From: Torres.Elineth@epamail.epa.gov

Sent: Thursday, August 05, 2010 2:58 PM

To: Laney, Michael N.

Cc: Barnett.Keith@epamail.epa.gov

Subject:Comparative Memo and ISIS documentation for the docket

Attachments: 20100805 ISIS Cement Documentation_Final.pdf; ISIS documentation_All  

Appendices in one document.pdf; Fina Technical Memo_ Comparison of  

Analytical Tools_Aug_5_2010.pdf

Mike,

Please find attached the ISIS peer‐reviewed documentation and the appendices  

to the ISIS documentation.  Also attached the Final Technical memo comparing  

the analytical tools used in the NESHAP and NSPS analysis.  I will send the  

other documents in another email due to their size.

(See attached file: 20100805 ISIS Cement Documentation_Final.pdf)(See attached  

file: ISIS documentation_All Appendices in one document.pdf) (See attached  

file: Fina Technical Memo_ Comparison of Analytical

Tools_Aug_5_2010.pdf)

Elineth Torres

Environmental Protection Specialist

U.S. EPA

OAQPS/SPPD/Program Design Group

109 T.W. Alexander Dr.

Durham, NC 27711

Phone: 919‐541‐4347

Fax: 919‐541‐5600
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 ee)scost(t,i,

tcontrolco st(t,i,k)

timportsco st(t,id)

ttranscost(t,dc)

z tannualpro )dncost(t,i

dis(t)

dis(t) j)alprice(t,

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

dctprice ),( dctddemand ),( 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

DP )( (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 

min ⋅= range)-(1D0D (2.2.3) 

max +⋅= range)(1D0D (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 

stepsofNumber

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

1)-dstep(2dwidth

DD min

dstep ⋅ ⋅

+= (2.2.6) 

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

σ

1

dstep

dstep

D0

D

P0DP 















)( = (2.2.7) 

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

)(

dstep dstep P DD

stepsofNumber

1dsteps

∑=

⋅

(2.2.8) 
2-7 

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

∑ ∑

∑

∑

∑

∑

∑

∑













− ⋅ ⋅

⋅ ⋅ +

⋅ +

⋅ +

⋅ +

⋅ +

⋅= +

t dstepdc

t,j

t,i,ee

t,i,k

t,id

t,dc

t,i

dstepdctdemand

totalemissions(t,j)

teemeasure ee)scost(t,i,

tcontrolcost(t,i,k)

timportscost(t,id)

ttranscost(t,dc)

z tannualpro )dncost(t,i

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

dis(t) j)alprice(t,

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

prodnquant dcitity importedqu dcidtantity dstepdctdemand (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, 

prodnquant )ity(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, 

itprodn ),( ≤ i).,capacity(t (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 istepoi

importedquantity(t,id,dc) id,istep)oiImports(t,

,

, (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 

Imin = Imports0⋅ range)-(1 (2.3.6) 

I Imports0 range)(1 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 

steps ofNumber 

I-I

Iwidth 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

1)-istep(2Iwidth

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, 

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

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

;i),capacity(t

∈ ∪∪

<≤ +

= − + ∑ ⋅

v

addcap(t)addcap(t,i i)timeblock, )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 ∈= { }.iE(i)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) ecolifepla iP(i)i nts);

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

≤≤ + ∈

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) = itAddcap ⋅ i),capcostt(t),( ⋅ 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

itfc H2Oconsump )tncost(t,i SWdispcost(t,i,SW)

i)varcost(t, i)RMTt(t, i)ELCt(t,i)LBRt(t,i)VOMt(t, itprodn

(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

]]

ofNumber pollutants

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

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

f

⋅

= ⋅ ⋅

∑

∑

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

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 cos = Ht(t,i) 2Oconsumptn(t,i)⋅H2Ocostt(t) 1000/ (2.4.8) 

With annual water consumption given by 
2-13 

H 2Oconsumptn(t,i) = H2Ointensity(i)⋅ itprodn ).,( (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 sw)costt(t, (2.4.10) 

With annual solids discharges given by 

SWgen(t,i,sw) = SWgenerati sw)on(i, ⋅ itprodn ).,( (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 

tannualpro )dncost(t,i = plantcapcost(t,i) + i)varcost(t, . (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 

