). Together, ten 

companies accounted for about 80 percent of the total cement production in U.S. for 2005 (USGS, 

2007b). California, Texas, Pennsylvania, Florida, and Alabama are the five leading cement-producing 

states, which accounted for about 43 percent of the total production in 2005 (USGS, 2007b: Table 3). 

In 2009, PCA projected a capacity expansion of 27 million tons between 2008 and 2013, an 18 -percent 

increase in existing capacity from 2006. PCA’s projected capacity expansions are to come from 23 kilns; 

5 came on-line in 2008 and 18 more are expected to come on-line between 2009 and 2013 (PCA, 2009b). 

The investment in these projected capacity expansions is projected at $6.9 billion. Note that a typical 

project for a new facility (greenfield) from ground breaking to startup has a timeframe of about 2 to 3 

years. Building a new kiln in an existing facility (brownfield) can take approximately 1 year (EPA, 2007). 

If permitting for mining and (re)construction is accounted, a typical project can take as long as 4 to 6 

years for a greenfield facility and up to 3 years for a brownfield facility (Andover Technology Partners, 

2009a). 

Mississippi-Hudson River

Cement Plant Locations

Largest Cement Import Custom Districts

Cement Import Custom Districts

Figure 1-4. Portland Cement Plant Locations 
1-8 

1.1.5 Imports of Portland Cement in the U.S. 

Portland cement is not only produced and consumed domestically, but it is also traded internationally. In 

2005, the U.S. (not including Puerto Rico) produced 94 million metric tons of cement (USGS, 2007b: 

Table 3) and imported 34 million metric tons of hydraulic cement and clinker (USGS, 2007b: Tables 3 

and 18). The level of imports to the U.S. is highly cyclical, with domestic producers importing primarily 

when domestic plants are at full capacity and cannot meet excess demand. Imports also occur when 

domestic producers seek to capture market share. Generally, imported cement and clinker make up 20 to 

27 percent of domestic cement consumption. In 2005, total imports of cement and clinker (especially 

clinker) increased, owing to continued high demand; imported cement accounted for about 24 percent of 

the total cement sales in the U.S. (USGS, 2007b). 

In 2005, the ten leading international cement and clinker suppliers to the U.S. were, in descending order, 

Canada, China, Thailand, Greece, the Republic of Korea, Venezuela, Mexico, Colombia, Taiwan, and 

Sweden. The ten busiest ports of entry within customs districts existing in 2005 were, in descending 

order, New Orleans, Tampa, Los Angeles, Houston-Galveston, San Francisco, Miami, Seattle, Detroit, 

New York, and Charleston (South Carolina) (USGS, 2007b). Table 1-3 shows the major customs districts 

for hydraulic cement and clinker imports in the U.S. Figure 1-5 shows the imports of clinker and cement 

from 1998 to 2008. 

Table 1-3. Largest Hydraulic Cement and Clinker Import Custom Districts in the U.S. in 2005 

Import of Hydraulic Cement and Clinker 

Custom District 

thousands of metric tons thousands of tons 

Percentage 

of Total US 

Imports 

New Orleans, LA 4,095 4,514 12.31 

Tampa, FL 3,478 3,834 10.46 

Los Angeles, CA 3,053 3,365 9.18 

Houston-Galveston, TX 2,619 2,887 7.87 

San Francisco, CA 2,363 2,605 7.10 

Miami, FL 2,265 2,497 6.81 

Seattle, WA 1,489 1,641 4.48 

Detroit, MI 1,317 1,452 3.96 

New York, NY 1,264 1,394 3.80 

Charleston, SC 1,102 1,215 3.31 

Total 23,046 25,404* 69.29%

* rounding, original data in metric tons 

(Source: USGS, 2007b (Table 18) 
1-9 

Thousands of Metric Tons

Figure 1-5. Imports of Clinker and Cement from 1998 to 2008 (PCA, 2009a) 

1.1.6 Cement Demand Centers 

Because of the relatively high transportation costs, the U.S. cement industry is structured around statespecific cement demand centers. PCA reports that the vast majority of cement produced in the U.S. is 

being transported less than 300 miles by truck due to cement's low value by weight and high cost of 

transport (PCA, 2005). However, cement may be transported over longer distances, especially when the 

less expensive rail and water transportation modes are available (APCA, 1997). 

1.2 Emissions from the U.S. Cement Industry and Applicable Regulations 

Criteria pollutants, hazardous air pollutants, and CO2 are released during cement manufacturing. Nitrogen

oxides (NOX) emissions from cement kilns result primarily from the combustion process: oxidation of 

fuel nitrogen (fuel NOX) and the oxidation of nitrogen in the combustion air (thermal NOX). EPA’s 2005 

National Air Toxics Assessment (NATA) Inventory reports that cement kilns released 181,000 metric 

tons (200,000 short tons) of NOX emissions from the combustion of fuels (EPA, 2009). 

Sulfur dioxide (SO2) emissions2

 from cement kilns result from the combustion of sulfur-bearing 

compounds in coal, oil, and petroleum coke, as well as sulfur compounds in raw materials. Sulfur in the 

fuel will oxidize to SO2 during pyroprocessing and a significant amount is likely to be captured in the 

form of sulfates as the flue gas passes through the calcination zone. Compared to long dry and wet kilns, 

preheater and precalciner kilns tend to be more effective at capturing fuel-generated SO2. Accordingly, 

oxidation of sulfur in the feed materials is likely to be the major component of total SO2 emissions. The 

 

2

 Small amounts of SO3 may be released in addition to bulk SO2 but the SO3 emissions are treated as SO2 for computational 

purposes. Small amounts of CO may be released in addition to bulk CO2

 but CO emissions are treated as CO2 for computational 

purposes. 
1-10 

2005 NATA Inventory reflects that cement kilns released 133,000 metric tons (147,000 short tons) of SO2

emissions in 2005. 

Particulate matter (PM) emissions result from quarrying operations (the crushing and grinding of raw 

materials and clinker) as well as kiln line operations. The 2005 NATA Inventory estimates show that, in 

2005, cement kilns released 10,000 tons of PM10 emissions. The cement industry is also a source of HAPs 

(e.g., hydrochloric acid vapor and chlorine), as well as metals including but not limited to mercury, 

antimony, cadmium, and lead (EPA, 2008). 

The cement manufacturing process is also a source of carbon dioxide (CO2) emissions. CO2 emissions are 

a product of the combustion of fuel as well as the calcination of the limestone in the raw meal. The CO2

from fuel combustion can be calculated from heat input and fuel characteristics combined with clinker 

production. The CO2 from calcination can be calculated taking into account the amount of limestone used 

as a source of clinker calcium. Limestone currently is the predominant source of calcium for the clinker. 

Pure limestone would produce 0.44 tons of CO2 for every ton of limestone completely calcined to calcium 

oxide. Substitute materials may be used in lieu of limestone with the effect of reducing the CO2 emissions 

from clinker production. The CO2 emissions reduction would be proportional to the amount of substitute 

materials added. Detailed discussion of CO2 emissions from cement kilns can be found in Appendix A. 

Multiple regulatory requirements to reduce criteria air pollutants and HAPs emissions currently apply to 

the cement industry sector. The New Source Performance Standards (NSPS) and the National Emissions 

Standards for Hazardous Air Pollutants (NESHAPs) are two of the federal requirements that apply to 

cement facilities. Additionally, state and local regulatory requirements might apply to individual cement 

facilities depending on their locations. In 2008, 44 cement facilities were located within ozone (O3) 

nonattainment areas (NAA), while 20 facilities were within PM2.5 nonattainment areas. Seventeen 

facilities were found to be located in (or within 30 miles of) Class 1 areas, as shown in Figure 1-6. Class 1 

areas are areas of special natural, scenic, recreational, or historic (national or regional) value for which the 

Prevention of Significant Deterioration (PSD) regulations provide special protection. 

1.3 Overview of ISIS 

ISIS, a sector-based dynamic programming model, will facilitate the analyses of emission reduction 

strategies for multiple pollutants while taking into account plant-level economic and technical factors 

such as the type of emission units (for cement –kiln), associated capacity, location, cost of production, 

applicable controls, and their costs. For each of the emission reduction strategies under consideration, the 

model is able to provide information on the following: 

• Optimal (least cost) industry operation 

• Cost-effective controls to meet the demand for cement 

• Emission reduction requirements over the time period of interest 

ISIS design allows for incorporating multiple industries within a multi-market, multi-product, 

multipollutant, and multi-region emissions trading framework. The objective function in ISIS maximizes

total surplus and uses an elastic formulation of the demand function to estimate area under the demand

curve. 
1-11 

Figure 1-6. Portland Cement Facilities and O3 NAA, PM2.5 NAA, and Class 1 Areas 

The ISIS code is written in the General Algebraic Modeling System (GAMS) language. Input data, 

organized in various spreadsheets of a Microsoft Excel workbook, are passed on to the GAMS files. 

These input data consist of an industry database, which provides unit-level production, capacity, 

production cost, and emissions information. The controls database provides information regarding 

applicable air pollution control technologies and their cost and emission control characteristics. A policy 

module is used to specify various parameters of interest to the policy analyst, such as emissions cap,

emission reduction scenarios, and discount rate. The input data, control data, and policy parameters are 

then transmitted to the optimization part of the ISIS model, where they are used to solve the selected base 

and policy cases. After solving, the results are post-processed to calculate values of various outputs of 

interest. The output data are exported to Excel spreadsheets for further analyses and graphical 

representation of selected results. 

Within an industrial sector, generally emissions arise from four pathways: (1) on-site emissions due to 

combustion of fossil fuels for energy at plants, (2) on-site emissions due to processing of certain raw 

materials (e.g., limestone calcination in cement plants, non-energy uses of fossil fuels in chemical 

processing and metal smelting), (3) off-site emissions due to combustion of fossil fuels at power plants to 

generate the electricity needed by the industrial sector, and (4) overseas emissions associated with 

imports. These pathways are depicted in Figure 1-7.
1-12 

Also shown in Figure 1-7 are the potential options for abating emissions from industrial sectors and 

multimedia impacts. The options shown in green are pollution prevention measures and the ones in red 

are mitigation measures. Clearly, the integrated picture presented in Figure 1-7 makes a compelling case 

for considering commodity production/supply activities along with emissions while developing holistic 

emission reduction strategies. While developing the ISIS framework, care has been taken to build the 

emission pathways and abatement options shown in Figure 1-7. Example emission reduction policies that 

can be evaluated using ISIS are: 

• Criteria pollutants (NOX, SO2, PM, CO) –emission limits and/or cap-and-trade 

• Hazardous Air Pollutants (e.g. Hg, HCl) – emission limits 

• CO2 – cap-and-trade and/or emission taxes 

• Long and short time horizons: CO2 (decades), criteria pollutants (annual) 

• Regional or national requirements 

Pollution Controls (NOx, SO2,PM, 

CO2, VOC, .. )

Industrial 

Plants

Improvements

Fuel(s)

Pollution Reduction Policy 

(Rate-based, cap-and-trade, 

emission taxes)

Air emissions

Product(s)

Product Substitution

Fuel Substitution/ 

Reduction (NOx, SO2, PM, 

CO2, ...)

Raw Materials 

Raw Material Substitution

Electricity

Change in use (Power 

plant emissions)

Water

Waste 

Market(s)

Imports

(Overseas emissions)

Figure 1-7. An Integrated View of Pollution Generation Pathways, Emissions Abatement 

Approaches, and Multimedia Impacts for an Industrial Sector 
1-13 

References for Chapter 1 

APCA (1997). Comments on EPA’s Draft Economic Analysis of Air Pollution Regulations for the 

Portland Cement Industry (May 1996), prepared for American Portland Cement Alliance by 

Environomics, January 29, 1997. 

ARCADIS (2010). Development of U.S. EPA Industrial Sector Integrated Solutions (ISIS) Model, 

Quality Assurance Project Plan. Prepared under contract number EP-C-09-027 for the U.S. 

Environmental Protection Agency, Air Pollution Prevention and Control Divisions, Research Triangle 

Park, NC. February 3, 2010.

CEMBUREAU (1999). Best Available Techniques for the Cement Industry, CEMBUREAU Report, The 

European Cement Association, December 1999. D/1999/5457/December, Brussels. 

http://www.cembureau.be

EPA (2007). Alternative Control Techniques Document Update – NOX Emissions from New Cement 

Kilns. EPA-453/R-07-006, November 2007. http://www.epa.gov/ttn/catc/dir1/cement_updt_1107.pdf, 

accessed October 21, 2008. 

EPA (2008). AP 42, Fifth Edition, Compilation of Air Pollutant Emission Factors, Volume I, Chapter 11:

Mineral Products Industry. http://www.epa.gov/ttn/chief/ap42/ch11/final/c11s06.pdf, accessed October 

21, 2008. Complete document: http://www.epa.gov/ttn/chief/ap42/, accessed October 21, 2008. 

EPA (2009). EPA 2005 National Air Toxics Assessment Inventory. U.S. Environmental Protection 

Agency. October 2009. 

NAS (2004). Air Quality Management in the United States. National Research Council (U.S.), Committee 

on Air Quality Management in the United States, National Academies Press, Washington, 2004. 

http://books.nap.edu/catalog.php?record_id=10728, accessed October 21, 2008. 

PCA (2005). Letter from David S. Hubbard, Director, Legislative Affairs, Portland Cement Association. 

RE: Hours of Service of Drivers; Proposed Rule (Docket Number FMCSA-2004-19608), March 10, 

2005. http://www.cement.org/exec/DHOS%20Comments percent203.10.05.pdf, accessed October 21, 

2008. 

PCA (2006). U.S. and Canadian Portland Cement Industry: Plant Information Summary. Portland Cement 

Association, Skokie, IL, 2006. 

PCA (2008). History & Manufacture of Portland Cement. Portland Cement Association. 

http://www.cement.org/basics/concretebasics_history.asp, accessed October 21, 2008. 

PCA (2009a). PCA Annual Yearbook 2009. North American Cement Industry. Portland Cement 

Association. 

PCA (2009b). PCA Capacity Report. Flash Report. Portland Cement Association’s Economic Research 

Department. Updated October 19, 2009. 

Andover Technologies Partners (2009a). Memorandum: NOX, SO2 and CO2 Emissions from Cement Kilns 

(Emissions Memo), from Jim Staudt to Ravi Srivastava, Samudra Vijay, Elineth Torres. Dated March 10, 

2009 (see Appendix A of this document). 
1-14 

USGS (2005). Background Facts and Issues Concerning Cement and Cement Data. U.S. Geological 

Survey, Open-File Report 2005-1152. http://pubs.usgs.gov/of/2005/1152/2005-1152.pdf, accessed 

October 21, 2008. 

USGS (2007a). Mineral Commodity Summaries. pp. 40-41, January 2007, 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/cemenmcs07.pdf, accessed September 24, 

2009. 

USGS (2007b). 2005 Minerals Yearbook: Cement. U.S. Geological Survey, p. 16.2, February 2007, 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/cemenmyb05.pdf, accessed September 24, 

2009. 

van Oss, H.G. (2008). Personal communication from Hendrik G. van Oss, USGS, to Elineth Torres, US 

EPA, on July 7, 2008. 
2-1 

Chapter 2 

ISIS Mathematical Framework 

ISIS is a sector-based dynamic linear programming model that can determine optimal sector operation for 

meeting demand and pollution reduction requirements over specified time periods. The objective in an 

ISIS simulation is to maximize total surplus (see Figure 2-1) over a horizon of interest for an industry, 

which, in general, can be a multi-product one. 

The general concept of spatial price equilibrium (SPE) in a network, where the mutual influences of 

production, transportation, and consumption patterns are given full consideration, has been developed 

over the past 6 decades. In SPE network models, interregional economies are simulated by finding the 

balance of demand, supply, and trade that will result in competitive market equilibrium among the 

regions. Enke (1951) first demonstrated how the cost of transportation might be included in an 

equilibrium analysis of spatially separated markets by means of analogy with resistance to the flow of

current in an electric circuit. Shortly after Enke, Samuelson (1952) analyzed interregional flows of 

commodities and market equilibrium using a linear programming formulation. In this type of formulation, 

the equilibrium for each market of a sector is equivalent to the quantities and prices that result while 

maximizing the sum of consumer and producer surpluses for each market of the sector. This sum is 

referred to as the total surplus or net social payoff of the sector; McCarl and Spreen (1980) provide 

interpretation and justification. The linear programming formulation of the SPE problem was developed 

by Duloy and Norton (1975). 

Using Figure 2-1, the definition of the suppliers’ surplus corresponding to a quantity Q of a commodity is 

the difference between the total revenue and the total cost of supplying the commodity. This surplus 

(gross profit) is given by the area under the horizontal line P1-P minus the area under the inverse supply 

curve up to point P. Similarly, the consumers’ surplus corresponding to a quantity Q is given by the area 

under the inverse demand curve up to point C minus the area under the horizontal line C1-C. This area is 

the cumulative opportunity gain of all consumers who purchase the commodity at a price lower than the 

price they would have been willing to pay. It is evident from Figure 2-1 that the total surplus is 

maximized exactly when Q is equal to the equilibrium quantity QE. This is a very useful result, as it 

provides a method for computing the equilibrium. 

The total surplus concept has long been a mainstay of social welfare economics because it takes in to 

account the interests of both consumers and of producers (Samuelson and Nordhaus, 1977). 

The broad modules and associated information flows utilized in the ISIS framework are shown in Figure 

2-2. While ISIS structure permits accounting for multiple products, the description below is provided 

relative to one product to bring out the important elements of the formulation and not burden the reader 

with many details. Also, to make the description more readable and understandable, the input data (i.e., 

those supplied by the user, or derived from user-supplied data) are shown in bold and the variables, 

whose values are determined in the optimization process, are shown in italics. This scheme helps in 

organizing the numerous data elements and variables, and hopefully makes it easier for the reader to 

relate the data element to descriptions in the previous chapters. Finally, the names of variables and data 

elements have been chosen to be self-explanatory as far as possible. 
2-2 

Price

Supply curve

Quantity

Demand curve

Equilibrium

Consumer

surplus

Producer

surplus

C1

P1

Q QE

P

C

Figure 2-1. Consumer and Producer Surplus in a Market 

ISIS Engine

Industry Information

• Products

• Production capacities and 

costs

• Demand 

• Imports parameters

• Transportation costs

• Noted retirements and growth

• Energy Intensity

Production Technologies 

and Emission Abatement 

Approaches

Availability, applicability, 

cost, and performance

Policy Parameters

• Time horizon

• Policy type

Model Outputs

• Base case and controlled emissions

• Production, Inter-market trades, imports

• Commodity and allowance prices

• Retirements and capacity growth

• Controls installed

• Costs

• Other

Economic Parameters

• Discount rate

• Escalation rates

• Demand elasticity

• Economic life

Fuels

• Primary: coal, coke, 

natural gas, coal+TDF

• Substitute: tires, TDF

Emissions

• Sulfur dioxide

• Nitrogen oxides

• Carbon dioxide

Figure 2-2. Broad Modules and Associated Information Flows Utilized in the ISIS Framework 
2-3 

2.1 Indexes, Sets, and Mappings 

Before we start the description of the mathematical structure of ISIS, it is helpful to define the sets of 

relevant entities and mappings describing the relationships among various entities. 

Indexes (one-dimensional sets)

• ISIS data structures (sets and parameters), variables, and equations use the following indexes: 

• t is the set of years in the time horizon of interest;

• i is the set of all units, including existing, replacement, expansion, and new units. Subsets of i

described below define more-specific populations of units;

o iE(i) is the set of existing units 

o irepl(i) is the set of replacement units 

o iExp(i) represents the set of expansion units 

o iNew(i) represents the set of new units

• f is the set of fuels applicable to the sector;

• m is the set of raw materials specific to the sector;

• j is the set of sector-specific pollutants of interest;

• k is the set of sector-specific controls relevant to the emission reduction policy of interest;

• ee is the set of energy efficiency measures; and

• r is the set of geographic regions where units are located.

Mappings

The mappings used in ISIS are:

• ri(i,r) is the mapping relating a unit i to its geographic location r;

• poltikjee(t,i,k,j,ee) is the mapping relating availability, applicability, and the ability to change 

emissions of controls k and energy efficiency measures ee to unit i, pollutant j, and year t; 

• poltik(t,i,k) is the mapping relating availability and applicability of controls k to unit i and year t; 

• poltiee(t,i,ee) is the mapping relating availability and applicability of energy efficiency measures ee

to unit i and year t; 

• poltirk(t, ri(i,r),k) is the mapping relating availability and applicability of controls k to unit i located 

in geographic region r and year t; and 
2-4 

• poltiree(t, ri(i,r),ee) is the mapping relating availability and applicability of energy efficiency 

measures ee to unit i located in geographic region r and year t. 

2.2 Objective Function 

As mentioned above, the objective function solved in the ISIS model corresponds to maximizing the total 

surplus (or minimizing the negative of the total surplus) for the sector of interest over the selected time 

horizon. This objective function is: 

∑ ∫

∑

∑

∑

∑

∑

∑













− ⋅ ⋅

⋅ ⋅ +

⋅ +

⋅ +

⋅ +

⋅ +

= ⋅ +

t dc

t,j

t,i,ee

t,i,k

t,id

t,dc

t,i

price(t,dc ) ddemand(t, dc)

totalemissions(t,j)

teemeasure scost(t,i, ee)

tcontrolco st(t,i,k)

timportsco st(t,id)

ttranscost(t,dc)

z tannualpro dncost(t,i)

dis(t)

dis(t) alprice(t, j)

dis(t)

dis(t)

dis(t)

dis(t)

Minimize dis(t)

(2.2.1) 

where the quantities appearing in the equation above are defined for year t as follows, 

dis(t) is the discount factor, 

tannualprodncost(t,i) is the annual production cost ($) for production unit i, 

ttranscost(t,dc) is the cost ($) of transporting the product to demand center dc, 

timportscost(t,id) is the cost ($) of importing foreign product in import district id, 

tcontrolcost(t,i k) is each unit’s total control cost ($) using control technology k, 

teemeasurescost(t, i, ee) is each unit’s total cost ($) using the energy efficiency measures ee, 

alprice(t,j) is the allowance price input by the user, 

totalemissions(t,j) is emission of pollutant j from the sector, 

∫

⋅

dc

price t,( dc) ddemand t,( dc) is the area under the demand-price curves for all markets dc. 
2-5 

Note that in the objective function the term with alprice(t,j) comes into effect only if the user provides 

values for alprice(t,j). If these values are specified, the model runs as described under the “Allowance 

Price Inputs” in a later section. 

A stepwise approximation of the demand curves (FPL-PELPS, 2003) is used in ISIS so that relevant area 

can be computed in a linear programming scheme. This approximation is explained below. 

Stepwise Approximation of Demand Curves in ISIS

For clarity, the subscripts t and dc corresponding to time and demand center are dropped in the following 

explanation. 

The relationship between demand and price in a market dc is expressed as 

σ

1

D0

P0 











=

D

P(D) (2.2.2) 

where 

D is the demand for the commodity with corresponding price P(demand), 

σ is the elasticity of demand relative to price, and

D0 and P0 are the initially-specified demand quantity and price, respectively. 

Figure 2-3 shows a representation of the above equation and also reflects how a stepwise approximation

of the price-demand curve can be created. 

P(d1)

P(d2

)

P(d3

)

P(d4)

P0

Dmin D0 Dmax

d1 d2 d3 d4

P(D)

Dwid

Dwid

Price

Quantity

Figure 2-3. Stepwise Calculation of the Demand Curve 
2-6 

First the range of the demand-price curve is defined using 

Dmin = D0 ⋅(1 - range) (2.2.3) 

Dmax = D0 ⋅(1 + range) (2.2.4) 

where 

range is a user-supplied parameter with value between 0 and 1. This parameter defines the interval 

Dmin - Dmax within which the new equilibrium demand quantity is expected to be found. Note range 

should be large enough to ensure that the solution does fall in the interval Dmin - Dmax. On the other 

hand a smaller value of range can increase precision of the stepwise approximation. In ISIS a value 

of 0.5 is used for range. 

Next, a series of steps is defined within the interval Dmin - Dmax with the width of each step given by 

Number of steps

D - D

dwidth max min = (2.2.5) 

where 

Number of steps is another user-supplied parameter. Increasing the value of this parameter will 

increase precision of the stepwise approximation, but will increase the model size. In ISIS, a value of 

100 is used for Number of steps. 

Now the demand quantity at the center of the slice dstep inside the interval is 

2

dwidth (2 dstep 1)-

D Dmin

dstep ⋅ ⋅

= + (2.2.6) 

Using the above information, the price-demand curve is determined 

σ

1

dstep

dstep

D0

D

P D P0 















( ) = (2.2.7) 

Finally, the approximated area under the price-demand curve is 

( )

dstep dstep D P D

Number of steps

dsteps 1

∑=

⋅

(2.2.8) 
2-7 

Substituting (2.2.8) in (2.2.1), the objective function becomes 

∑ ∑

∑

∑

∑

∑

∑

∑













− ⋅ ⋅

⋅ ⋅ +

⋅ +

⋅ +

⋅ +

⋅ +

= ⋅ +

t dstep dc

t,j

t,i,ee

t,i,k

t,id

t,dc

t,i

demand t dc dstep

totalemissions(t,j)

teemeasurescost(t,i,ee)

tcontrolcost(t,i,k)

timportscost(t,id)

ttranscost(t,dc)

z tannualprodncost(t,i)

dis(t) P(demand(t,dc, dstep)

dis(t) alprice(t, j)

dis(t)

dis(t)

dis(t)

dis(t)

dis(t)

,

,( , )

Minimize

(2.2.9) 

The above objective function is minimized with the constraints and related equations described in the 

following sections to arrive at the relevant optimum solution. 

Equation (2.2.7) is used to generate the price-demand curves for all time periods and demand centers. 

However, this equation needs specifications of one point on each demand function in each time period 

(i.e., [P0{t, dc}, D0{t, dc}]). To determine such points, a single run of the inelastic version of the ISIS 

model (with exogenous D0[t, dc]) is made and then the resulting shadow prices P0(t, dc) are used in 

Equation (2.2.7) to generate price-demand curves for all time periods and demand centers. These curves

are used in the last term of the objective function and in the supply constraint, Equation (2.3.1), presented 

in the next section. 

2.3 Supply 

The demand for a commodity in a market can be satisfied by domestic production and foreign imports as 

follows: 

∑ ,,( ) +∑ ,( , ) ≥ ∑ ,( , .)

i id dstep

prodnquantity it dc importedquantity idt dc demand t dc dstep (2.3.1) 

where 

prodnquantity(t,i,dc) is the quantity supplied from the domestic production unit i to demand center dc

in year t, 

importedquantity(t,oi,id,dc) is the quantity of commodity received from the origin (country) of 

imports oi at the domestic import district id and supplied from that district to demand center dc in 

year t, and 

demand(t, dc,dstep) is the demand for the commodity at the dstepth level of the price-demand curve 

for the demand center dc in year t. The construction of the price-demand curves has been explained 

above. 
2-8 

Note that transportation of quantity from production units and import districts to demand centers is 

implicit in the above equation. 

The sum of all quantities in year t supplied from a kiln i to various demand centers dc must equal the 

production level of unit i in that year. Then, 

prodnquantity(t,i,dc) prodn(t,i).

dc

∑ = (2.3.2) 

where 

prodn(t, i) is the production level (tons/year) of manufacturing unit i in year t. Note that, in general, 

production can be from existing units, units added at a plant (i.e., expansion units), newer units 

replacing units at a plant (i.e., replacement units), and new kilns. Treatment of these is explained in a 

subsequent section. 

Production of a unit is constrained by its capacity. So, 

prodn ,( it ) ≤ capacity(t,i). (2.3.3) 

where 

capacity(t, i) is the capacity (tons product/year) of manufacturing unit i in year t. 

The sum of all imported quantities supplied from an import district id to various demand centers dc in 

year t must equal the imports available at that import district in that year. Then, 

∑ = ∑

dc oi istep

importedquantity(t,id,dc) Imports(t,oi id,istep)

,

, (2.3.4) 

where 

Importedquantity(t,id,dc) is the imported quantity supplied from import district id to demand center 

dc in year t, and

Imports(t,oi,id,istep) is the imported quantity available at the import district id in year t at the istepth

level of the imports curve for country oi. Construction of the country (or region) and import district 

specific imports-cost curves is explained below. 

Stepwise Approximation of Imports Curves in ISIS

The treatment of imports is similar to the treatment for elastic demand curves. Again, for clarity, the 

subscripts t, oi, and id corresponding to time, origin of imports, and import district are dropped in the 

following explanation. 

The relationship between imports arriving from oi at id and their price is expressed as 
2-9 

α

1

Imports0

Iprice0 















=

Imports Iprice(Imports) (2.3.5) 

where 

Imports is the imports of the commodity arriving from oi at id with corresponding imports price 

(value) Iprice, 

α is the elasticity of imports relative to imports price, and 

{Imports0, Iprice0} is a point on the applicable quantity-price curve.

First the range of the imports-price curve is defined using 

I Imports0 (1 - range) min = ⋅ (2.3.6) 

I Imports0 (1 range) max = ⋅ + (2.3.7) 

where 

range is a user-supplied parameter with value between 0 and 1. This parameter defines the interval 

Imin - Imax within which the new equilibrium imports quantity is expected to be found. Note range 

should be large enough to ensure that the solution does fall in the interval Imin - Imax. On the other 

hand a smaller value of range can increase precision of the stepwise approximation. In ISIS a value 

of 0.5 is used for range. 

Next, a series of steps is defined within the interval Imin - Imax with the width of each step given by 

Number of steps

I IIwidth max min

= (2.3.8) 

where 

Number of steps is another user-supplied parameter. Increasing the value of this parameter will 

increase precision of the stepwise approximation, but will increase the model size. In ISIS, a value of 

100 is used for Number of steps. 

Now the import quantity at the center of the slice istep inside the interval is 

2

Iwidth (2 istep 1)-

Imports Imin

istep ⋅ ⋅

= + (2.3.9) 
2-10 

Using the above information, the imports-price curve is determined 

α

1

istep

istep

Imports0

Imports Iprice Imports Iprice0 















( ) = (2.3.10) 

Equation (2.3.10) is used to generate the imports-cost curves for all time periods, origins of imports, and 

import districts. However, this equation needs specifications of one point on each import function in each 

time period (i.e., [Iprice0{t, oi, id}, Imports0{t, oi, id}]). To determine such points, a single run of the 

inelastic version of the ISIS model (with exogenous Imports0[t, oi, id] corresponding to capacities of 

import districts) is made and then the resulting Iprice0(t, oi, id) are used in Equation (2.3.10) to generate 

the imports-cost curves for all time periods, origins of imports, and import districts. 

2.4 Production Capacity and Supply Costs 

The ISIS model includes constraints for ensuring that endogenous production capacity changes occur in a 

realistic way. This section describes how the capacity changes take place in the ISIS framework and the 

treatment of related costs. Note that various cost elements (e.g., capital cost of a unit [$/ton clinker] in the 

cement sector) are escalated appropriately to reflect values in years of interest. 

Production Capacity Related Constraints

Added capacity in year t is given by 

When t = 1, 

(2.4.1a) 

For t > 1, 

i { } irepl(i) iExp(i) iNew(i) .

vyear(v) tyear(t) (vyear(v) techlifeplants);

capacity(t ;i),

∈ ∪ ∪

≤ < +

= − + ∑ ⋅

v

addcap(t,i) addcap(t timeblock, i) tcap(t,i,v )

 (2.4.1b) 

where 

v is the set of vintages of unit i,

vyear(v) and tyear(t) are parameters with values corresponding to years in the selected time horizon, 

tcap(t,i,v) is a binary variable that can bring v

th vintage of unit i online in year t, 
2-11 

timeblock is the block of years used in simulation, and 

techlifeplants is the technical life of a unit. 

Only one vintage of a unit is possible for the period starting when the vintage comes on line and ending 

with its technical life. 

(2.4.2) 

The annual costs associated with meeting the demand for the commodity include: (1) annualized capital 

costs associated with capacity growth (i.e., replacement units, expansion units, and new capacity) and

projected units, (2) annual fixed operation and maintenance (FOM) costs, (3) annual variable costs 

associated with use of labor, raw material, fuel, electricity, and operation and maintenance, (4) annual 

transportation costs, and (5) annual cost of imports. These costs are described below. 

Capital Recovery

If the existing units are paid for and do not have capital recovery costs, then 

plantcapcost(t,i) = ;0 i ∈{iE(i)}. (2.4.3) 

where 

plantcapcost(t,i) is the annual capital cost of a unit. 

For projected units, for which startup date is known, annual capital cost is given by 

{ }.

;

tstart(i) tyear(t) (tstart(i) ecolifeplants); i iP(i)

CRFplant capacity(t i), pcapcostt(t, i)

≤ ≤ + ∈

plantcapco st(t,i) = ⋅ ⋅

 (2.4.4) 

where 

pcapcostt(t,i) is the capital cost (e.g., $/ton of clinker for the cement sector) of i

th unit in year t, and 

CRFplant is the capital recovery factor calculated using an appropriate interest rate and time period, 

ecolifeplants, for capital recovery. 

Annual capital costs for all populations except existing and projected is 

plantcapco st(t,i) = Addcap t,( i) ⋅ capcostt(t i), ⋅ CRFplant

 (2.4.5) 
2-12 

where 

capcostt(t,i) is the capital cost (e.g., $/ton of clinker for the cement sector) for the i

th unit. 

Variable Costs

The annual variable cost at a unit is calculated using 

[ ]

,( ) .

,( )

+ +∑

= + + + ⋅ +

sw

fc it H2Oconsumptncost(t,i) SWdispcost(t,i,SW)

varcost(t,i) RMTt(t, i) VOMt(t, i) LBRt(t, i) ELCt(t, i) prodn it

 (2.4.6) 

where 

RMTt(t,i) is the raw material cost ($/ton product) at unit i in year t, 

VOMt(t,i) is the cost of operation and maintenance ($/ton product) at unit i in year t, 

LBRt(t,i) is the cost of labor ($/ton product) at unit i in year t, 

ELCt(t,i) is the cost of electricity use ($/ton product) at unit i in year t, 

fc(t, i) is the cost of fuel ($/ton product) at unit i in year t, and 

varcost(t,i) is the annual variable cost ($/ton product) at unit i in year t.

The fuel cost for a unit is calculated as follows: 

.

1

]]

Number of pollutants

[eintensity(i) fuelcostt( f)i,t,

m,poltikjee( i,t, k,j,ee)

f

⋅

= ⋅ ⋅

∑

∑

varprodn(t,i,f,m,j,k,ee)

fc(t,i)

(2.4.7) 

where 

eintensity(i) is the energy intensity (MMBtu/ton product) for unit i, 

fuelcostt(t,i, f) is the cost of fuel f ($/MMBtu) at unit i in year t, and 

varprodn(t,i,f,m,j,k,ee) is a production coefficient described in the next section. 

Annual water consumption related cost is given by 

H 2Oconsumptn cost(t,i) = H 2Oconsumptn(t,i)⋅H2Ocostt(t)/1000 (2.4.8) 

With annual water consumption given by 
2-13 

H 2Oconsumptn(t,i) = H2Ointensity(i)⋅ prodn ,( it ). (2.4.9) 

where 

H2Ocostt(t) is the cost of water ($/1000 gallon) in year t, and 

H2Ointensity(i) is the gallons of water needed to produce a ton of product at unit i. 

Annual solids discharge related cost is given by 

SWdispcost (t,i,sw) = SWgen(t,i ,sw)⋅SWdisposal costt(t, sw) (2.4.10) 

With annual solids discharges given by 

SWgen(t,i,sw) = SWgeneration(i,sw)⋅ prodn ,( it ). (2.4.11) 

where 

SWdisposalcostt(t,sw) is the cost of disposing of a solid discharge sw in year t, and 

SWgeneration(i,sw) is the discharge of a solid sw (tons) in the process of producing a ton of product 

at unit i. 

Note that SWdisposalcostt(t,sw) value can be positive (disposal cost) or negative (sale price). 

Total Annual Cost of Production

Using the above information, the total annual cost of production at unit i in year t is 

tannualprodncost(t,i) = plantcapcost(t,i) + varcost(t,i). (2.4.12) 

Annual Cost of Imported Commodity

The cost of imports of commodity at import district id in year t is calculated using the following equation 

[( ) ,( , , )].

,

Imports t oi id isteps

timportscost(t,id)

oi isteps

∑ + + ⋅

=

Iprice(t,oi,id,isteps) InsFreight(id) handling(id) (2.4.13) 

where 

Iprice(t,oi,id,isteps) is the price (value) of importing commodity from origin oi (foreign country) at 

the import district dc, at the istepth level of the relevant price-import curve. 

InsFreight(id) is the insurance and freight at the import district. 
2-14 

handling(id) is the handling cost at the import district. 

Total Annual Transportation Cost

The cost of transporting the commodity from unit i and import district id to demand center dc is 

calculated using 

,( , )].