[( ) )].,,,(

,

istepsidoitImports

timportscost(t,id)

istepsoi

∑ + + ⋅

=

isteps)id,oi,Iprice(t, 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 

)].,,(

)],,(

Imported dcidtquantity

ttranscost(t,dc) prodn dcitquantity

id

i

∑

∑

⋅

= ⋅ +

[imprttran dc)id,(t,sportcostt

[prodntran dc)i,(t,sportcostt

(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. 

emintensit j)f,yfuel(i, modeintens k)/100].j,cp(i,[1ee)k,ity(i,

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

⋅ −⋅

=

(2.5.1) 

ee).,eHIdispl(i

(i)eintensity primaryHIc k)hange(i, secondaryH k)Ichange(i,

modeintens ee)k,ity(i,

+

+ +

=

(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, 

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

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, 

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

),,( = ∑ ⋅ .),,,,,,(

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

jitemissions ee)k,j,m,f,i,pol(t, eekjmfitvarprodn (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

totalemiss jtions jitemissions (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 

jmfitprodnpol ),,,,( = ≠ jjjjjmfitprodnpol ;);,,,,( (2.5.7) 

with, 

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

),,,,(

= ∑

eejkitpoltikjee

jmfitprodnpol eekjmfitvarprodn (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) techlifeco + [(vyear(v) trols(k)].n

;1

<≤

∑ ≤

vt

,v)t,i,kc(poltikts

,

)(_

(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) techlifeco .ntrols(k))

,_, ;i),capacity(t

<≤ +

= ∑ ⋅

v

ControlCap v)c(t,i,ktsk)(t,i

 (2.6.2a) 

For t > 1, 

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

, ,_, ;i),capacity(t

<≤ +

= − +∑ ⋅

v

ControlCap k)(t,i ControlCap (t v)c(t,i,ktsk)itimeblock,

 (2.6.2b) 

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

ControlCap kit ),,( + ControlCap kkit ),,( ≤ itcapacity ),( (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).

_ ;i),capacity(t

ofNumber pollutants

),,,,(

≤

≤ ∑ ⋅

∑

v

eejkitjeef,m,poltik

c(t,i,k,v)ts

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

(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 

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

cntrlcapit k)i,alcostt(t, CRFcontrol ;(k)

_

<≤ +

⋅ ⋅

=

ControlCap(t,i,k)

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

(2.6.4) 

Similarly, annual FOM cost is given by 

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

cntrlfixed ;k)i,costt(t,

(_ )

<≤ +

⋅

=

ControlCap(t,i,k)

FOMcost i,k)poltik(t, c

(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 

.

]

[

),,,(

),,,(,),(

ofNumber pollutants

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

primaryHIc k)hange(i, fr,t,fuelcostt(

∑

∑

+ ⋅ ⋅

⋅ ⋅

=

kf(k,f),m, j,eekitpoltikjee

j,eekitpoltikjee,mfifi

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

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

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/

),( ),,,(

),( ),,,(

ofNumber pollutants

secondaryRMTchangepercent i)RMTt(t,

primaryRMTchangepercent i)RMTt(t,

∑

∑

+ ⋅ ⋅

⋅ ⋅

=

j,eekitee,m,poltikjfifi

j,eekitee,m,poltikjfifi

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

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

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 

⋅reagentpri rgnt).cet(t,

=

∑

rgnt

rgntconsum rgntkitcpt

rgntconsum kitpoltikcptcost

),,,(_

)),,((_

(2.6.8) 

The annual consumption of a reagent given by 

.

[ ]

),,,(_

, ),,,,(),(

),( ),,,,(

ofNumber pollutants

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

ofNumber pollutants

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

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

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

rgntconsum rgnt)kitc(poltikpt

jm eejkit,poltikjeefifi

,jfifi eejkitem,poltikje

∑ ∑

∑ ∑

⋅

+

⋅

=

(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

catconsump catkitct

catconsumptcost c poltik t i k )

catalystpr catice(t,

(2.6.10) 

with 
2-21 

.

),,(_

),( ),,,,(

ofNumber pollutants

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

∑ ⋅⋅ ∑

=

j eejkitee,m,poltikjfifi

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

10000

catconsump ,cat)kitc(poltikt

(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 )kitc(poltik

_

),,(_

(2.6.12) 

with annual generation of a byproduct given by 

.

),,(_

),,,,(),(

),( ),,,,(

Number of pollutants

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

Number of pollutants

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

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

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

byproductg ,byprod)kitc(poltiken

m,j eejkit,poltikjeefifi

,jfifi eejkitem,poltikje

∑ ∑

∑ ∑

⋅

+

⋅

=

(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 

.