,,( )]

Importedquantity idt dc

ttranscost(t,dc) prodnquantity it dc

id

i

∑

∑

⋅

= ⋅ +

[imprttransportcostt(t,id,dc)

[prodntransportcostt(t,i,dc)

(2.4.14) 

where 

prodntransportcostt(t,i,dc) is the cost of transporting one ton of commodity ($/ton) from unit i to 

demand center dc, and 

imprttransportcostt(t,id,dc) is the cost of transporting one ton of commodity ($/ton) from imprt 

district id to demand center dc.

2.5 Emissions 

As discussed in the previous chapter, emissions can be generated from fuel firing and also from use of

raw materials (e.g., CO2 emissions from calcination of limestone in cement kiln). As such, both of these 

emission generation pathways are included in ISIS. Further, the ISIS framework includes algorithms to 

account for tracking multiple pollutant streams associated with uncontrolled emissions, controlled 

emissions, pollution prevention from process modifications and energy efficiency measures, and any 

controls-related effects (e.g., generation of CO2 in a wet SO2 scrubber). These algorithms are described 

below. 

emintensityfuel(i,f, j) modeintensity(i, k, ee) [1 cp(i, j,k)/100].

polfuel(t, f,i, j, k, ee)

⋅ ⋅ −

=

(2.5.1) 

eHIdispl(i,ee).

eintensity(i) primaryHIchange(i,k) secondaryHIchange(i,k)

modeintensity(i,k,ee)

+

+ +

=

(2.5.2) 

where 

polfuel(t,i,f,j,k,ee) is emission (tons pollutant per ton product) of pollutant j resulting from 

processing (firing) fuel f and application of any control k and/or energy efficiency measure ee at unit i

in year t, taking into account whether a unit is available for operation in that year; 

emintensityfuel(i,f,j) is the emission intensity (tons pollutant per ton product) of pollutant j resulting 

from processing (firing) fuel f at unit i, taking in to account whether any controls (e.g., Best Available 

Control Technology [BACT]) are already installed on the unit; 
2-15 

modeintensity(i,k,ee) is the modified energy intensity (MMBtu per ton product) needed to produce 1 

ton of product at unit i. This energy intensity takes into account any heat input changes accompanying 

a control k and/or an energy efficiency measure ee;

eintensity(i) is the energy intensity (MMBtu per ton product) needed to produce 1 ton of product at 

unit i;

primaryHIchange(i,k) is the change in primary heat input (MMBtu per ton product) due to 

application of control k at unit i;

secondaryHIchange(i,k) is the change in heat input (MMBtu per ton product) due to any secondary 

fuel addition resulting from application of control k at unit i;

eHIdispl(i,ee) is the amount of heat input (MMBtu per ton product) displaced (reduced) due to 

application of energy efficiency measure ee at unit i; and 

cp(i,j,k) is the reduction efficiency for pollutant j using control k at unit i. 

Similarly, 

[1 cp(i, j, k)/100].

secondaryRMTchangepercent(i, k)/100

1 primaryRMTchangepercent(i, k)/100

polrmt(t, i, m, j, k, ee) emintensityrmt(i, m, j)

−

⋅











 + +

= ⋅

(2.5.3) 

where 

polrmt(t,i,m,j,k,ee) is emission (tons pollutant per ton product) of pollutant j resulting from 

processing raw material m and application of any control k and/or energy efficiency measure ee at 

unit i in year t, taking into account whether a unit is available for operation in that year; 

emintensityrmt(i,m,j) is the emission intensity (tons pollutant per ton product) of pollutant j

resulting from processing raw material m at unit i, taking into account whether any controls (e.g., 

BACT) are already installed on the unit; 

primaryRMTchangepercent(i,k) is the percent change in primary raw material input (tons raw 

material per ton product) due to application of control k at unit i; and 

secondaryRMTchangepercent (i,k) is the percent change in raw material input (tons raw material 

per ton product) due to any secondary raw material addition resulting from application of control k at 

unit i. 

Finally, 

pol(t, f,i, m, j, k, ee) = polfuel(t, f,i, j, k, ee) + polrmt(t, i, m, j, k). (2.5.4) 

where 
2-16 

pol(t,i,f,m,j,k,ee) is emission (tons pollutant per ton product) of pollutant j resulting from processing 

fuel f and raw material m, and application of any control k and/or energy efficiency measure ee, at 

unit i in year t, taking into account whether a unit is available for operation in that year. 

Emissions of pollutant j at unit i in year t are given by 

,,( ) = ∑ ⋅ ,,( , , , , .)

f,m,poltikjee( i,t, k,j,ee)

emissions it j pol(t, f,i, m, j,k,ee) varprodn it f m j k ee (2.5.5) 

where 

varprodn(t,i,f,m,j,k,ee) is the production variable associated with use of k

th control and/or eeth energy 

efficiency measure for j

th pollutant at unit i using fuel f and raw material m in year t;

emissions(t,i,j) are the emissions (tons of pollutant per ton of clinker) of pollutant j at unit i; and

poltikjee(t,i,k,j,ee) is the mapping described above. 

Now total emissions from all units are 

,( ) = ∑ ,,( .)

i

totalemissions t j emissions it j (2.5.6) 

where 

totalemissions(t,,j) are total emissions (tons of pollutant per ton of clinker) of pollutant j resulting 

from production activity in the entire sector.

Note that production associated with each pollutant is the same and therefore 

prodnpol it ,,( f ,m, j) = prodnpol it ,,( f ,m, jj); j ≠ jj; (2.5.7) 

with, 

,,( , , ) ,,( , , , , .)

,,( , , )

= ∑

poltikjee it k j ee

prodnpol it f m j varprodn it f m j k ee (2.5.8) 

where 

prodnpol(t,i,f,m,j,) production variable associated with pollutant j. 

2.6 Controls and Costs 

The ISIS framework includes constraints to ensure that endogenous applications of sector-based controls 

and energy efficiency options occur in a realistic manner. A description of these constraints and costs for 
2-17 

controls is presented in this section. The treatment of energy efficiency measures is described in a 

subsequent section. 

Controls Related Constraints

Only one vintage of a control on a unit is possible for the period starting when the vintage comes on line 

and ending with its technical life. 

vyear(v) tyear(t) [(vyear(v) + techlifecontrols(k)].

;1

≤ <

∑ ≤

t v

ts c(poltik t,i,k ,v)

,

_ ( )

(2.6.1) 

where 

v is the set of vintages of a control for unit i,

vyear(v) and tyear(t) are parameters with values corresponding to years in the selected time horizon, 

ts_c(t,i,k,v) is a binary variable that can bring v

th vintage of control k for unit i online in year t, and 

techlifecontrols(k) is the technical life of control k. 

Control capacity is given by the following equations. 

For t = 1, 

vyear(v) tyear(t) (vyear(v) techlifecontrols(k)).

, _ , capacity(t ;i),

≤ < +

= ∑ ⋅

v

ControlCap (t,i k) ts c(t,i,k v)

 (2.6.2a) 

For t > 1, 

vyear(v) tyear(t) (vyear(v) techlifeco ntrols(k)).

, , _ , capacity(t ;i),

≤ < +

= − +∑ ⋅

v

ControlCap (t,i k) ControlCap (t timeblock, i k) ts c(t,i,k v)

 (2.6.2b) 

In any year, no two incompatible controls can coexist on a unit, 

ControlCap it ,,( k) + ControlCap it ,,( kk) ≤ capacity ,( it ) (2.6.2c) 

where 

k and kk are incompatible controls. 

Finally, the production coefficient associated with a control is less than the capacity of the unit the control 

is installed on, 
2-18 

vyear(v) tyear(t).

_ capacity(t ;i),

Number of pollutants

( ,, , , )

≤

≤ ∑ ⋅

∑

v

f,m,poltikjee it k j ee ts c(t,i,k,v)

varprodn(t,i,f,m,j,k,ee)

(2.6.3) 

where 

capacity(t,i) is the capacity of unit i in year t. 

In general, the costs associated with controls comprise the following components: (1) capital and fixed 

operation and maintenance costs, (2) costs associated with any reagent and/or catalyst consumption, (3) 

costs associated with any reduction in fuel and/or raw material use, (4) cost associated with electricity 

consumption, (5) cost associated with byproduct(s), and (6) costs associated with water use. The 

calculations of these costs are described below. Note that various cost elements (e.g., capital cost of a 

control [$/ton clinker] for the cement sector) are escalated appropriately to use values in years of interest. 

Capital Recovery and Fixed Cost

Annual recovery of capital cost of control k is given by 

vyear(v) tyear(t) [vyear(v) ecolifecontrols(k)].

cntrlcapitalcostt(t,i, k) CRFcontrol(k);

_

≤ < +

⋅ ⋅

=

ControlCap(t,i,k)

capcost c(poltik(t, i,k))

(2.6.4) 

Similarly, annual FOM cost is given by 

vyear(v) tyear(t) [vyear(v) techlifecontrols(k)].

cntrlfixedcostt(t,i, k);

_ ( )

≤ < +

⋅

=

ControlCap(t,i,k)

FOMcost c poltik(t, i,k)

(2.6.5) 

where 

cntrlcapitalcostt(t,i,k) is the capital cost ($/ton of clinker) of application of control k on i

th unit,

cntrlfixedcostt(t,i,k) is the annual fixed operation and maintenance (FOM) cost ($/ton of clinker) of 

application of control k on i

th unit,

capcost_c(t,i,k) is the annualized capital cost ($) of k

th control application on ith unit, 

FOMcost_c(t,i,k) is the annual fixed operation and maintenance cost ($) of k

th control application on 

i

th unit, and 

CRFcontrol(k) is the capital recovery factor calculated using an appropriate interest rate and time 

period, ecolifecontrols(k), for capital recovery. 
2-19 

Variable Costs

Change in fuel cost associated with application of controls is given by 

.

]

[

( ,, , )

,( ) , ( ,, , )

Number of pollutants

secondaryH Ichange(i, k) fuelcostt(t, r,f)

primaryHIc hange(i, k) fuelcostt(t, r,f

∑

∑

+ ⋅ ⋅

⋅ ⋅

=

kf(k,f),m,poltikjee it k j,ee

fi i f ,m poltikjee it k j,ee

varprodn(t,i,f,m,j,k,ee)

varprodn(t,i,f,m,j,k,ee) )

fuelcostchange_c(poltirk(t,ri(i,r),k))

(2.6.6) 

where 

primaryHIchange(i,k) is the primary heat input change (MMBtu per ton clinker) with use of k

th

technology, 

secondaryHIchange(i,k) is the primary heat input change (MMBtu per ton clinker) with use of k

th

technology, 

fuelcostt(t,r,f) is the regional cost of fuel ($/MMBtu) in year t, 

ri(i,r) is a set with elements corresponding to mapping between kiln and their geographic locations, 

and 

kf(k,f) is a set with elements corresponding to mapping between fuels and technologies. 

poltirk(t, ri(i,r),k) is a mapping described above. 

Change in raw material cost associated with application of controls is given by 

.

/100 ]

[ /100

,( ) ,,( , )

,( ) ,,( , )

Number of pollutants

secondaryRMTchangepercent RMTt(t,i)

primaryRMTchangepercent RMTt(t,i)

∑

∑

+ ⋅ ⋅

⋅ ⋅

=

fi i f ,m,poltikjee it k j,ee

fi i f ,m,poltikjee it k j,ee

varprodn(t,i,f,m,j,k,ee)

varprodn(t,i,f,m,j,k,ee)

RMTcostchange_c(poltirk(t,ri(i,r),k))

(2.6.7) 

where 

primaryRMTchangepercent is the percent change in primary raw material with use of k

th

technology, 

secondaryRMTchange(i,k) is the percent change raw material input corresponding to any secondary 

raw material addition with use of k

th technology, and 

RMTt(t,i) is the unit-specific cost of raw material ($/ton clinker) in year t. 
2-20 

Annual reagent consumption costs are 

⋅reagentpricet(t, rgnt).

=

∑

rgnt

rgntconsumpt c it k rgnt

rgntconsumptcost c poltik it k

_ ,,( , )

_ ( ,,( ))

(2.6.8) 

The annual consumption of a reagent given by 

.

[ ]

_ ,,( ),

, ( , ) ( ,, , , )

( , ) ( ,, , , )

Number of pollutants

reagentcon sumptrmt(i ,m, k, j, rgnt)

Number of pollutants

reagentcon sumptfuel( i,f, k, j, rgnt)

[ varprodn(t ,i,f,m,j,k ,ee)]

varprodn(t ,i,f,m,j,k ,ee)

rgntconsum pt c(poltik t i k rgnt)

m j fi i f ,poltikjee it k j ee

fi i f ,j m,poltikje e it k j ee

∑ ∑

∑ ∑

⋅

+

⋅

=

(2.6.9) 

where 

reagentconsumptfuel(i,f,k,j,rgnt) is the reagent consumption due to control (tons reagent/tons 

clinker) associated with fuel-based emission intensity, 

reagentconsumptrmt(i,m,k,j,rgnt) is the reagent consumption due to control (tons reagent/tons 

clinker) associated with raw material-based emission intensity, 

reagentpricet(t,rgnt) is the price of reagent rgnt ($/ton of reagent) in year t, and 

rgntconsumpt_c(t,i,k,rgnt) is the annual reagent consumption due to control (tons reagent/year). 

Catalyst consumption cost is 

_ ,,( , ) ;)

_ ( ( , , )

∑ ⋅

=

cat

catconsumpt c it k cat

catconsumptcost c poltik t i k )

catalystprice(t,cat

(2.6.10) 

with 
2-21 

.

_ ,,( )

,( ) ,,( , , )

Number of pollutants

EGFW(i) catalystconsumpt(i, k, j, cat) ∑ ∑ ⋅ ⋅

=

j fi i f ,m,poltikjee it k j ee

varprodn(t,i,f,m,j,k,ee)

10000

catconsumpt c(poltik t i k ,cat)

(2.6.11) 

where 

catalystconsumpt(i,k,j,cat) is the catalyst consumption rate (ft3 catalyst/10000 ft3 flue gas), 

EGFW(i) is the exhaust gas flow rate (scf/ton clinker), 

catalystpricet(t,cat) is the price of the catalyst ($/ft3

) in year t, 

catconsumpt_c(t,i,k,cat) is the catalyst consumption rate (ft3

/year), and 

catconsumptcost_c(t,i,k) is the catalyst cost ($/year). 

Annual cash flow associated with byproduct generation disposal/sale is 

⋅byproductpricet(t, byprod);

=

∑

byprod

byproductgen c(t,i,k,byprod)

byproductcost c(poltik it k )

_

_ ,,( )

(2.6.12) 

with annual generation of a byproduct given by 

.

_ ,,( )

,( ) ( ,, , , )

,( ) ( ,, , , )

Number of pollutants

byproductp rodnrmt(i, m, k, j,byprod)

Number of pollutants

byproductp rodnfuel(i f,, k, j,byprod)

[ varprodn(t ,i,f,m,j,k ,ee)]

[ varprodn(t ,i,f,m,j,k ,ee)]

byproductg en c(poltik t i k ,byprod)

m,j fi i f ,poltikjee it k j ee

fi i f ,j m,poltikje e it k j ee

∑ ∑

∑ ∑

⋅

+

⋅

=

(2.6.13) 

where 

byproductprodnfuel(i,f,k,j,byprod) is the byproduct generation due to control (tons reagent/tons 

clinker) associated with fuel-based emission intensity, 

byproductprodnrmt(i,m,k,j,byprod) is the byproduct generation due to control (tons reagent/tons 

clinker) associated with raw material-based emission intensity, 
2-22 

byproductpricet(t, byprod) is the price of disposing or selling the byproduct byprod ($/ton of 

reagent) in year t, and 

byproductgen_c(t,i,k,byprod) is the annual byproduct generation due to control (tons reagent/year). 

Note that byproductpricet value can be positive (disposal cost) or negative (sale price). 

Annual electricity consumption (kWh/yr) due to control is 

.

[ ]

,,( )

,( ) , ,,( , , )

Number of pollutants

∑ kWhperton(i,f,k)⋅ ∑

=

fi i f m poltikjee it k j ee

varprodn(t,i,f,m,j,k,ee)

kwh_c(poltik t i k )

(2.6.14) 

The cost of electricity consumption is 

kwhcost_c( poltik it ,,( k ) ) = kwh_c(t,i, k)⋅ electricit ycostt(t, i). (2.6.15) 

where 

KWhperton(i,f,k) is the electrical requirement (kWh per ton of clinker) for technology k, and 

electricitycostt(t,r) is the electricity price ($/kWh) in year t at unit i.

Annual water consumption associated with control k is 

.

2 _

,( ) ,,( , , )

,( ) ,,( , , )

Number of pollutants

H2Ousermt(i,m,k)

Number of pollutants

H2Ousefuel(i,f,k)

[ varprodn(t,i,f,m,j,k,ee)]

[ varprodn(t,i,f,m,j,k,ee)]

H Oconsumpt c(poltik(t,i,k))

m,j fi i f ,poltikjee it k j ee

fi i f ,j m,poltikjee it k j ee

∑ ∑

∑ ∑

⋅

+

⋅

=

(2.6.16) 

The cost of water consumption is 

H2Ocost_c( poltik(t,i ,k)) = H2Oconsump t_c(t,i,k) ⋅ H2Ocostt(t ) /1000. (2.6.17) 

where 

H2Ousefuel(i,f,k) is the water use due to control (tons reagent/tons clinker) associated with fuelbased emission intensity, 
2-23 

H2Ousermt(i,m,k) is the water use due to control (tons reagent/tons clinker) associated with raw 

material-based emission intensity, 

H2Ocostt(t) is the price of water ($/1000 gallons) in year t, and 

H2Oconsumpt_c(t,i,k) is the annual water use due to control (gallons/year). 

Total Annual Cost

Using the above costs, the total annual cost of controls is 

kWhcost_c(t,i,k) H2Ocost_c(t,i,k)

rgntconsumptcost_c(t,i,k) catconsumptcost_c(t,i,k) byproductcost_c(t,i,k)

fuelcostchange_c(t,i,k) RMTcostchange_c(t,i,k)

capcost_c(t,i,k) FOMcost_c(t,i,k)

tcontrolcost(t,i,k)

+ +

+ + +

+ +

+

=

(2.6.18) 

2.7 Costs of Energy Efficiency Measures 

As described below, the treatment of energy efficiency measures in ISIS is similar to that for control. 

Energy Efficiency Measures Related Constraints

As for controls, constraints are needed to ensure realistic applications of energy efficiency measures. 

These constraints are described below. 

Only one vintage of an energy efficiency measure on a unit is possible for the period starting when the 

vintage comes on line and ending with its technical life. 

vyear(v) tyear(t) [(vyear(v) + eetechlife(ee)].

;1

≤ <

∑ ≤

t v

ts ee(poltiee t,i,ee ,v)

,

_ ( )

(2.7.1) 

where 

v is the set of vintages of measure ee,

poltiee(tpolc(t) is the mapping described before, 

vyear(v) and tyear(t) are parameters with values corresponding to years in the selected time horizon, 

ts_ee(t,i,ee,v) is a binary variable that can bring v

th vintage of measure ee online on unit i in year t, 

and 

eetechlife (ee) is the technical life of measure ee.

Energy efficiency measure capacity is given by the following equations. 
2-24 

For t = 1, 

vyear(v) tyear(t) (vyear(v) techlife(e e)).

, _ ( ), capacity(t ;i),

ee

EECap(t,i ee) ts ee(poltiee t,i,ee v)

v

≤ < +

= ∑ ⋅

 (2.7.2a) 

For t > 1, 

vyear(v) tyear(t) (vyear(v) techlife(e e)).

, , _ ( ), capacity(t ;i),

ee

EECap(t,i ee) EECap(t timeblock, i ee) ts ee(poltiee t,i,ee v)

v

≤ < +

= − + ∑ ⋅

 (2.7.2b) 

In any year, no two incompatible measures can coexist on a unit, 

∑[ _ ,,( ) + _ ,,( ) ]≤ ; 1

v

ts ee(poltiee it ee ,v) ts ee(poltiee it eee ,v) (2.7.2c) 

where 

ee and eee are incompatible measures. 

Finally, production coefficient associated with an energy efficiency measure is less than the capacity of 

the unit the measure is installed on, 

vyear(v) tyear(t)

capacity(t, i);

Number of pollutants

≤

≤ ∑ ⋅

∑

v

f,m,poltikjee(t i k j,ee ts ee(t,i,ee,v)

varprodn(t,i,f,m,j,k,ee)

_

,, , )

(2.7.3) 

where 

capacity(t,i) is the capacity of unit i in year t.

Capital Recovery and Fixed Cost

For an energy efficiency measure ee, annual recovery of capital and annual FOM cost are given by 

Equations 2.7.4 and 2.7.5, respectively. 

vyear(v) tyear(t) [vyear(v) ecolifeee(ee)].

ecapitalcostt(t,i, ee) CRFee(ee);

_

≤ < +

⋅ ⋅

=

EECap(t,i,ee)

capcost ee(poltiee(t, i,ee))

(2.7.4) 
2-25 

vyear(v) tyear(t) [vyear(v) eetechlife(ee)].

efixedcostt(t,i, ee);

_ ( )

≤ < +

⋅

=

EECap(t,i,ee)

FOMcost ee poltiee(t, i,ee)

(2.7.5) 

where 

ecapitalcostt(t,i,ee) is the capital cost ($/ton of clinker) of eeth energy efficiency measure application 

on i

th unit,

efixedcostt(t,i,ee) is the annual fixed operation and maintenance (FOM) cost ($/ton of clinker) of eeth

energy efficiency measure application on i

th unit,

ts_ee(t,i,ee,v) is a binary variable that can bring v

th vintage of measure ee online on unit i in year t,

capcost_ee(t,i,ee) is the annualized capital cost ($) of ee application on i

th unit, and 

FOMcost_ee(t,i,ee) is the annual fixed operation and maintenance cost ($) of ee application on i

th

unit, and 

CRFee(ee) is the capital recovery factor calculated using an appropriate interest rate and time period, 

ecolifeee(ee), for capital recovery. 

Variable Costs

Change in fuel cost associated with application of ee measures is given by 

Number of pollutants

eHIdispi(i, ee) ⋅ ∑ ⋅fuelcostt(t, r, f

=

( ,, , , )

_

f,m,poltikjee it k j ee

varprodn(t,i,f,m,j,k,ee) )

fuelcostchange ee(poltire e(t,ri(i,r),ee))

(2.7.6) 

where 

eHIdispi(i,ee) is the displacement of primary heat input (MMBtu per ton clinker) with use of eeth

energy efficiency measure, 

fuelcostt(t,r,f) is the regional cost of fuel ($/MMBtu) in year t, 

ri(i,r) is a set with elements corresponding to mapping between kiln and their geographic locations, 

and 

poltiree(t, ri(i,r),ee) is a mapping described above. 

Annual electricity consumption (kWh/yr) due to an energy efficiency measure is 
2-26 

Number of pollutants

kWhperton(i, ee)⋅ ∑

=

, , ( ,, , , )

_ ,,( )

f m poltikjee it k j ee

e varprodn(t,i,f,m,j,k,ee)

kwh ee(poltiee it ee )

(2.7.7) 

The cost of electricity consumption is 

kwhcost_ee (poltiee it ,,( ee) ) = kwh_ee(t,i ,ee) ⋅ electricit ycostt(t, i) (2.7.8) 

where 

eKWhperton(i,f,ee) is the electrical requirement (kWh per ton of clinker) for ee, and 

electricitycostt(t,i) is the electricity price ($/kWh) in year t at unit i.

Total Annual Cost

Using the above costs, the total annual cost of energy efficiency measures is 

fuelcostch ange ee(t,i,ee) kWhcost ee(t,i,ee)

capcost ee(t,i,ee) FOMcost ee(t,i,ee)

teemeasure scost(t,i, ee)

_ _

_ _

+

+ +

=

(2.7.9) 

2.8 Policy Options 

ISIS can help design and evaluate a number of emissions reduction policy options including cap-andtrade, emissions taxes, and emissions limits. Additionally, appropriate combinations of these options can 

also be evaluated. The policy options included in ISIS are described below. 

Cap-and-Trade

Under this option, an emissions cap is set on the amount of a pollutant that can be emitted. Sources, 

companies, or other groups are issued emission permits (allowances) which represent the right to emit a 

specific amount of the pollutant. Allowances may be banked for use in future. The total amount of 

allowances available in the current period and those banked in previous periods cannot exceed the cap in 

the current period. Sources or companies that need to increase their emissions must buy allowances from 

those who pollute less. The transfer of allowances is referred to as a trade. In effect, the buyer is paying a 

charge for polluting, while the seller is being rewarded for having reduced emissions by more than was

needed. Thus, in theory, those that can easily reduce emissions most cheaply will do so, achieving the

pollution reduction at the lowest possible cost to society. 

Generally, annual caps have been utilized in ongoing programs (e.g., Title IV SO2 reduction program). 

However, ISIS does permit evaluation of potential programs with caps over user-selected time periods 

(e.g., 5-yearly caps). This evaluation is accomplished using, 

totalemissions t,( j) ≤ ec(t,j)+ bnk t,( j) −bnk(t + period, j) (2.8.1) 
2-27 

where 

ec(t,j) is the emission cap for pollutant j in year t, and 

bnk(t+period, j) are the allowances of the pollutant j banked in year t for the year t + period. 

Cap-and-Trade with a Minimum Reduction Requirement

While designing a cap-and-trade program, there may be an interest in requiring a minimum level of 

emission reduction from each affected entity. Such a requirement may be able to help address any local

emissions-related concerns. In ISIS, this requirement can be imposed using 

emissions( i,t, j) ≤ ecpminer(t i,, j) (2.8.2) 

where 

ecpminer(t,i,j) is the unit-specific minimum emission reduction requirement for pollutant j in year t.

Emissions Limits

ISIS permits evaluation of the costs and emissions reductions associated with more traditional emission 

reduction programs utilizing unit-specific rate-based emission limits. Such requirements are imposed 

using 

emissions it ,,( j) ≤ el(t, j)⋅ prodn ,( it ) (2.8.3) 

where 

el(t,j) is the rate-based emission limit for pollutant j in year t and every affected unit complies with 

this limit. 

Allowance Price Inputs

In some cases, there may be an interest in endogenously determining the level of emission reduction 

corresponding to a certain allowance price. This information may be useful, for example, in a situation 

where an allowance price is set for reducing emissions from many industrial sectors. In such a case the 

levels of emission reductions corresponding to the same allowance price may be different for the sectors 

under consideration. The emissions response to a given allowance price is driven by the following term in 

the objective function (see section 2.1), 

( ) ,( )

,

dis t totalemissions t j

t j

∑ ⋅ alprice(t,j)⋅ (2.8.4) 

where 

alprice(t,j) is the exogenous allowance price of pollutant j in year t. 
2-28 

Note that the allowance price inputs scheme above is equivalent to emission-tax-based programs in which 

affected units or companies pay a tax for every unit of pollution they produce. Thus this scheme can also 

be used to evaluate such programs. 

2.9 Optimization and Post-Processing 

In ISIS, the input data are pre-processed to arrive at suitable input parameters for use in the model 

equations explained in this chapter. Once the data have been pre-processed, ISIS solves for the 

appropriate levels of production, imports and controls required to meet the constraints associated with 

commodity demand and emissions, while maximizing total surplus. Once the surplus maximization 

problem has been solved, the results are post-processed to obtain parameters and level values of the 

variables of interest. The key variables of interest are: production level of each production unit to meet 

regional demand, level of imports in each region, installation of various controls, emissions, and various 

costs. Output data are written in appropriate worksheets in an Excel workbook and further linked to 

various plots to enable visual presentation and analyses of the results. 
2-29 

References for Chapter 2 

Duloy, J. H.; Norton, R.D. (1975). Prices and incomes in linear programming models. American Journal 

of Agricultural Economics. 57(4): 591.600. 

Enke, S. (1951). Equilibrium among spatially separated markets: solution by electric analogue. 

Econometrica. 19: 40.47. 

FPL-PELPS, A price endogenous linear programming system for economic modeling, supplement to 

PELPS III, version 1.1, USDA Research paper FPL-RP-614, 2003. 

McCarl, B.A.; Spreen, T.H. (1980). Price endogenous mathematical programming as a tool for sector 

analysis. American Journal of Agricultural Economics. 62: 87.102. 

Samuelson, P.A. (1952). Spatial price equilibrium and linear programming. American Economic Review. 

42(3): 283.303. 

Samuelson, P.A.; and W. Nordhaus. (1977). Economics (17th edition), John Wiley. 
3-1 

Chapter 3 

Cement Data 

3.1 Data Requirements 

Data requirements for ISIS include sector-specific (in this case, cement-specific) data as well as policy 

and economic parameters. The cement-specific data requirements are discussed below. 

3.2 Cement-Specific Data 

The inputs to the ISIS model for the cement industry can be broadly categorized into three main 

components: 

• Industry production, fuel, and emissions 

• Control technologies, and emission abatement approaches 

• Policy and economic parameters 

3.2.1 Industry, Fuel, and Emissions 

3.2.1.1 Existing, Planned/Committed, and Potential Units 

The ISIS cement industry model contains information on 189 cement kilns that were in existence in 2009

and PCA’s projected capacity expansions from 2009 to 2012, as shown in Table 3-1 (PCA, 2009a). 

Additionally, the model also includes potential kiln representations that may come on line as a result of 

endogenous capacity addition (i.e., new production capacity [by state] and replacements [by kilns]). 

Table 3-1. Summary of Kilns Modeled in the ISIS Cement Industry Model 

Kiln Population Number of Kilns 

Existing (2009) 189 

PCA’s Projected New Kilns (2009 – 2012) 17 

Each kiln modeled in ISIS is characterized by its location, design (i.e., wet, dry, preheater, or precalciner), 

daily and annual clinker capacity3, vintage, and retirement information when available (PCA, 2006). In 

addition, each kiln is characterized by its average variable costs components (AVC). 

 

3

 Annual clinker capacity as reported by PCA refers to the kiln daily capacity multiplied by 365 less normal downtime days, 

where daily capacity is the normal clinker output a kiln can produce per day given a realistic work pattern and normal downtime 

days are number of days of downtime required for maintenance, repair or cleanup. 
3-2 

3.2.1.2 Average Variable Costs 

In previous economic analyses, five input variables in cement production have been identified to 

determine the kiln-level AVC functions: raw materials, repair and maintenance, labor, electricity, and fuel 

(Depro et al., 2007; Depro, 2010; Depro and Lentz, 2010). Raw materials serve as the kiln feed, and 

repair and maintenance are required for periodic upkeep of the kiln. Labor is used in the quarry, in the 

operation of the kiln, and for packing. Electricity is consumed mainly by the auxiliary equipment and fuel 

is largely consumed in the kilns. The AVC for raw materials, labor, repair and maintenance, and 

electricity was determined following the methodology in the EPA regulatory impact analysis of the 

cement kiln dust rulemaking (EPA, 1998). 

3.2.1.3 Cement Demand Centers 

The U.S. cement markets are organized in state-specific demand centers. Figure 3-1 shows the 

distribution of portland cement kilns operating in 2009. Each state containing at least one kiln is shaded. 

The ISIS-cement model simulates each cement plant’s ability to compete in each of the demand centers as 

a function of the plant’s production cost and transportation cost associated with supply to each demand 

center. 

3.2.1.4 Portland Cement Demand 

One of the key data inputs for the ISIS-cement model is the demand projection for each demand center. In 

general, this demand is a function of gross domestic product (GDP) growth, interest rates, special 

construction projects (e.g., highways), and public sector construction spending. Portland cement demand 

was 128 million metric tons in 2005. PCA expects cement demand will reach 192 million metric tons by 

2035, which reflects an increase of nearly 64 million metric tons with a compound annual growth rate of 

1.4 percent. Cement demand through year 2035 is reported in the PCA Long-Term Cement Consumption 

Outlook (PCA, 2009b). PCA projections of cement demand (in million metric tons) by state for 2005 

through 2035, in 5-year increments, are shown in Table 3-2. 
3-3 

Figure 3-1. Distribution of Cement Kilns in the United States as of 2009 
3-4 

Table 3-2. Portland Cement Demand in Millions of Metric Tons 

State Demand Center 2005 2010 2015 2020 2025 2030 2035 

Alabama 1.92 1.45 2.02 2.16 2.32 2.49 2.67 

Arizona 4.77 2.21 3.85 5.16 6.55 7.95 9.59 

Arkansas 1.30 0.93 1.29 1.41 1.54 1.67 1.81 

California 16.01 8.33 13.88 15.43 17.11 18.95 20.96

Colorado 2.55 1.74 2.76 3.08 3.47 3.90 4.33 

Connecticut 0.82 0.54 0.77 0.83 0.88 0.94 0.98 

Delaware 0.22 0.18 0.26 0.28 0.29 0.31 0.32 

District of Columbia 0.21 0.15 0.18 0.18 0.17 0.18 0.18 

Florida 12.28 4.77 8.99 11.05 13.47 16.33 19.67 

Georgia 4.75 2.52 3.95 4.96 5.67 6.17 6.65 

Hawaii 0.44 0.35 0.47 0.49 0.52 0.55 0.57 

Idaho 0.70 0.44 0.70 0.82 0.92 1.03 1.15 

Illinois 4.64 3.10 4.29 4.62 4.99 5.44 5.98 

Indiana 2.27 1.69 2.30 2.49 2.73 2.98 3.25 

Iowa 1.94 1.59 1.98 2.04 2.10 2.15 2.20 

Kansas 1.55 1.36 1.65 1.71 1.78 1.85 1.91 

Kentucky 1.60 1.06 1.48 1.59 1.71 1.84 1.97 

Louisiana 2.23 2.44 2.91 3.15 3.29 3.42 3.52 

Maine 0.35 0.20 0.29 0.31 0.32 0.34 0.35 

Maryland 1.66 1.12 1.63 1.78 1.94 2.12 2.31 

Massachusetts 1.26 0.81 1.11 1.21 1.31 1.43 1.56 

Michigan 3.06 1.54 2.05 2.19 2.44 2.68 2.75 

Minnesota 2.06 1.29 1.86 2.09 2.34 2.57 2.80 

Mississippi 1.14 1.07 1.50 1.58 1.66 1.76 1.85 

Missouri 2.87 2.03 2.71 3.18 3.58 3.90 4.29 

Montana 0.38 0.30 0.42 0.45 0.48 0.51 0.53 

Nebraska 1.37 1.14 1.46 1.55 1.66 1.76 1.87 

Nevada 2.63 1.20 2.33 3.26 4.27 5.14 6.16 

New Hampshire 0.37 0.23 0.33 0.36 0.39 0.42 0.45 

New Jersey 2.06 1.45 1.89 1.99 2.09 2.18 2.28 

New Mexico 0.91 0.65 0.94 1.00 1.07 1.12 1.15 

New York 3.29 2.84 3.60 3.73 3.87 3.98 4.08 
3-5 

State Demand Center 2005 2010 2015 2020 2025 2030 2035 

North Carolina 3.25 2.16 3.43 3.92 4.50 5.16 5.88 

North Dakota 0.36 0.39 0.47 0.47 0.46 0.44 0.42 

Ohio 4.06 2.59 3.61 3.75 3.90 4.02 4.12 

Oklahoma 1.67 1.57 1.68 1.76 1.87 1.98 2.11 

Oregon 1.24 0.83 1.22 1.40 1.61 1.86 2.15 

Pennsylvania 3.44 2.61 3.49 3.64 3.78 3.89 3.97 

Rhode Island 0.19 0.12 0.17 0.18 0.19 0.19 0.20 

South Carolina 1.94 1.07 1.81 1.99 2.19 2.38 2.56 

South Dakota 0.48 0.44 0.55 0.57 0.61 0.64 0.67 

Tennessee 2.52 1.54 2.38 2.61 2.89 3.19 3.53 

Texas 15.09 12.11 17.51 21.03 24.10 27.48 30.58 

Utah 1.53 1.22 1.82 2.08 2.40 2.78 3.23 

Vermont 0.16 0.11 0.16 0.17 0.18 0.20 0.21 

Virginia 2.87 1.87 2.84 3.13 3.45 3.80 4.17 

Washington 2.24 1.68 2.50 2.75 3.04 3.36 3.73 

West Virginia 0.54 0.47 0.59 0.60 0.60 0.59 0.58 

Wisconsin 2.37 1.59 2.18 2.32 2.45 2.59 2.74 

Wyoming 0.47 0.43 0.55 0.55 0.55 0.55 0.54 

Total 128.03 83.54 122.81 139.06 155.70 173.14 191.51 

3.2.1.5 Transportation-Interregional Trade 

In ISIS-cement, a transportation matrix is used to describe the costs for transporting cement from kiln and 

import district locations to demand centers. To develop these costs, information on distances between 

supply and demand points and costs of transportation modes (truck, rail, or water transport) was obtained. 