[ ]

),,(

),( ),,,,(,

ofNumber pollutants

∑ kWhperton( k)f,i, ⋅ ∑

=

fifi eejkitpoltikjeem

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

kwh_c(polt )kitik

(2.6.14) 

The cost of electricity consumption is 

kwhcost_c( ),,( )kitpoltik = k)kwh_c(t,i, ⋅ electricit i).ycostt(t, (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 _

),,,,(),(

),( ),,,,(

ofNumber pollutants

H2Ousermt( k)m,i,

ofNumber pollutants

H2Ousefuel k)f,(i,

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

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

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

m,j eejkit,poltikjeefifi

,jfifi eejkitem,poltikje

∑ ∑

∑ ∑

⋅

+

⋅

=

(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) byproductc k)ost_c(t,i,

fuelcostchange_c(t,i,k) RMTcostcha k)nge_c(t,i,

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

;1

<≤

∑ ≤

vt

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

,

)(_

(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)).

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

ee

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

v

<≤ +

= ∑ ⋅

 (2.7.2a) 

For t > 1, 

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

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

ee

EECap(t,i ee) EECap(t tsee)itimeblock, ee(poltiee v)t,i,ee

v

<≤ +

= − + ∑ ⋅

 (2.7.2b) 

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

∑[ + ]≤ ;),,(_),,(_ 1

v

ts ts,v)eeitee(poltiee ,v)eeeitee(poltiee (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)

i);,capacity(t

ofNumber pollutants

≤

≤ ∑ ⋅

∑

v

f,m,poltik j,eekijee(t

v)ee(t,i,ee,ts

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

_

),,,

(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. 

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

ecapitalco ee)i,stt(t, ;CRFee(ee)

_

<≤ +

⋅ ⋅

=

EECap(t,i,ee)

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

(2.7.4) 
2-25 

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

efixedcost ;ee)i,t(t,

(_ )

<≤ +

⋅

=

EECap(t,i,ee)

FOMcost poltiee(t,ee 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 

ofNumber pollutants

ee),eHIdispi(i ⋅ ∑ ⋅ fr,t,fuelcostt(

=

),,,,(

_

f,m,poltik eejkitjee

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

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 

ofNumber pollutants

kWhperton( ee)i, ⋅ ∑

=

),,,,(,,

),,(_

eejkitpoltikjeemf

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

kwh )eeitee(poltiee

(2.7.7) 

The cost of electricity consumption is 

kwhcost_ee ),,( )eeit(poltiee = kwh_ee(t,i ,ee) ⋅ electricit i)ycostt(t, (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 ee)scost(t,i,

_ _

_ _

+

+ +

=

(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, 

totalemiss jtions ),( j)ec(t, +−+≤ jperiodtbnkjtbnk ),(),( (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( j)i,t, ≤ j)i,,ecpminer(t (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 

jitemissions ),,( j)el(t, ⋅≤ itprodn ),( (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), 

)( ),(

,

tdis totalemiss jtions

jt

∑ ⋅ j)alprice(t, ⋅ (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 Orleansa1.75 

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 

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 temperaturthose 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 exnorthern California landfills range from $26/short ton to $69/short ton of CKD plus transportation, which may range frto $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 requirementincrease 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. ARCEPA (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 limits5

. 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

1

2

3

4

5

250

260

270

280

290

300

0.0 5.0 10.0 15.0 20.0 25.0

Change in imports (%)

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 

0

5

10

15

20

25

30

35

40

0

10

20

30

40

50

0.0 5.0 10.0 15.0 20.0 25.0

Change in Retired/mothballed Capacity (%)

Retired/mothballed Capacity (million short tons)

CO2 Reduction (%)

Retired/mothballed Capacity (million tons)

Retired/mothballed Capacity (%)

Figure 5-6. Projected Capacity Retirement/Mothballing 

0

100

200

300

400

500

600

700

0

200

400

600

800

1000

0.0 5.0 10.0 15.0 20.0 25.0

CO2 Emissions (million short tons)

NOx, SO2 Emissions (thousand short tons)

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. 

0

10

20

30

40

50

60

70

80

90

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 

0

5

10

15

20

25

30

35

40

45

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. 
A-1

Appendix A

Andover Technology Partners and RTI International Memos

Andover Technology Partners

May 7, 2010 Electrical Load for Wet Scrubbers

February 26, 2010 Wet Scrubber Cost Algorithms

July 10, 2009 GHG Mitigation Methods for Cement

March 10, 2009 Costs and Performance of Controls – revised from comments

March 10, 2009 NOX, SO2 and CO2 Emissions from Cement Kilns (Emissions Memo) – revised

from comments

RTI

March 31, 2010 ISIS Cement Production Costs

August 31, 2007 Documentation for Portland Cement Kiln Cost Functions (2005)
A-2

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Andover Technology Partners Page 1

July 10, 2009

To: Ravi Srivastava, Nick Hutson, Samudra Vijay, Elineth Torres

Cc: Clint Burklin

Fm: Jim Staudt

Re: GHG Mitigation Methods for Cement

The greenhouse gas mitigation methods that will be examined in this work are

broken down generally into four categories:

 Energy Efficiency Options – these options will reduce the amount of fossil

fuels used to manufacture cement, either directly through reduction of heat

input at the plant, or indirectly though reduction of electricity demands.

 Supplementary Cementitious Materials – These are materials that can be

used in lieu of manufactured Portland cement in blended cements or concrete,

and therefore do not release the CO2 that is involved in production of Portland

cement. Their use will mitigate the amount of CO2 produced per ton of

concrete used.

 Decarbonated kiln feedstocks – These include materials that do not require

calcining and are a substitute for CaCO3, which is a source of CO2 emissions.

 CO2 Capture and Sequestration – This is technology for capture of the CO2

produced from the kiln that would otherwise be released to the atmosphere.

The captured CO2 would be sequestered.

Andover Technology
Andover Technology Partners Page 2

Energy Efficiency Options

Reduction of energy consumption will reduce the CO2 emissions either directly

through reduction of fossil fuel requirements at the kiln or indirectly, through reduction

of electrical demands. Energy efficiency methods are especially attractive because there

is an economic as well as environmental benefit. Therefore, these methods can often be

justified even absent an environmental motivation. However, an environmental

motivation in addition to economic benefits will increase the likelihood that these “low

hanging fruit” approaches will be utilized.

Energy efficiency improvement options generally entail an up-front capital cost to

install or modify equipment. The up-front investment is recovered over time through the

realized reductions in fuel or electricity costs. Assuming similar year-to-year facility

operation, the energy savings should be similar from year-to-year. Hence, these methods

lend themselves to being represented economically in terms of simple payback, or how

many years of savings it takes to recover the cost of the initial investment. A shorter

payback period (typically represented in years) implies an economically more attractive

energy savings approach – at least from the perspective of the measure of payback

period. Worrell & Galitsky 1developed estimates of the payback period and the energy

savings of several energy saving methods. This report summarizes the capital cost

algorithms that have been developed for the energy efficiency measures that are

described by Worrell & Galitsky.

Summary of Capital Cost Estimation

Capital cost algorithms were developed which estimate capital cost of the energy

efficiency measure in proportion to the capacity of the kiln raised to the 0.60 power as in

Equation E.1. Table 1 provides a tabulation of the scale-up factors to estimate the capital

cost in 2008$ of various energy efficiency measures in the cement industry using

Equation E.1. Equation E.1 is developed based upon the approach described by Peter and

1 Worrell, E., and Galitsky, C. (2008). Energy efficiency improvement and cost saving opportunities for

cement making an Energy Star guide for energy and plant managers. Berkeley, CA: U.S.

Environmental Protection Agency, Environmental Energy Technology Division.
Andover Technology Partners Page 3

Timmerhaus.2The scale-up factors for the energy efficiency measures are grouped

according to their kiln types, wet, long dry, preheater, and precalciner. To obtain the

capital costs, the scale-up factors are to be multiplied with the production capacity of a

cement plant, expressed in tons of cement per year (TPY), raised to the six-tenth power:

Capital Cost ($2008) = Scale-Up Factors * Capacity0.6 E. 1

The scale-up factors were calculated primarily by the payback period method or,

by capital cost (in $/TPY of capacity) when capital cost information was provided by

Worrell and Galitsky with a base year of the cost. In the payback period method, capital

cost was back-calculated from the payback period using fuel and electricity savings

provided by Worrell and Galitsky.