In particular, the Transportation Routing Analysis GIS (TRAGIS) model (TRAGIS, 2003) was used to 

develop the origin-destination distances as described below. Also, in the matrix, the applicable lowest 

cost transportation option is used to connect a supply point with a destination. While the cement demand 

centers are interlinked through a transportation matrix, the competition is generally maintained on a 

regional level because the cost of transporting cement is relatively high. 

As mentioned above, the TRAGIS model was used to calculate transportation distances associated with 

delivery of portland cement from domestic U.S. manufacturing sites and import terminals to each of the

demand centers. Operation of the TRAGIS program requires the identification of a shipment’s origin and

destinations, as well as the selection of mode of transportation (highway, rail, or water) for each origindestination pair. The TRAGIS program does not use actual addresses or coordinates as origins or 

destinations. Instead, the program uses a fixed set of pre-determined locations within the model 

corresponding to locations in a number of U.S. cities and towns. The locations of domestic portland 

cement manufacturers used correspond to the lists of manufacturing plants published in the Cement 
3-6 

Americas (Penton Media Inc.), 2009; North American Cement Directory (2008) (Cement Americas 

2008); and the Portland Cement Association, U.S. and Canadian Portland Cement Industry Plant 

Information Summary December 31, 2006 (2007). 

The cost of transportation for interregional trade takes into account the impact of terminals and the 

frequent use of modes other than truck. According to the USGS (2007a), nearly half of cement shipments 

reach customers via terminals rather than direct. Shipments to terminals are more than 80 percent by rail 

or water, rather than by truck. Table 3-3 provides information on cost per ton mile for bulk shipping via 

truck, rail, and barge. In ISIS-cement, transportation costs for each mode of transportation (truck, rail, 

and barge) are calculated from each kiln and import district to each demand center. For a given origindestination pair, the dominant mode (lowest cost of shipment) is used to determine the transportation cost 

for that route. The TRAGIS model was used to arrive at the origin-destination distances and feasible 

routes for rail and barge; Google Maps was used for truck transportation. The shipment costs shown in 

Table 3-3 were used to determine the transportation costs for the routes in ISIS. 

Table 3-3. Bulk Shipment Costs (Cents per Metric Ton per Mile) 

Journal of Transport 

Geography Report, 2005 

N&S American 

Mode Cement, 2004 

BTS1 WDOT2 NACT3

American 

University, 1994 

Averages Used 

in Model 

Truck 33.06 7.64 11.02 7.18 14.73 

Rail 2.78 3.32 5.51 N/A 3.87 

Barge 0.89 1.05 2.20 N/A 1.38 

1. (Bureau of Transportation Statistics) General Bulk Transportation "...can only be considered the upper 

boundary of transport costs for each mode assuming each industry is operating at-cost or at a profit".

2. (Washington State Department of Transportation) Cost study to examine options for the transport of wheat 

(Jessup and Casavant, 1998). 

3. (North American Cement Transportation) Report covers cement-specific transportation costs, assumes

short tons--converted to metric. 

3.2.1.6 Imports 

U.S. cement markets receive imported quantities of cement and clinker from a number of countries, and 

these imports arrive at more than 30 import districts (USGS, 2007b). In ISIS-cement, international 

supplies from exporting countries to U.S. import districts are modeled using a supply elasticity and then 

these imports are transported to the demand centers. 

The five largest international suppliers of cement and clinker to the U.S. are China, Thailand, Venezuela, 

South Korea, and Greece. An econometric study was conducted to provide an estimate of international 

supply elasticity for supplies from these countries and the rest of the world. The results of this study 

(Burtraw, 2010) reflected that the best estimate of the international supply elasticity of cement and clinker 

from China, Thailand, Venezuela, South Korea, Greece, and the rest of the world into the U.S. is 3.94.

This value indicates that if the price of cement were to increase by 1 percent within any import district in 

the U.S., then, ceteris paribus, the quantity of cement imported from each of these five supply countries 

into that district would increase by 3.94 percent. 

Table 3-4 shows the import levels by USGS Customs District, then state, for 2005, estimated by using 

USGS data (USGS, 2007b). 
3-7 

Table 3-4. Portland Cement and Clinker Imports in Million Metric Tons, by Customs District in 

2005 

USGS Customs District Quantity, million 

metric tons 

Alabama, Mobile 0.51 

Alaska, Anchorage 0.14 

Arizona, Nogales; Mexico 1.07 

California, Los Angeles 3.05 

California, San Diego 0.72 

California, San Francisco 2.36 

Florida, Miami 2.27 

Florida, Tampa 3.48 

Georgia, Savannah 0.08 

Hawaii, Honolulu 0.43 

Illinois, Chicago 0.01 

Louisiana, New Orleansa

 1.75 

Louisiana, New Orleansa

 2.43 

Maine, Portland 0.16 

Maryland, Baltimore 0.13 

Massachusetts, Boston 0.13 

Michigan, Detroit 1.32 

Minnesota, Minneapolis; Canada 0.04 

Minnesota, Duluth; Canada 0.16 

Missouri, St. Louis 0.01 

Montana, Great Falls 0.06 

New York, Buffalo 0.82 

New York, New York 1.26 

New York, Ogdensburg 0.33 

North Carolina, Wilmington 0.39 

North Dakota, Pembina; Canada 0.18 

Ohio, Cleveland 0.79 

Oregon, Columbia-Snake 0.87 

Pennsylvania, Philadelphia 0.49 

Rhode Island, Providence 0.74 

South Carolina, Charleston 1.1 
3-8 

USGS Customs District Quantity, million 

metric tons 

Texas, El Paso; Mexico 0.72 

Texas, Houston-Galveston 2.61 

Texas, Laredo; Mexico 0.14 

U.S. Virgin Islands 0.06 

Vermont, St. Albans; Canada 0.13 

Virginia, Norfolk 0.7 

Washington, Seattle 1.49 

Wisconsin, Milwaukee; Canada 0.19 

Total 33.33 

a

 Imports for New Orleans are distributed between Birmingham and Dallas markets using baseline domestic

production levels. 

Source: USGS, 2007b. Data do not include Puerto Rico 

3.2.1.7 Capacity Changes 

Cement plants have a relatively long lifespan, typically 50 years or more (FLSmidth, 2007). Various 

factors, including (but not limited to) raw material availability in the quarry, technology changes, 

productivity, efficiency, longevity, reliability, maintenance, and long-term costs can affect the lifespan of 

a cement kiln/plant. In ISIS-cement, retirements from 2002 to 2009 and projected retirements after 2009 

of existing kilns were based on information from PCA on capacity expansion estimates. These estimates 

were supplemented with information from individual cement companies on their plans for shut-downs, 

new construction, and kiln consolidation (PCA, 2004). Further, as mentioned earlier, ISIS-cement 

includes algorithms for endogenous capacity growth and retirement of kilns. To determine capital 

recovery factor for capital costs associated with kiln capacity changes, an economic life of 25 years and 

an interest rate of 15 percent are used. Capital costs in 2005 $ per short ton of clinker for new, 

replacement of wet, and replacement of dry capacity are 208, 296, and 238, respectively (PCA, 2009b). 

3.2.1.8 Fuel Intensity 

The Annual Energy Outlook energy use profile for 2005 (EIA, 2008) is shown in Figure 3-2. In 2005, the

cement sector consumed 451.2 trillion Btu (476.0 trillion kJ) of energy (EIA, 2008). As shown in Figure 

3-2, the primary fuel being burned in kilns is coal. Coal is projected to remain the dominant fuel used by 

the U.S. cement industry. However, there has been an increasing trend towards using other fuels, 

particularly alternative fuels, such as coke,4

 waste tires, and other wastes, especially oily wastes. 

 

4

 PCA does not specify if “coke” is metallurgical coke or petroleum coke. Authors believe it is the latter. 
3-9 

3.72%

0.76%

64.11%

19.30%

3.20%

0.50%

8.40%

Natural Gas

Fuel Oil

Coal

Petroleum Coke

Tires

Other Solid Wastes

Liquid Wastes

Figure 3-2. Commercial Fuel Use Profile by U.S. Cement Industry in 2005. 

Source: EIA, 2008 

In the ISIS-cement model, to determine the fuel intensity of each kiln, correlations of kiln type to heat 

input and/or gas flow were developed (see Appendix A). Once determined, the kiln’s specific fuel 

intensity is used to calculate fuel cost for each kiln. PCA’s data on heat input to various kilns by type 

were used to develop kilns’ fuel intensities. The data, expressed in heat input per unit of clinker (specific 

fuel consumption [SFC]) and exhaust gas flow rate (wet) (EGFW) per unit of clinker, are summarized in 

Table 3-5. 

Table 3-5. Specific Fuel Consumption and Total Exhaust Gas Flow Rate (wet) for Various Kiln 

Types 

SFCa

 EGFWb

Kiln Type 

MMBtu/tona

 Nm3/kg SCF/tonc

Wet 6.0 3.4 108,990 

Dry 4.5 1.8 57,701 

Preheater 3.8 1.5 48,084 

Preheater/Precalciner 3.3 1.4 44,878 

a. SFC=specific fuel consumption; (Source: EPA, 2007 (Table 3-3) 

b. EGFW=exhaust gas flow rate (wet): (Source: PCA, 2004; original data in metric units) 

c. short ton 

For each individual kiln, the ISIS model determines the optimal fuel type(s) based on the regional cost of 

the chosen fuel and the kiln’s specific fuel intensity. 

3.2.1.9 Emission Intensities 

The design of the ISIS Model can accommodate any number of pollutants of interest. In ISIS-cement, 

each kiln is characterized by its NOX, SO2, PM, hydrochloric acid (HCl), mercury, total hydrocarbon 
3-10 

(THC), and CO2 emission intensities. These emission intensities were developed using available data 

(Andover Technology Partners, 2009a). 

NOX emissions from cement kilns result primarily from the following combustion process: oxidation of 

fuel nitrogen (fuel NOX) and the oxidation of nitrogen in the combustion air (thermal NOX). Oxidation of 

nitrogen in the feed materials (feed NOX) can also influence total NOX emissions. Table 3-6 shows NOX

emission intensities for cement kilns in lb/ton of clinker and in lb/MMBtu (EPA, 2007). 

Table 3-6. Estimated Uncontrolled NOX Emission Intensities for Cement Kilns 

Uncontrolled NOX Emissions 

Kiln Type 

Heat Input, 

MMBtu/ton of clinker lb/ton of clinker * lb/MMBtu 

Wet 6.0 9.7 1.62 

Long Dry 4.5 8.6 1.91 

Preheater 3.8 5.9 1.55 

Preheater/Precalciner 3.3 3.8 1.15 

* Average 

Source: EPA, 2007 (Table 3-3 and Table 6-1) 

SO2 emissions from cement kilns are the product of sulfur in the fuel as well as sulfur in the feed 

materials. Sulfur in the fuel will oxidize to SO2 during pyroprocessing, and a significant amount is likely 

to be captured in the form of sulfates as the gas passes through the calcination zone. Compared to long 

dry and wet kilns, preheater and preheater/precalciner kilns tend to be more effective at capturing fuelgenerated SO2. Accordingly, oxidation of sulfur in the feed materials is likely to be a major component of 

total SO2 emissions. Table 3-7 shows average SO2 emissions for each kiln type for each state (Andover 

Technology Partners, 2009a). State-specific emission intensities of SO2 were determined from emissions 

reported for the kilns in that state. For any state where the emission intensity was not available for a kilntype, the national average emission intensity was assigned to kilns in that state. 

CO2 emissions from cement kilns result from limestone calcination and fuel combustion. Appendix A 

explains how CO2 emission intensities from calcination and combustion are calculated in the ISIS model. 

Calcination releases 0.52 tons of CO2 per ton of clinker produced, while fuel-based CO2 emission factors 

range from 199.52 lb CO2/MMBtu for coal to 105.02 lb CO2/MMBtu for natural gas (Andover 

Technology Partners, 2009b, 2010a, and 2010b). Table 3-8 shows approximate CO2 and H2O produced 

from combustion for the most important fuels for cement kilns. 
3-11 

Table 3-7. Average SO2 Emissions for Each Kiln Type in Each State 

SO2 (lb/ton clinker) 

State 

Precalciner Preheater Dry Wet 

AL 0.09 0.61 9.02 13.99 

AZ 1.30 0.07 8.51 7.81 

AR 2.94 2.32 3.45 7.59 

CA 0.33 1.12 0.81 0.00 

CO 0.32 0.10 1.07 4.24 

CT 1.15 2.32 9.02 7.81 

DE 1.15 2.32 9.02 7.81 

FL 0.45 0.02 9.02 7.81 

GA 0.95 2.73 11.42 12.25 

ID 0.13 2.32 9.02 0.70 

IL 5.58 5.77 5.88 9.50 

IN 1.15 2.32 9.02 7.81 

IA 1.15 2.32 9.02 7.81 

KS 1.25 5.88 24.85 8.61 

KY 0.19 5.04 12.53 7.81 

LA 1.15 2.32 9.03 7.81 

ME 0.30 0.30 N/A 11.98 

MD 3.14 3.95 7.31 7.49 

MA 1.15 2.32 9.03 7.81 

MI 5.23 2.32 5.31 18.00 

MN 1.15 2.32 9.03 7.81 

MS 0.09 0.61 9.02 13.99 

MO 2.18 2.32 1.51 7.02 

MT 0.13 2.32 9.02 0.70 

NE 1.25 5.88 24.85 8.61 

NV 0.49 0.05 1.51 7.81 

NH 1.15 2.32 9.02 7.81 

NJ 1.15 2.32 9.02 7.81 

NM 1.30 0.07 8.51 7.81 

NY 0.30 0.30 9.02 11.98 

NC 1.15 2.32 9.02 7.81 
3-12 

SO2 (lb/ton clinker) 

State 

Precalciner Preheater Dry Wet 

ND 1.15 2.32 9.02 7.81 

OH 0.19 5.04 12.53 7.81 

OK 1.25 5.88 24.85 8.61 

OR 0.13 2.32 9.02 0.70 

PA 1.15 2.32 9.02 7.81 

RI 1.15 2.32 9.02 7.81 

SC 0.95 2.73 11.42 12.25 

SD 0.32 0.10 1.07 4.24 

TN 0.95 2.73 11.42 12.25 

TX 1.15 2.32 9.02 7.81 

UT 0.13 2.32 9.02 0.70 

VT 1.15 2.32 9.02 7.81 

VA 0.95 2.73 11.42 12.25 

WA 0.53 2.32 9.02 3.80 

WV 3.14 3.95 7.31 7.49 

WI 1.15 2.32 9.02 7.81 

WY 0.32 0.10 1.07 4.24 

National Average 1.15 2.32 9.02 7.81 

N/A Not available. 

Table 3-8. Approximate CO2 and H2O Produced from Combustion of Fuels 

Variable Name Flue Gas Tires Petroleum 

Cokes 

Heavy 

Fuel 

Oil 

Rosemont 

PRBa

Logan, 

WV 

BITb

Natural 

Gas 

LBMCO2MMBTU lbmoles 

CO2/MMBtu 4.26 4.83 3.85 4.25 4.53 2.39 

LBCO2MMBTU lb CO2/MMBtu 187.44 212.56 169.32 186.83 199.52 105.02 

LBMH2OMMBTU lbmoles 

H2O/MMBtu 2.76 2.76 2.23 3.23 4.14 2.77 

LBH2OMMBTU lbs H2O/MMBtu 49.71 49.73 40.26 58.21 74.64 49.92 

a. PRB = Powder River Basin coal 

b. WV BIT = West Virginia bituminous coal 
3-13 

3.2.2 Control Technologies and Emission Abatement Approaches 

ISIS-cement contains information on abatement approaches for NOX, SO2, PM, HCl, mercury, THC, and 

CO2 emissions described above. The three categories of abatement approaches included are: process 

modifications and upgrades, raw material and/or fuel substitution, and mitigation technologies. For each 

emission abatement approach, where possible, information on the following parameters was developed 

(Andover Technology Partners, 2009b and 2009c) and included in the model: capital cost, fixed operating 

cost, variable operating cost, emission reduction performance for all of the pollutants, impacts on fuel 

and/or raw material use, impact on electricity consumption, byproduct generation and cost, and impact on 

water use. 

To estimate capital recovery factors for capital costs associated with control technologies, economic life 

values of 15 and an interest rate of 7 percent are generally used, but different values can be selected by 

the user. Payback periods and technical life for the energy efficiency measures shown in Tables 3-14 

through 3-17 are given in Worrell and Galitsky (2004). Economic life for each of these measures can be

taken to be the average of the technical life and the payback period. Again, an interest rate of 7 percent 

can be used for capital recovery in the absence of more specific information. 

Tables 3-9 through 3-12 show the NOX, SO2, CO2, HCl, Hg, THC emissions control technologies being

used in the ISIS-cement model. Tables also reflect the impacts of these technologies on pollution 

reduction, electricity use, and water use. Multimedia impacts of changes to the capacity of cement kilns 

are listed in Table 3-13. Tables 3-14 through 3-17 show the electricity consumption and heat input 

changes accomplished as a result of implementation of energy efficiency measures for Raw Materials 

Preparation, Clinker Making, Finish Grinding, and Plant-Wide Measures, respectively. 

More details on cost, efficiency, and co-impacts for each of the above control technologies or process

modifications are given in Appendix A. 

3.2.3 Policy and Economic Parameters 

3.2.3.1 Policy Parameters 

The ISIS model framework allows the user to select a variety of potential policy options for evaluation. 

The user can select from cap-and-trade policy (with or without de minimis requirements), emissions 

charge policy, or rate-based policies. In a cap-and-trade policy scenario, separate caps on pollutants of 

interest can be specified. The user has the option to run a cap-and-trade policy scenario with or without 

banking of emissions. Further, a cap-and-trade policy scenario can include de minimis requirements, 

where the user defines a minimum level of emission reduction required for each emission unit. As 

mentioned before, it is also possible for the user to input an emission charge for pollutants of interest. 

Furthermore, traditional policy scenarios (rate-based policies) with unit specific emission reduction 

requirements specified by the user can be modeled in ISIS. 

The user can specify the policy horizon (time period) to be used for the model runs. Since climate-related 

simulation horizons can be long (e.g., 40 years), the user may choose to run ISIS with blocks of years

(e.g., 5-year blocks). The simulation horizon and blocks of years can be chosen by the user subject to

availability of data. 

3.2.3.2 Additional Economic Parameters 

In the ISIS framework, the following additional economic parameters are used: discount rate, escalation 

rates, and demand elasticity. For the ISIS-cement model, the default discount rate has been chosen as 
3-14 

7-percent, as recommended by the U.S. Office of Management and Budget (OMB) for project evaluation 

(OMB, 1992). Escalation rates used can be found in the “ISIS_Inputs.xls” workbook. Escalation rates for 

labor are based on historical data from Bureau of Labor and Statistics (BLS). Raw materials escalation

factors are calculated from historical price data of crushed sandstone and gypsum from USGS. The 

escalation factors for variable operating and maintenance costs are based on Chemical Engineering Index. 

Escalation factors for various fuels and electricity are estimated based on data from the EIA’s Annual

Energy Outlook 2008 (RTI, 2010). Demand for cement is relatively inelastic and an elasticity value of -

0.88 is used in the model (EPA, 1998). 
3-15 

Table 3-9. NOX Control Technologies for Cement Kilns 

Impact on Emissions, 

±% change 

Electricity Consump

kWh/ton of ceme

Control Type 

NOX SO2 PM Hg Other Grinding Kiln Ope

Low NOX Burners – 

Indirect Firing -20% to -30%1 No impact2 03 -1.2

Mid Kiln 

Firing-Tires -20% to -40%1 May vary5 03 06 

Low NOX Burner + Mid 

Kiln Firing- Tires -20% to -40%1 May vary5 03 -1.24

Low NOX Burners + Tire 

Derived Fuel -20% to -40%7 May vary5 03 -1.24

Low NOX Burner + 

Selective Non Catalytic 

Reduction 

-50%2 No data5 No data8 No data8 No data8 0 -1.24

Low NOX Burner + 

Selective Catalytic 

Reduction 

-90%2 Oxidation10 No data8 Oxidation10 No data8 0 -1.24

Low NOX Burners + 

CemStara -30%11

-1.3 (wet process); 

-1.9 (dry 

process) 12

-1.24,12 fro

and 

-1.5 (wet p

or 

-2.2 (dry p

from Cem

CemStar/Fly Ash Injection 

-20%14

 -1.3 (wet 

process); 

-1.9 (dry 

process) 13

-1.5 (wet p

or 

-2.2 (dry p

from Cem

Notes to Table 3-9 on following pages 
3-16 

1. See EPA (2004), Table 5-1. 

2. See Andover Technology Partners 2009b memo. 

3. These technologies do not use and do not affect the consumption of water in raw mix preparation or significantly affec

consumption in cement manufacturing processes. 

4. Conversion from direct firing systems typical of wet and dry process kilns and older preheater kilns to indirect firing sy

implement low NOX burner (LNB) technology, could result in reductions in primary air fan and kiln-induced draft fan 

concomitant slight increases in coal conveying power requirements. A reduction in fan/blower power on the order of 1

anticipated for a moderately sized (300,000 – 500,000 clinker tons per year) kiln converted from direct firing to indirecsavings on adding an LNB would be anticipated if the kiln system is already indirect-fired. 

5. SO2 emissions from cement kilns are strongly related to fuel and raw materials sulfur content and to method of kiln ope

of TDF (typically, 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuels commonly used 

as coal or coke. Therefore it is not practical to relate SO2 emissions to the use of these NOX control methods. With res

wet scrubber using ground limestone, it is assumed that, for wet process kiln systems, uncontrolled SO2 emissions of 8

(8.9 lb/ton of cement) (EPA, 1995: Table 11.6-8) are treated by use of a stoichiometric amount (with respect to uncontr

of 90-percent purity in a 15-percent limestone slurry. Limestone and water consumption are 15.5 lb and 87.7 lb, respec

cement produced. For a precalciner kiln, it is assumed that uncontrolled SO2 emissions of 1.1 lb per ton of clinker (1.2 

1995: Table 11.6-8) are treated similarly (stoichiometric amount limestone of 90% purity in a 15% limestone slurry). Lconsumption are 2.1 lb and 11.8 lb per ton of cement produced. 

6. Kiln system and raw materials grinding electric power consumption would not be significantly affected by introduction 

fuel. 

7. Combined effect will vary. Low NOX 20 to 40 percent, MKF 20 to 40 percent. 

8. While there may be theoretical or limited experimental bases to assume increases or decreases in emissions of various p

with NOX emission controls, statistics on such effects are not available. See, generally, EPA (2007a), Chapter 11. 

9. Assumes SNCR or SCR both with stoichiometric addition of NH3 in 20-percent solution, typical precalciner NOX emis

clinker before treatment (EPA 1995, Table 11.6-8), and 0.92 tons of clinker being used in each ton of cement. It should SNCR is not applicable to wet process, long dry process, and many preheater kilns because the kiln gas exit temperatur

those units for the necessary reactions to take place. See EPA (2007a). SNCR is assumed to achieve 63-percent reducti

from applicable kiln systems. See EPA (2007a), Chapter 8. There may be an attendant small increase in kiln system eleconnection with injection of water into the kiln system due to an increase in gas volume handled by the kiln’s fan syste

considered in the calculation. 

10. Typically, up to about 1 percent of SO2 can be oxidized to SO3. Elemental form of Hg oxidized across SCR provided th

concentration of halogens in flue gas. 

11. On a lb/ton of clinker basis. 

12. Use of the CemStar process may have multiple effects on electric power consumption, including reduced raw mix prep

event that unground CemStar material is fed to the rotary kiln, and reduced fan power requirements resulting from redu
3-17 

connection with both combustion gases and raw mix calcination. Assuming a 5-percent replacement of raw mix with cmaterial, a concomitant 5-percent reduction in raw mix preparation energy is also assumed. An approximate 5-percent electrical energy consumption is assumed based on a roughly 5-percent reduction in kiln exit gases from both calcinatio

13. CemStar and fly ash injection are expected to have similar effects on raw mix preparation and kiln process electrical en

14. See NESCAUM (2000) for effect of CemStar on NOX reductions from cement kilns. Investigation of the combined eff

technologies on pollutant emissions was not carried out for this summary. 

General Notes A. Cement kiln processes do not use steam. B. Cement kiln dust (CKD) disposal rates as functions of NOX or SO2 emissions control technology has not been reported

rates range from 0.042 to 0.115 tons CKD/ton clinker (0.046 to 0.125 tons CKD/ton cement assuming 0.92 tons clinker 

C. CKD disposal costs vary widely by region, CKD characteristics, CKD volumes, and location of disposal site (on-site o

disposal costs are expected to be of comparable cost to transportation costs in connection with off-site disposal. For exa

northern California landfills range from $26/short ton to $69/short ton of CKD plus transportation, which may range fr

to $1,200 per truck load. Pers. Comm., E. Leamer (ARCADIS) Feb. 26, 2009. CKD disposal prices would be expected unaffected by the type of cement kiln pollution controls employed. 

References for Table 3-9 (references called out in the above notes can be found at the end of the chapter) 

Air Pollution Controls and Efficiency Improvement Measures for Cement Kilns, Prepared by ARCADIS Under Contract N

31, 2008. 

Srivastava et al., ES&T, March 2006, pp.1385-1393. Theoretical Approach for Enhanced Mass Transfer Effects in-Duct Flue Gas Desulfurization Processes. Final Report. 1992
3-18 

Table 3-10. SO2 Control Technologies for Cement Kilns 

Impact on Emissions, 

±% change 

Electricity Consumption, 

kWh/ton of cement Control Type 

NOX SO2 PM Hg Other Grinding Kiln Operation 

Wet Scrubber -90% to -95%1 -80% -50% for THC -99.9% for HCl +0.2 (wet process kilns) 2 +52 

Dry Lime Injection -50%4 -75% for HCl 5 5 

Notes to Table 3-10 1. See Andover Technology Partners 2009b memo. 

2. Electric power consumption in connection with wet scrubbing is assumed to be primarily a result of: 1) grinding of lim

74 micrometers in particle size and pumping limestone slurry; and 2) increased kiln exhaust gas fan power requirement

increase is caused by increased pressure drop demand on kiln ID fans due to conveying kiln exit gases across the scrubincreased gas volume demand on kiln ID fans due to the addition of water in the limestone slurry. Slurry preparation anto require 20 kWh per ton of limestone. The spray tower pressure drop is assumed to add 500 hp to ID fan requirement500,000 ton per year cement kiln process. 

3. SO2 emissions from cement kilns are strongly related to fuel and raw materials sulfur content and to method of kiln ope

of TDF (typically 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuels commonly used coal or coke. Therefore it is not practical to relate SO2 emissions to the use of these NOX control methods. With respe

scrubber using ground limestone, it is assumed that, for wet process kiln systems, uncontrolled SO2 emissions of 8.2 lb 

lb/ton of cement) (EPA, 1995: Table 11.6-8) are treated by use of a stoichiometric amount (with respect to uncontrolled 

90-percent purity in a 15-percent limestone slurry. Limestone and water consumption are 15.5 lb and 87.7 lb, respectiv

produced. For a precalciner kiln, it is assumed that uncontrolled SO2 emissions of 1.1 lb per ton of clinker (1.2 lb/ton o

Table 11.6-8) are treated similarly (stoichiometric amount limestone of 90% purity in a 15% limestone slurry). Limestoconsumption are 2.1 lb and 11.8 lb per ton of cement produced. 

4. 20 percent at Ca:S stoichiometry of 2:1 to 5:1. See Dry Sorbent Injection report (ARCADIS, 1992). 

5. Dry lime injection technology is not well developed for the cement industry and no statistics are available. 

General Notes: See Table 3-9 notes 
3-19 

References for Table 3-10 (references called out in the above notes can be found at the end of the chapter) 

Andover Technology Partners, Cost and Performance of Controls, March 10, 2009. Srivastava et al., ES&T, March 2006, pp.1385-1393. Theoretical Approach for Enhanced Mass Transfer Effects in-Duct Flue Gas Desulfurization Processes. Final Report. ARC

EPA (1995). Compilation of Air Pollution Emission Factors - Volume 1: Stationary Point and Area Sources. Fifth Edition, 

42. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina. 

Table 3-11. CO2 Control Technologies for Cement Kilns 

Impact on Emissions, 

+/- % change Control Type 

NOX SO2 PM Hg CO2 Other 

Electricity 

Consumption, 

MWh/ton of 

clinker 

Process Water 

Consumption, 

ton/ton of 

clinker 

Solvent CCS1 New -99% -99% -85% -0.022 0.429 

Solvent CCS1 Retrofit -99% -99% -85% -0.103 0.160 

Oxy-combustion -99% -99% -85% 0.174 

Notes to Table 3-11 1. CCS = carbon capture and sequestration. 

References for Table 3-11 Andover Technology Partners,, Cost and Performance of Controls, March 10, 2009. 

EPA, Report to Congress on Cement Kiln Dust, Chapter 3: CKD Generation and Characteristics; US Environmental Protec
3-20 

Table 3-12. HCl, Hg, and THC Control Technologies for Cement Kilns 

Impact on Emissions, 

Control Type ± % change 1

NOX SO2 PM Hg Other 

ACI2 -99.9% -90% -50% for THC 

Membrane Bag -99.9% 

RTO3 -98% for THC 

Dry Lime Injection -50% -75% for HCl 

Wet Scrubber -90% to 

-95% -80% 

Notes to Table 3-12 

1. Feasible combinations of the above control technologies may be utilized as necessary. 

2. ACI = activated carbon injection. 

3. RTO = regenerative thermal oxidizer 

Table 3-13. Multimedia Impacts of Process Capacity Replacement on Cement Kiln Operation1

Electricity 

Consumption, 

kWh/ton of cement 

Waste 

Kiln Type 

Water 

Consumption,

gal/ton of 

cement2

Grinding3 Kiln 

Operation4

Generation 

Rate, ton/ton 

of cement5

Disposal Cost, 

$/ton of 

cement 

Wet to 

Precalciner -2142,6 -128

 -78 -0.072 see notes 9,10

Long Dry 

to 

Precalciner 

No change2 Not 

available7

Not 

available7 -0.062 see notes 9,10

Preheater 

to 

Precalciner 

No change2 Not 

available7

Not 

available7 0

11 see notes 9,10

Notes to Table 3-13. 

1. Unless specifically noted otherwise, impacts are presented on a per-short-ton-of-cement basis and 

generally make use of published data reported on a per-short-ton-of-clinker basis. These values are 

converted to a per-ton-of-cement basis by assuming that cement consists of 92-percent clinker. All 

weight units in this table are short tons. 

2. Water is not normally consumed in dry process cement kiln processes, except on an emergency basis 

to prevent damage to process equipment by hot gas or solid process streams. Water is used in noncontact cooling processes that are common in both wet process and dry process cement plants, some 

of which may or may not use closed circuit cooling systems with no evaporation losses. Water is 

consumed in direct contact cooling processes such as cement grinding in both wet process and dry 
3-21 

process plants. However, the nature and amount of such water consumption is not intrinsically 

different between wet process and dry process plants. 

3. Power data reported in Worrell and Galitsky (2004) are used for raw mix preparation and kiln process 

electrical energy consumption for wet and all dry processes. 

4. Air pollution control device contributions to electric power consumption data on cement kiln process 

systems are assumed to include only existing particulate matter control devices and not scrubbers for 

SO2 or other criteria pollutants. 

5. Solid waste from all process types is assumed to be cement kiln dust (CKD). Data used here are as 

reported in EPA (1993). 

6. Cement is assumed to consist of 92-percent clinker. Clinker is assumed to require 1.42 tons of raw 

kiln feed (dry) per ton of clinker. Kiln feed loss on ignition is assumed to be 35 percent. Kiln feed 

slurry is assumed to contain 36-percent water. 

7. Industry-wide statistics on electrical energy use do not distinguish among the various cement 

processing stages between the various dry processes – all dry process plants are averaged together. 

Published data that distinguish between various process types combine all process phases without 

indicating energy use for individual process phases. Therefore, electrical energy use is reported here

to be the same for all dry process plants. See Worrell and Galitsky (2004). 

8. As stated in note 7, industry data on electric power use in raw materials preparation and kiln 

processing are not available for every dry process. Data are for all dry processes are combined. 

9. Data reported in EPA (1993) on CKG generation by cement plants doe not distinguish between 

preheater and precalciner kilns with respect to net CKD generation rates. 

10. CKD disposal prices vary widely by location. Transportation of CKD is a significant component of 

the cost of disposal if disposal is off-site. For example, an estimated off-site disposal cost ranging

from $26 to $69 per ton of CKD for disposal, plus transportation costs ranging from $500 - $1,500 

per truck load for one northern California location, depending on the landfill chosen. (ARCADIS, 

2009) On-site disposal costs would include costs of transportation, dust control, and landfill 

operation. These costs have not been determined and would most certainly vary widely by location 

based on terrain, site geology, landfill operating requirements, and permit and future closure costs. 

11. Modern preheater cement kilns and precalciner cement kilns generate similar volumes of CKD. Older 

preheater cement kilns are similar in CKD generation to long dry process cement kilns. 
3-22 

Table 3-14. Energy Efficiency Measures for Raw Materials Preparation 

Electricity Consumption Change, 

kWh/ton clinker 

Energy Efficiency Improvement Method 

Dry Wet Preheater 

Precalciner

ETS (Efficient Transport System) -3.20 -3.20 -3.20 

RMB (Raw Materials Blending) -2.70 -2.70 -2.70 

PCVM (Process Control Vertical Mill) -0.90 -0.90 -0.90 

HERM (High Efficiency Roller Mill) -11.05 -11.05 -11.05 

SBH (Slurry Blending and Homogenization) -0.35 

WMCCC (Wash Mills with Closed Circuit Classifier) -12.00 

RMTHEC (High-Efficiency Classifiers) -5.05 -5.05 -5.05 -5.05 

Table 3-15. Energy Efficiency Clinker Making Measures 

Electricity Consumption Change, 

kWh/ton clinker 

Heat Input Change, MMBtu/ton of 

Energy Efficiency clinker 

Improvement Method 

Dry Wet Preheater 

Precalciner Dry Wet Preheater Precalciner 

EMCS 

(Energy Management and 

Control System) 

-1.90 -1.50 -1.90 -1.90 -0.15 -0.21 -0.15 -0.15 

SR 

(Seal Replacement) -0.02 -0.02 -0.02 -0.02 

CSI 

(Combustion System 

Improvement) 

 -0.25 -0.35 -0.25 -0.25 

IF 

(Indirect Firing) -0.16 -0.16 -0.16 -0.16 

SHLR 

(Shell Heat Loss Reduction) -0.20 -0.20 -0.20 -0.20 

OGR 

(Optimize Grate Cooler) 0.90 0.90 0.90 -0.09 -0.10 -0.09 -0.09 

CGC 

(Convert to reciprocating 

grate cooler) 

2.40 2.40 2.40 2.40 -0.23 -0.24 -0.23 -0.23 

HRPG 

(Heat Recovery for Power 

Generation) 

-18.0 

EMD 

(Efficient Mill Drives) -2.00 -1.70 -2.00 -2.00 
3-23 

Table 3-16. Energy Efficiency Measures for Finish Grinding 

Electricity Consumption Change, 

Energy Efficiency Improvement Method kWh/ton clinker 

Dry Wet Preheater Precalciner

EMPC (Energy Management and Process Control) -1.60 -1.60 -1.60 -1.60 

IGMBM (Improved Grinding Media [Ball Mills]) -1.80 -1.80 -1.80 -1.80 

HPRP (High-Pressure Roller Press) -16.00 -16.00 -16.00 -16.00 

HEC (High-Efficiency Classifiers) -3.85 -3.55 -3.85 -3.85 

Table 3-17. Energy Efficiency Plant-wide Measures 

Electricity Consumption Change, 

Energy Efficiency Improvement Method kWh/ton clinker 

Dry Wet Preheater Precalciner

PM* (Preventive Maintenance) -2.50 -2.50 -2.50 -2.50 

HEM (High Efficiency Motors) -2.50 -2.50 -2.50 -2.50 

ASD (Adjustable Speed Drives) -6.25 -6.00 -6.25 -6.25 

OCAS (Optimization of Compressed Air Systems) -1.00 -2.50 -1.00 -1.00 

*This is the only occurrence wherein “PM” does not stand for “particulate matter” 
3-24 

References for Chapter 3 

American University (2008). TED Case Studies: Cemex Case. http://www.american.edu/TED/cemex.htm, 

accessed October 21, 2008. 

Andover Technologies Partners (2009a). Memorandum: NOX, SO2 and CO2 Emissions from Cement Kilns 

(Emissions Memo), from Jim Staudt to Ravi Srivastava, Samudra Vijay, Elineth Torres. Dated March 10, 

2009 (see Appendix A of this document). 

Andover Technology Partners (2009b). Memorandum: Costs and Performance Controls, from Jim Staudt 

to Ravi Srivastava, Samudra Vijay, Elineth Torres. Dated March 10, 2009 (see Appendix A of this 

document). 