Payback Period Method

To estimate capital cost, and express it in dollars per tons per year of cement

(TPY), recall that Capital cost (CC) divided by the value of the annual savings equal the

payback period (PP) in years. So, if the value of the annual savings (S) is estimated and

multiplied times the payback period it is possible to estimate the capital cost in tons per

year of capacity as.

CC = S * PP E. 2

Savings (expressed in $ per ton of cement) can be estimated by

Fuel Saving (MBtu/ton) * Cost of Fuel ($/MBtu) + Electric Savings

(kWh/ton) * Cost of Electricity ($/kWh) = Savings ($/ton) E. 3

Capital cost will equal

Plant Capacity (in TPYCEMENT) * PP * S E. 4

The payback period in years along with the energy savings (Btu/ton of cement

and kWh/ton of cement) are found in Tables 3 and 4 on pages 47-46 of Worrell and

Galitzky. The body of the Worrell and Galitzky report includes a method description

along with the base year of the estimate and, in a few cases, estimates of capital cost.

Energy costs were estimated from average retail price of delivered coal for

industrial users in the particular year and average cost of electricity for industrial users in

2

Peters, M.S., and Timmerhaus, K.D. (1991). Plant design and economics for chemical engineers (4th ed.)

New York: McGraw-Hill, p 169
Andover Technology Partners Page 4

the particular year of the estimate. The payback calculation yielded capital costs in

dollars per ton per year of cement capacity for the year that the energy savings or the

payback period was determined. Accounting for inflation through the application of a

GDP deflator allowed the scale-up factors to be adjusted to 2008$. The scale up factor

was determined by multiplying the average estimated cost (in $/TPY of cement capacity)

times the average capacity for the applicable type of kiln (wet, dry, preheater, etc.) raised

to the 0.40 power. The average capacity for the kiln type was based upon existing kiln

capacity data in the Cap+Trade workbook used for ISIS. Worrell and Galitzky provided

a table for energy saving methods applicable to wet kilns and another for dry kilns. So,

for most energy saving methods listed for dry kilns the average capacity of all dry

process kilns was used in formulating the scale up factor for dry process kilns. If a

process was applicable to only one type of dry process (ie., long dry), the average

capacity for that particular type of dry process was used in formulating the scale up

factor.

In some cases the body of Worrell and Galitsky provided information on capital

costs, and in these cases alternative capital cost factors were developed and are shown on

Table 1 for comparison. These are also in 2008 $. The Capital Cost Computations

section in the Appendices contains additional information regarding the derivation of the

factors.

There are some cases where the payback period approach was not employed for

technologies listed in Tables 3 and 4 of Worrell and Galitsky. The scale-up factors for

indirect firing for both wet and dry kilns and for raw meal blending for the dry kiln were

computed using reported figures of capital cost in $/ton of cement. In the case of indirect

firing, the scale-up factor was calculated using a single point datum of an actual plant that

installed the system in 1997. For raw meal blending, we used the retrofit cost of

$5.61/ton of cement. The value was calculated from the reported cost of $3.30/ton of raw

materials (Worrell & Galitsky, 2008, p. 22). A conversion factor of 1.7 was applied to

change the cost from $/ton of raw materials to $/ton of cement since this is the rough

ratio of raw materials to final Portland cement product. The Capital Cost Computations

in the Appendices have further details.
Andover Technology Partners Page 5

Table 1. Scale-Up Factors for Use with Equation E.1 Capital Cost Estimation of Energy Efficiency Measure in the Cement Industry for Capital Costs in 2008$