Andover Technology Partners (2009c). Memorandum: GHG Mitigation Methods for Cement, from Jim 

Staudt to Ravi Srivastava, Nick Hutson, Samudra Vijay, Elineth Torres. Dated July 10, 2009 (see 

Appendix A of this document). 

Andover Technologies Partners (2010a). Memorandum: Wet Scrubber Cost Algorithms, from Jim Staudt 

to Ravi Srivastava, Elineth Torres, Keith Barnett. Dated February 26, 2010 (see Appendix A of this 

document). 

Andover Technologies Partners (2010b). Memorandum: Electrical Load for Wet Scrubbers, from Jim 

Staudt to Elineth Torres. Dated May 7, 2010 (see Appendix A of this document). 

ARCADIS (1992). Theoretical Approach for Enhanced Mass Transfer Effects in-Duct Flue Gas 

Desulfurization Processes. Final Report. ARCADIS, 1992. 

ARCADIS (2009). Personal Comm. with E. Leamer of ARCADIS, 26 February, 2009. 

Burtraw, D. (2010). Supply Elasticity Estimation, memorandum to Ravi Srivastava, U.S. EPA, March 10, 

2010. 

Depro, Brooks, Rogozhin, Alex, and Wood, Dallas (2007). RTI International. “Documentation for 

Portland Cement Kiln Cost Functions (2005)”, Memorandum to Keith Barnett, US EPA, August 31, 2007 

(see Appendix A of this document). 

Depro, Brooks. (2010). RTI International. “ISIS Cement Production Costs”, Memorandum to Elineth 

Torres, US EPA, March 31, 2010 (see Appendix A of this document). 

Depro, Brooks and Lentz, Anthony. (2010). RTI International. “ISIS Cement Production Costs”, Excel 

Spreadsheet Attachment to Memo of Same Name from Brooks Depro (2010), February 16, 2010. 

BLS (2008). Bureau of Labor Statistics, Databases, Tables & Calculators by Subject, Major Sector 

Productivity and Costs Index. Series ID: PRS30006112, Sector: Manufacturing. 

http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_numbers&series_id=PRS30006112, 

accessed November 24, 2008. 

EIA (2008) Annual Energy Outlook 2008, DOE/EIA-0383(2008), June 2008. 

http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2008).pdf, accessed October 21, 2008. 
3-25 

EPA (1993). Report to Congress on Cement Kiln Dust, Chapter 3: CKD Generation and Characteristics; 

US Environmental Protection Agency: 1993. 

EPA (1995). Compilation of Air Pollution Emission Factors - Volume 1: Stationary Point and Area 

Sources. Fifth Edition, Supplements A-F, AP-42. U.S. Environmental Protection Agency, Research 

Triangle Park, North Carolina. 

EPA (1998). June 1998. Regulatory Impact Analysis of Cement Kiln Dust Rulemaking. Washington, DC: 

U.S. Environmental Protection Agency. 

http://www.epa.gov/osw/nonhaz/industrial/special/ckd/ckd/ckdcostt.pdf, accessed October 21, 2008. 

EPA (2004). Alternative Control Techniques Document - NOX Emissions from Cement Manufacturing, 

dated March 1994 (EPA-453/R-94-004) 

EPA (2005). EPA 2002 National Emission Inventory v3. 

http://www.epa.gov/ttn/chief/net/2002inventory.html, accessed October 21, 2008 

EPA (2006). EPA 2005 National Emissions Inventory Version 2. 

http://www.epa.gov/ttn/chief/net/2005inventory.html accessed November 17, 2008 

EPA (2007). November 2007. Alternative Control Techniques Document Update - NOX Emissions from 

New Cement Kilns. EPA-453/R-07-006. Research Triangle Park, NC. US Environmental Protection 

Agency. 

FLSmidth & Co. A/S (2007). Q2 Report 2007. August 2007 

http://hugin.info/2106/R/1148414/219358.pdf, accessed October 21, 2008. 

NESCAUM (2000). Status Report on NOX Controls for Gas Turbines, Cement Kilns, Industrial Boilers, 

Internal Combustion Engines: Technologies & Cost Effectiveness. Northeast States for Coordinated Air 

Use Management, December 2000. Accessed at http://www.nescaum.org/documents/nox-2000.pdf/

OMB (1992). Guidelines and Discount Rates for Benefit-Cost Analysis of Federal Programs. OMB 

Circular No.A-94 (Revised). U.S. Office of Management and Budget, October 29, 1992. 

http://www.whitehouse.gov/omb/circulars/a094/a094.html#8, accessed October 21, 2008. 

PCA (2004). Innovations in Portland Cement Manufacturing. Portland Cement Association. Edited by J. 

I. Bhatty, F. M. Miller, and S. H. Kosmatka. 2004. 

PCA (2004a). U.S. and Canadian Portland Cement Industry: Plant Information Summary of December 

31, 2004. 

PCA (2006). U.S. and Canadian Portland Cement Industry: Plant Information Summary. Portland Cement 

Association, Skokie, IL, 2006. 

PCA (2008). Forecast Report: Long-Term Cement Consumption Outlook. By Ed Sullivan January 31, 

2008. http://www.cement.org/econ/pdf/Long-TermFlashwinter2007nonmember.pdf, accessed October 21, 

2008. 

PCA (2009a). PCA Capacity Report. Flash Report. Portland Cement Association’s Economic Research 

Department. Updated October 19, 2009. 
3-26 

PCA (2009b). Forecast Report: Long-Term Cement Consumption Outlook. By Ed Sullivan. October 28, 

2009. Portland Cement Association. 

Ryan, S. (2006). The Costs of Environmental Regulation in a Concentrated Industry. Department of 

Economics, MIT, Cambridge, MA, October 5, 2006. http://econ-www.mit.edu/files/1166, accessed 

October 21, 2008. 

RTI (2010). ISIS Input Price Escalation Factors, Excel Spreadsheet. 

TRAGIS (2003). Transportation Routing Analysis Geographic Information System (TRAGIS) User’s 

Manual, Revision 0, Oak Ridge National Laboratory, June 2003. 

https://tragis.ornl.gov/TRAGISmanual.pdf. 

USGS (2005). Background Facts and Issues Concerning Cement and Cement Data. U.S. Geological 

Survey, Open-File Report 2005-1152. http://pubs.usgs.gov/of/2005/1152/2005-1152.pdf, accessed 

October 21, 2008. 

USGS (2007a). 2005 Minerals Yearbook: Cement. U.S. Geological Survey, p. 16.2, February 2007, 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/cemenmyb05.pdf, accessed October 21, 2008 

USGS (2007b). Mineral Commodity Summaries: Cement, U.S. Geological Survey, pp. 40-41, January 

2007. http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/cemenmcs07.pdf, accessed October 

21, 2008. 

USGS (2009). 2005 Minerals Yearbook: Cement. U.S. Geological Survey (Table 17), pp. 16.21. 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/myb1-2007-cemen.pdf. 

Worrell, E.; Galitsky, C. (2004). Energy Efficiency Improvement Opportunities for Cement Making; 

Lawrence Berkeley National Laboratory, LBNL-54036, Jan 2004. An ENERGY STAR® Guide for 

Energy and Plant Managers, http://www.osti.gov/energycitations/servlets/purl/821915-Re2kcK/native/. 

Yates, J.R. et al. (2003). CemStar Process and Technology for Lowering Greenhouse Gases and Other 

Emissions while Increasing Cement Production. 

http://www.hatch.ca/Environment_Community/Sustainable_Development/Projects/Copy%20of%20Cem

Star-Process-final4-30-03.pdf accessed February 27, 2009. 
4-1 

Chapter 4 

Model Calibration 

Large techno-economic models of ISIS framework size require model calibration as they utilize extensive 

amount of data which comes from different sources. This chapter outlines calibration methodology that 

was used, discusses data used for calibration, presents calibration results, and gives further 

recommendations. 

4.1 Calibration Methodology 

Calibration methodology utilizes the concept of calibration constant. The calibration constant has been 

developed to account for possible errors in costs. The value of calibration constant, calconst(i), is set by 

trial and error during calibration. The objective of the trial and error approach is to minimize the absolute 

difference in the reported and model-predicted prices (which are marginal values of the supply equation) 

for each USGS district. 

In the first step of calibration, the model is set to run for 2005-2007 by making appropriate changes in the 

input “Policy” worksheet, and GAMS input files. The import quantities are then adjusted equal to 

reported import quantity for each of the import district, except for those of Mexico and Canada. 

In the next step, impact of changing the calibration constant is monitored. This impact of the calibration 

constant is assessed on estimated production quantities to keep the difference between reported and model 

predicted production values within reasonable limits

5

. The calibration constant modifies each kiln’s 

variable cost of production. “Calibration” worksheet within the Inputs workbook has values of calibration 

constant assigned for each USGS district. Finally, in the input GAMS file, the values are assigned for a 

given USGS district to each of the kilns located in that USGS district. 

The model is first calibrated for year 2005, to obtain values of the calibration parameter calconst(i) for the 

year. Next, calconst(i) values for 2005 are used as starting point and obtain values for the same parameter 

for 2006. Similarly, the process is repeated to obtain the values for 2007. Then, an average of the 

calconst(i) values over the three years is taken and used for the future model runs. Current values of the 

parameter Calconst(i) being used in the model runs can be found in the worksheet “Calibration” of the 

“ISIS_Inputs.xls” workbook. 

Calibration is a dynamic process, and it is recommended that model calibration be performed periodically. 

In this fashion, any available new production, imports, or price data could be utilized in t he model.. 

4.2 Data for Calibration 

Production quantities in various USGS production districts, import levels in the import districts and 

reported cement prices for USGS districts are the key quantities used for calibration of the model. 

 

5

 There is no standard method to guide user in determining acceptable level of “error” in the reported and predicted values for the 

purpose of calibration. In this works, we have set acceptable level for the absolute gap between the individual reported and 

predicted values to ± 15%, although effort has been made to keep this level below 10% for most of the quantities. However, due 

to discontinuities in the transportation matrix, error in the reported data, or other unknowns, the gap in the estimated and reported 

values may be higher in certain markets. 
4-2 

Reported data for years 2005, 2006 and 2007 were used to calibrate the model and obtain values of 

appropriate calibration parameters. 

4.2.1 Cement Prices in USGS Districts 

Reported cement prices in various USGS districts are shown in Table 4-1 for years 2005 through 2007. 

For a given year, the reported cement price shows significant variation across USGS districts. For most 

districts the cement price has increased over the three year period. USGS districts assignation is the

location of the reporting production facilities. 

4.2.2 Cement Production in USGS Districts 

Reported cement production in various USGS districts is shown in Table 4-2 for years 2005 through 

2007. For a given year, the reported cement production shows significant variation across USGS districts. 

Generally production dropped from 2005 to 2007, due to a decrease in demand resulting from the 

economic downturn. 

4.2.3 Cement Imports by Import Districts 

Reported cement import quantity in various import districts is shown in Table 4-3 for years 2005 through 

2007. For a given year, the reported cement imports show a significant variation across import districts. 

Generally imports dropped from 2005 to 2007, due to a decrease in demand resulting from the economic 

downturn. New Orleans (LA), San Francisco (CA) and Tampa (FL) suffered large drops in the quantities 

of cement imported in 2007 compared to that in 2005. 

4.3 Results of Calibration 

Reported and calculated values of cement prices by USGS district are shown for years 2005, 2006, and 

2007 in Table 4-4, Table 4-5, and Table 4-6, respectively. As can be seen from these tables, in most of the 

districts reported and calculated cement prices are within 10% range, whereas in a few they are higher. It 

should be noted that in general the price differentials are smaller in the year 2005 and have increased in 

2007. In some USGS districts the reported and calculated prices are different than the model predicted

prices due to several factors, including error in reported prices, discontinuities in the transportation 

matrix, and/or unique factors for some specific markets. 

Specifically, the Arizona and New Mexico USGS district has a price differential due to capacity 

constraints in New Mexico. New Mexico has only two small, very old kilns with a capacity half than the

demand in New Mexico. The balance demand is met by kilns in Texas, and imports, which incur a 

significant transportation cost, resulting in a high price for cement in this market. Similarly, Oregon and 

Washington USGS District is dependent on supplies from other states. While demand in Washington is 

met by domestic production, and imports from Canada, Oregon, relies on imports from Canada, and other 

nearby states, resulting in higher cost. 

Generally, individual market prices are within the criteria specified in the QA document, the aberrations 

are explained by the demand-supply gap and transportation cost. Moreover, root-mean-square values of 

the price difference for 2005 and 2006 are about 12% and 10% respectively, and 18% for 2007. 
4-3 

Further while calibrating for price, aggregate production level is also tracked to make sure that it is within 

reasonable limits. The reported and predicted aggregate production levels for 2005-2007 are shown in 

Table 4-7. 

The current set of calibration constant values are averaged over the years of calibration, and are available 

in the “Calibration” worksheet in the “ISIS_Inputs.xls” workbook. 
4-4 

Table 4-1. Cement Prices ($/short ton) for USGS Districts 

USGS District 2005 2006 2007 

Alabama 74.61 81.74 87.09 

Illinois 80.29 89.81 91.17 

Indiana 72.03 80.04 79.99 

Kansas 76.58 86.50 92.87 

Maryland 74.76 81.19 80.18 

Missouri 78.92 86.61 89.36 

Ohio 82.08 90.24 91.47 

South Carolina 68.57 80.46 87.54 

Maine and New York 80.74 92.53 96.62 

Pennsylvania 79.73 89.99 90.85 

Michigan and Wisconsin 79.83 84.82 90.12 

Iowa, Nebraska and South Dakota 78.72 89.83 93.08 

Florida 77.56 90.44 95.55 

Georgia, Virginia and West Virginia 84.49 95.93 93.60 

Kentucky, Mississippi and Tennessee 84.37 89.81 88.90 

Arkansas and Oklahoma 89.00 96.99 97.74 

Texas 74.72 85.10 88.33 

Arizona and New Mexico 75.76 84.16 86.64 

Colorado and Wyoming 82.24 92.90 92.73 

Idaho, Montana, Nevada and Utah 83.61 90.26 95.25 

California 88.20 99.09 100.24 

Oregon and Washington 79.38 90.72 90.72 
4-5 

Table 4-2. Cement Production (short tons) for Various USGS Districts 

USGS District 2005 2006 2007 

Alabama 5,647,141 5,733,121 5,578,650 

Illinois 3,568,182 3,425,984 3,434,254 

Indiana 3,370,868 3,334,492 3,285,999 

Kansas 3,182,373 3,310,241 3,039,164 

Maryland 2,813,098 2,922,227 3,305,160 

Missouri 5,877,524 5,776,111 5,763,479 

Ohio 1,086,879 1,064,833 1,010,077 

South Carolina 3,601,251 3,654,162 4,057,414 

Maine and New York 3,572,591 3,699,357 3,471,388 

Pennsylvania 6,931,334 6,631,505 6,239,551 

Michigan and Wisconsin 6,171,841 5,993,267 6,047,588 

Iowa, Nebraska and South Dakota 4,962,606 5,024,335 4,889,607 

Florida 6,311,835 6,477,181 6,076,137 

Georgia, Virginia and West Virginia 2,612,478 2,696,253 2,528,648 

Kentucky, Mississippi and Tennessee 3,649,753 3,849,271 3,770,250 

Arkansas and Oklahoma 3,097,495 2,979,547 2,879,982

Texas 12,737,207 12,510,131 12,038,919 

Arizona and New Mexico 3,073,244 2,809,792 2,902,131 

Colorado and Wyoming 2,918,920 2,842,861 2,797,240 

Idaho, Montana, Nevada and Utah 3,400,630 3,354,333 3,308,645 

California 12,747,128 12,069,207 11,941,198 

Oregon and Washington 2,175,963 2,101,005 2,103,481
4-6 

Table 4-3. Cement Imports (except from Canada and Mexico) for Import Districts (short tons) 

Import District 2005 2006 2007 

Baltimore, MD 146,300 203,500 18,700 

Boston, MA 145,200 50,600 3,300 

Buffalo, NY 6,600 4,400 0 

Charleston, SC 1,212,200 1,100,000 363,000 

Chicago, IL 1,100 1,100 0 

Cleveland, OH 0 1,100 0 

Columbia-Snake, ID-OR-WA 831,600 1,115,400 1,180,300 

Detroit, MI 59,400 0 0 

Duluth, MN 0 0 0 

El Paso, TX 0 0 0 

Great Falls, MT 0 0 0 

Houston-Galveston, TX 2,880,900 3,705,900 3,641,000

Laredo, TX 0 0 0 

Los Angeles, CA 3,357,200 3,759,800 2,029,500 

Miami, FL 2,395,800 2,311,100 980,100 

Minneapolis, MN 0 0 0 

Mobile, AL 565,400 573,100 0 

New Orleans, LA 4,503,400 5,090,800 1,171,500 

New York, NY 1,390,400 1,327,700 810,700 

Nogales, AZ 0 0 0 

Norfolk, VA 767,800 793,100 447,700 

Ogdensburg, NY 0 0 0 

Pembina, ND 0 0 0 

Philadelphia, PA 544,500 665,500 342,100 

Portland, ME 0 0 0 

Providence, RI 814,000 680,900 509,300 

San Diego, CA 620,400 708,400 427,900 

San Francisco, CA 2,599,300 3,081,100 1,524,600 

Savannah, GA 88,000 204,600 376,200 

Seattle, WA 368,500 733,700 644,600 

St. Albans, VT 0 0 0 

St. Louis, MO 0 0 3,300 

Tampa, FL 3,825,800 3,791,700 1,523,500 

Wilmington, NC 427,900 416,900 284,900 
4-7 

Table 4-4. Reported and Calculated Cement Prices in USGS Districts (2005) 

USGS District Reported Calculated %∆

Alabama 74.61 78 5 

Illinois 80.29 83 3 

Indiana 72.03 84 16 

Kansas 76.58 81 6 

Maryland 74.76 74 -1 

Missouri 78.92 82 4 

Ohio 82.08 83 1 

South Carolina 68.57 76 10 

Maine and New York 80.74 73 -9 

Pennsylvania 79.73 77 -3 

Michigan and Wisconsin 79.83 81 2 

Iowa, Nebraska and South Dakota 78.72 81 3 

Florida 77.56 75 -3 

Georgia, Virginia and West Virginia 84.49 80 -6 

Kentucky, Mississippi and Tennessee 84.37 81 -4 

Arkansas and Oklahoma 89.00 87 -2 

Texas 74.72 78 4 

Arizona and New Mexico 75.76 105 39 

Colorado and Wyoming 82.24 98 20 

Idaho, Montana, Nevada and Utah 83.61 70 -16 

California 88.20 85 -4 

Oregon and Washington 79.38 69 -13 
4-8 

Table 4-5. Reported and Calculated Cement Prices in USGS Districts (2006) 

USGS District Reported Calculated %∆

Alabama 81.74 85 4 

Illinois 89.81 83 -8 

Indiana 80.04 85 6 

Kansas 86.50 83 -4 

Maryland 81.19 80 -1 

Missouri 86.61 81 -6 

Ohio 90.24 83 -8 

South Carolina 80.46 85 6 

Maine and New York 92.53 78 -16 

Pennsylvania 89.99 80 -11 

Michigan and Wisconsin 84.82 80 -6 

Iowa, Nebraska and South Dakota 89.83 81 -10 

Florida 90.44 82 -10 

Georgia, Virginia and West Virginia 95.93 86 -10 

Kentucky, Mississippi and Tennessee 89.81 84 -6 

Arkansas and Oklahoma 96.99 91 -6 

Texas 85.10 81 -4 

Arizona and New Mexico 84.16 105 25 

Colorado and Wyoming 92.90 100 8 

Idaho, Montana, Nevada and Utah 90.26 83 -8 

California 99.09 85 -14 

Oregon and Washington 90.72 74 -18 
4-9 

Table 4-6. Reported and Calculated Cement Prices in USGS Districts (2007) 

USGS District Reported Calculated %∆

Alabama 87.09 73 -17 

Illinois 91.17 72 -21 

Indiana 79.99 75 -7 

Kansas 92.87 71 -24 

Maryland 80.18 74 -8 

Missouri 89.36 71 -21 

Ohio 91.47 76 -17 

South Carolina 87.54 69 -21 

Maine and New York 96.62 74 -24 

Pennsylvania 90.85 75 -17 

Michigan and Wisconsin 90.12 76 -15 

Iowa, Nebraska and South Dakota 93.08 71 -23 

Florida 95.55 66 -31 

Georgia, Virginia and West Virginia 93.60 74 -20 

Kentucky, Mississippi and Tennessee 88.90 71 -20 

Arkansas and Oklahoma 97.74 88 -10 

Texas 88.33 80 -10 

Arizona and New Mexico 86.64 101 17 

Colorado and Wyoming 92.73 89 -4 

Idaho, Montana, Nevada and Utah 95.25 83 -13 

California 100.24 81 -20 

Oregon and Washington 90.72 75 -18 

Table 4-7. Aggregate production (reported and modeled) for 2005-2007 

Year Reported Production 

(million short tons) 

Calculated Production 

(million short tons) %∆

2005 111.84 112.54 0.63 

2006 110.30 111.23 0.84 

2007 108.17 101.95 -5.75 

 
4-10 

4.4 Recommendations 

If any of the key input parameters, specifically those relating to production quantities and costs, are 

refined or otherwise modified or additional observed data becomes available, the calibration of the model 

should be repeated. Transportation matrix, modes, and cost of transportation also have significant impact 

on the behavior of production distribution across the districts and production prices. Therefore, if any 

further modifications or refinements are made to the transportation matrix, the model needs to be recalibrated. 

At the time of calibration of the current version of the model, production, import and price values for only 

2005 to 2007 were available. As discussed above, when the new values become available, the model 

needs to be calibrated again. Calibration of the model should be repeated as soon as new information or 

new observed data become available. Due to practical limitations it is recommended that the calibration of 

the model be repeated every two years. Further, since the calibration data is available only for three years, 

equal weight was given to the parameters obtained for each year. Once larger data-set is available, a 

modified weighing system can be adopted to give highest weight to the most-recent year data.
4-11 

4.5 References for Chapter 4 

United States Geological Service. Mineral Yearbook 2008. Table 5. 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/

United States Geological Service. Mineral Yearbook 2008. Table 3. 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/

United States Geological Service. Mineral Yearbook 2008. Table 11. 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/
5-1 

Chapter 5 

An Example Policy Analysis 

5.1 An Example Analysis of Potential Reductions in CO2 Emissions from the U.S. 

Cement Industry 

This section presents an example analysis of the U.S. cement industry utilizing ISIS-cement model. The

analysis investigates the potential for near-term reductions in emissions of CO2 and other pollutants, 

associated costs, and industry operation. The regulatory mechanism selected for analysis was cap-andtrade of CO2. This mechanism has been used in many of the recent policy proposals addressing reduction 

of CO2 emissions. In a cap and trade framework, CO2 allowance price is a metric of primary importance 

for deciding on CO2 reduction levels. For a reduction range selected for analysis to be practicable, CO2

allowance price projected by the model for a given year should be in agreement with allowance prices 

determined by recent analyses. The recent analysis selected for comparison with model projections was 

the American Clean Energy and Security Act of 2009. Once the range of CO2 emission reduction is 

determined to be practicable, the model projects characteristics of the industry for this emission reduction 

range. The analysis as described above is an example of the type of sector-specific analyses that may be 

needed for developing GHG reduction policies for industrial sectors. 

Two broad questions were investigated in this example analysis: 1) what range of CO2 reduction options 

may be practicable in the near-term (i.e., the decade ending 2020), and (2) for that range, what may be the 

market characteristics for the U.S. cement industry? The first question is relevant because in the absence 

of carbon capture technology, the path forward for reducing emissions in the near-term will need to 

depend on the currently available energy efficiency measures and raw material and product substitution

approaches, some of which are described earlier. The second question speaks to industry operation under 

the potentially practicable options determined while answering the first question. 

To investigate the above questions, CO2 emissions targets corresponding to a range of reductions from 

projected CO2 emissions in 2013 were analyzed. Relatively modest reduction levels ranging from 5 to 25 

percent were chosen to be generally consistent with the capabilities of energy efficiency and raw material 

substitution measures. These targets are summarized in Table 5-1. Only the measures depicted in Tables

3-15 and 3-17 were considered in this example analysis because at this time ISIS-cement does not include 

calculation procedures for the measures shown in Tables 3-14 and 3-16. In addition to the energy 

efficiency measures of Tables 3-15 and 3-17, a fuel substitution technology, MKF-tires, and a raw 

material substitution technology, CemStar, are also considered in this analysis. As mentioned before, the 

use of MKF-tires results in reduced fossil fuel consumption (by about 15%) and this fuel consumption 

reduction is included in ISIS-cement. Relative to CemStar, a 7.5-percent reduction in limestone and a 

3-percent reduction in fuel for every short ton of clinker produced is included in ISIS-cement. The costs 

for energy efficiency measures are described in Andover Technology Partners (2009) and for MKF-tires 

and CemStar in Andover Technology Partners (2008). These costs are included in ISIS-cement. 

Information on the extent to which each of the energy efficiency measures in Tables 3-15 and 3-17 could 

be applied on existing kiln capacity was obtained (Xu, 2010) and included in ISIS-cement. These 

penetration numbers ranged from 5 percent for CGC to 88 percent for preventive maintenance. Also for 

this example analysis it was assumed that each of CemStar and MKF-tires cannot be applied on more than

25 percent of the existing and unretired kiln capacity over the horizon 2013-2020. 
5-2 

Table 5-1. CO2 Caps Modeled in this Analysis 

CO2 Cap 

(short tons) 

Reduction from projected 2013 

Emission Level (%)* 

64,099,115 5 

60,725,478 10 

57,351,840 15 

53,978,202 20 

50,604,565 25 

*2013 emissions were those under the BAU case. 

Runs were made with ISIS-cement for the BAU case (i.e., when no emission reduction requirements are 

in effect) and with each of the above caps. The corresponding results are presented in Figures 5-1 through 

5-7. 

In a cap-and-trade framework, CO2 allowance price is a primary metric for deciding on CO2 reduction 

levels. Figure 5-1 reflects that CO2 allowance price (in 2013 for example) for various reduction levels 

ranging from about 10 to 35 $ per short ton of CO2. A recent analysis of the American Clean Energy and 

Security Act of 2009 reflects a price range of 13-24 $ per metric ton in 2015 (EPA, 2009). Considering

this indication, a reduction range of 5 to 15 percent appears practicable for purposes of this example

analysis. 

0

5

10

15

20

25

30

35

40

0.0 5.0 10.0 15.0 20.0 25.0

CO2 Allowance Price ($/short ton)

CO2 Reduction (%)

Figure 5-1. Projected CO2 Allowance Prices 
5-3 

Two metrics for additional evaluation of potential emission reductions levels could be: (1) U.S. industry 

revenue under policy, and (2) cement price under policy. Arguably, these metrics speak to the interests of 

both the industry and consumers. 

An increase in cement price under a CO2 reduction policy will cause a drop in demand. Also, if the policy 

does not impose any requirements on imports, these can increase under policy because production can 

shift to other countries. Both factors, drop in demand and increased levels of imports, can cause a 

reduction in revenue for the cement industry in the U.S. 

An increase in price will, in general, result in a reduction in demand because consumers will reduce their 

needs at higher prices. And the drop in demand will contribute to reduction in revenue for the industry. As 

mentioned before, ISIS-cement includes a procedure for calculating drop in demand with increase in 

price. 

Figures 5-2 and 5-3 show the U.S. industry revenue and cement price (in 2013, for example) for the BAU

case (0% reduction), and for emission reductions ranging from 5 to 25 percent. Figure 5-2 shows that the 

drop in U.S. industry revenue under policy relative to BAU ranges from about 4 to 6.5 percent for the 

reduction range of 5 to 15 percent. For the same reduction range, Figure 5-3 reflects that the increase in 

cement price ranges from about 5 to 12 percent. Figure 5-4 shows the drop in demand due to rising prices 

under increasing CO2 reduction levels. This drop contributes to the loss of revenue seen in Figure 5-2. 

Notably, the drop in demand relative to BAU is less than about 16 percent if the reduction levels are kept 

at or below 15 percent. 

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

43

44

45

46

47

48

0.0 5.0 10.0 15.0 20.0 25.0

Change in U.S. Industry Revenue (%)

U.S. Industry Revenue (billion $) 

CO2 Reduction (%)

U.S. Industry Revenue (billion $) Change in U.S. Industry Revenue (%)

Figure 5-2. Projected Revenue for the U.S. Cement Industry over the Horizon 2013-2020 
5-4 

0

5

10

15

20

25

0

40

80

120

0.0 5.0 10.0 15.0 20.0 25.0

Cement Price ($/short ton)

Change in Cement Price (%)

CO2 Reduction (%)

Cement Price Change in Cement Price (%)

Figure 5-3. Projected Average Cement Prices in 2013 for the U.S. Cement Industry 

-25

-20

-15

-10

-5

0

0

100

200

300

400

500

600

700

800

900

1,000

1,100

1,200

0.0 5.0 10.0 15.0 20.0 25.0

Change in Demand (%)

Demand (million short tons)

CO2 Reduction (%)

Demand (million short tons) Change in Demand (%)

Figure 5-4. Projected Demand for Cement 
5-5 

Figure 5-5 reflects that under example policy options imports increase relative to the BAU case. Also, as 

the cap is tightened from the 15-percent reduction level to the 25-percent reduction level, the industry 

resorts to increasing imports in a monotonic fashion. Since imports come with zero emissions in the U.S. 

and result in reduced domestic fuel and raw materials processing, they can help comply with a policy 

requiring reductions in domestic emissions. While the impact of increases in imports in the U.S. on 

emissions in exporting countries is beyond the scope of this analysis, such increases in imports will 

generally result in increases in CO2 and other emissions in exporting countries. 

0

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Imports (million short tons)

CO2 Reduction (%)

Imports (million short tons) Change in Imports (%)

Figure 5-5. Projected Imports for the U.S. Cement Industry over the Horizon 2013-2020 

A concern related to drop in revenue can be retirement/mothballing of kiln capacity in the U.S. cement

industry. Figure 5-6 reflects the potential for retirements/mothballing under the example reduction 

options. For this work, a unit is considered retired or mothballed if it does not produce in any year of the 

horizon 2013-2020. The figure shows that even under the BAU case, 12 to 13 percent of the existing 

capacity may be retired or mothballed because in each year of the horizon 2013-2020, the U.S. cement 

industry has excess kiln capacity relative to BAU demand. For the reduction range 5 to 15 percent, the

retired or mothballed capacity ranges from 23 to 27 percent of the existing capacity, or about 10 to 14 

percentage points over the BAU projection. 

Significant collateral reductions in other pollutant emissions may be possible under the range of 

practicable CO2 reduction levels arrived at in this example analysis. Figure 5-7 shows such collateral 

reductions in NOX and SO2 emissions. At the 15-perecnt CO2 reduction level, each of NOX and SO2

emissions may be reduced by more than 250 thousand short tons. These reductions result from use of 

energy efficiency measures (see below) and reduced production. 
5-6 

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Retired/mothballed Capacity (million short tons)

CO2 Reduction (%)

Retired/mothballed Capacity (million tons)

Retired/mothballed Capacity (%)

Figure 5-6. Projected Capacity Retirement/Mothballing 

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CO2 Reduction (%)

NOx SO2 CO2

Figure 5-7. Projected Emissions of NOX, SO2, and CO2 from the U.S. Cement Industry over the 

Horizon 2013-2020 
5-7 

For the cap at the 15-percent reduction level, which is in the range of practicable reductions for purposes 

of this example analysis, additional detailed results are presented in Figures 5-8 and 5-9. 

Figure 5-8 shows the projected CO2 emissions under the BAU case, emission caps corresponding to 

15-percent reduction from CO2 emissions in 2013 and banked allowances. The industry engages in 

relatively modest levels of CO2 allowance banking and related trading activity, practices consistent with 

the relatively modest level of reduction required. 

Figure 5-9 reflects that, in response to the CO2 reduction requirement, the industry installs and operates 

measures with multi-pollutant (NOX, SO2, and CO2) reduction benefits. The majority of these measures 

are installed in 2013 to help comply with the reduction requirement. The energy efficiency measures 

reflected in the legend of this figure are defined in Tables 3-15 and 3-17. Also, CemStar and MKF-tires 

are briefly described in the section on raw material and/or fuel substitution. Note that Figure 5-9 reflects 

that CemStar will need to be applied on about 40 percent of operating capacity in 2013. Consequently, a 

reliable and adequate supply of blast furnace slag (BFS) will need to be ensured for this level of CemStar 

application. 

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2013 2014 2015 2016 2017 2018 2019 2020

CO2 (million tons)

BAU Case Emissions, CO2 (tons) Emission Cap, CO2 (tons)

Controlled Emissions, CO2 (tons) Banked Allowances, CO2 (tons)

Figure 5-8. Projected CO2 Emissions and Reductions from the U.S. Cement Industry Under the 

BAU and the 10-percent CO2 Reduction Cases. 
5-8 

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2013 2014 2015 2016 2017 2018 2019 2020

Percent of Operating Capacity (%)

MKF-tires CEMstar PM EMCS SR CSI IF SHLR OGR

Figure 5-9. Projected Control Technology Applications in the U.S. Cement Industry Under the 

10-percent CO2 Reduction Case. 

5.2 Summary and Thoughts for Future Work 

In this chapter, ISIS-cement was used to conduct an example analysis of the U.S. cement sector to gain

some insights relative to two broad questions: (1) what range of CO2 reductions options may be 

practicable in the near-term, and (2) for that range, what may be the market characteristics for the U.S. 

cement industry? These questions are relevant because in the absence of carbon capture and sequestration 

technology, the path forward for reducing CO2 emissions in the near-term (decade ending 2020) will need 

to depend on the available energy efficiency measures and raw material and product substitution options. 

The energy efficiency measures for clinker making and plan-wide application were considered in this 

example analysis. In addition to the energy efficiency measures, a fuel substitution technology, MKFtires, and a raw material substitution technology, CemStar, were also included in the analysis. 

The example analysis reflects that, based on CO2 allowance prices, a range of CO2 reductions, 5 to 15 

percent, from the projected 2013 emissions appears to be practicable. For this range, the drop in U.S.

industry revenue under policy ranges from about 4 to 6.5 percent of the revenue in the BAU case and the 

increase in cement price (in 2013, for example) ranges from about 5 to 12 percent relative to the BAU.

Significant collateral reductions in other pollutant emissions may be possible under the range of 5- to 

15-percent CO2 reductions. For example, at the 15-percent CO2 reduction level, each of NOX and SO2

emissions may be reduced by more than 250 thousand short tons. To meet a CO2 reduction level of 

15 percent from the projected emissions in 2013, the U.S. cement industry would potentially use multipollutant energy efficiency improvement options and reduce domestic production. 
5-9 

The results of the example analysis presented above are indicative only for several reasons. First, as

mentioned before, the analysis did not include the full suite of abatement options potentially available in 

the near-term (i.e., the decade ending 2020). In particular, the analysis did not take into account the 

energy efficiency measures of Tables 3-14 and 3-16 and feasible raw material and product substitution 

approaches. Second, some assumptions associated with the abatement measures that were considered 

need additional evaluation. In particular, availability of materials (e.g., blast furnace slag, tires) and the 

degree to which a specific abatement measure could be applied across the industry need further 

assessment. Finally, extra-U.S. CO2 emissions associated with imports need to be taken into account 

while setting reduction targets. These areas need to be addressed to permit more comprehensive 

evaluation of the CO2 reduction potential relative to the U.S. cement industry. Work is being initiated to 

address these areas and modify ISIS-cement as needed. 

The focus of the example analysis presented is on more modest CO2 reductions potentially possible with 

use of energy efficiency approaches in the near-term. However, in the longer term more significant CO2

reductions (e.g., greater than 50%, perhaps) can only be achieved with use of CO2-specific mitigation 

technologies currently under development. 

5.3 Disclaimer 

Reference herein to any specific commercial products, process, or service by trade name, trademark, 

manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or 

favoring by the United States Government. The views and opinions of authors expressed herein do not 

necessarily state or reflect those of the United States Government, and shall not be used for advertising or 

product endorsement purposes. 
5-10 

References for Chapter 5 

Andover Technology Partners (2008). Cost and Performance of Controls, memorandum to U.S. EPA, 

September 25, 2008. 

Andover Technology Partners (2009). GHG Mitigation Methods for Cement,, memorandum to U.S. EPA, 

July 10, 2009. 

EPA (2009). EPA Analysis of the American Clean Energy and Security Act of 2009 HR 2454 in the 

111th Congress. U.S Environmental Protection Agency, Washington, DC, June 2009. 

http://www.epa.gov/climatechange/economics/economicanalyses.html. 

Xu, T. (2010). Personal communication: Tengfang Xu, Environmental Energy Technologies Division, 

Lawrence Berkeley National Laboratory with Ravi Srivastava, U.S. EPA, May 2010.