Energy Saving Method

From Payback Calculation From Reported Capital Cost

Capital Cost

Description

Wet

Process

Long Dry Preheater Precalci

ner

Wet

Process

Long

Dry

Preheate

r

Precalci

ner

Raw Materials Preparation

Efficient Transport System Minimum - 392 392 392 787 787 787

Raw Materials Blending Average - 1,181 1,181 1,181 1,181 1,181 1,181

Process Control Vertical Mill Average - 19 19 19

High Efficiency Roller Mill Minimum - 1,352 1,352 1,352 1,458 1,458 1,458

Slurry Blending and Homogenization Maximum 1,546 - - -

Wash Mills with Closed Circuit Classifier Minimum 1,136 - - -

High-Efficiency Classifiers Minimum 553 714 714 714 451 584 584 583

Clinker Making

Energy Management and Control System Average/Maximuma207 220 220 220

Seal Replacement Maximum 6 8 8 8

Combustion System Improvement Average 370 334 334 334 188 244 244 243

Indirect Firing Average 1,986 1,986 1,986 1394 1,802 1,802 1,802

Shell Heat Loss Reduction Average 66 88 88 88 47 60 61 60

Optimize Grate Cooler Average 48 78 78 78

Conversion to Grate Cooler Average 83 101 101 101 38 50 50 49

Heat Recovery for Power Generation Average - 604 - -

Conversion to Semi-Dry Process Kiln Minimum 2,455

Efficient Mill Drives Average 194 30 30 30

Finish Grinding

Energy Management and Process Control Maximum 16 20 20 20

Improved Grinding Media in Ball Mills Average 178 230 230 230

High Pressure Roller Press Minimum 1,515 1,958 1,958 1,958 903 1,166 1,166 1,166

High-Efficiency Classifiers Minimum 389 545 545 545 451 584 584 583

Plant Wide Measures

Preventative Maintenance Maximum 40 51 51 51

High Efficiency Motors Maximum 29 37 37 37

Adjustable Speed Drives Average 158 213 213 213 70 91 91 90

Optimization of Compressed Air Systems Maximum 86 44 44 44

Product Change

Blended Cement Maximum 294 294 294 294

Limestone Portland Cement Maximum 153 153 153 153

aAverage refers to the wet process; maximum refers to the dry processes.
Andover Technology Partners Page 6

The capital costs estimated from Equation E.1 are not necessarily the average

costs for all of the energy efficiency measures shown in Table 1. In some instances, the

capital costs reflect not the average, but the maximum or minimum investment required

based upon the payback period shown. Table 1 notes how each measure’s capital cost is

to be interpreted in the column “Capital Cost Description”. The variation in

interpretation is a result of how the payback period was expressed in Worrell and

Galitzky. The payback period for certain measures indicates either a minimum or a

maximum number of years necessary to pay off an investment. For example, installing

an efficient transport system in a dry process has a payback period greater than 10 years

(Worrell & Galitsky, 2008, p. 47). A scale-up factor based on this 10 year period will

yield the minimum capital requirement for the measure.

For some of the measures listed in Worrell & Galitsky, the scale-up factors are

not available. A few of the measures, such as fuel preparation and efficient lighting, do

not have the payback period necessary for computation of the capital costs. For others,

computations were not performed as cost equations have already been developed in other

reports.3 And, a few measures were applicable only to certain types of kilns.

One source of uncertainty in the data from Worrell & Galitsky is the year in

which the savings and the payback period were evaluated. Worrell & Galitsky did not

always report this consistently. Due to such inconsistencies, the year had to be estimated

in some cases. This has an impact since energy costs were evaluated in a base year and

capital costs were escalated using a GDP deflator. This may explain the differences

between the scale up factors developed from the energy savings payback period method

and those developed from the capital costs provided in Worrell and Galitzky.

A final comment regarding estimates using this data is that it is uncertain how

representative the energy savings and payback period data in Worrell and Galitsky are for

applicable kilns. As a result, when using the cost estimates developed here, it should be

understood that some situations may experience significantly different costs or energy

savings than estimated.

3 Memo J. Staudt to R. Srivatava, S. Vijay, E. Torres, Re: Cost and Performance of Controls – revised from

comments, dated March 10, 2009, pp 34-36
Andover Technology Partners Page 7

Oxygen Enrichment

An energy efficiency technology that is not addressed in Worrell and Galitzky is

oxygen enrichment. This technology has the potential to improve kiln output while

potentially reducing fuel input. This is made possible by the higher heat intensity of the

kiln and reduction of total gas flow for any given level of clinker production. Oxygen

enrichment will require additional electrical energy for the oxygen generation, or

alternatively, purchase of liquid oxygen. For oxygen enrichment, it is possible to use

vacuum swing absorption (VSA) with a molecular sieve rather than cryogenic air

separation because high purity oxygen is not necessary. This has the advantage of a low

pressure system and reduced electrical demand compared to cryogenic air separation.

The technology is limited by:

 Increased thermal load on kiln refractory. – the increased flame temperature will

cause refractory to deteriorate at an accelerated rate. While short term tests have been

performed to higher enrichment rates, it has been reported that the process is limited

to about 23%-25% oxygen concentration (versus 21% for normal air).4

 Increased NOx emissions from elevated flame temperature. – Elevated flame

temperature will elevate NOx emissions. TXI reported 50% increase in NOx

emissions from their wet kilns when using oxygen. But, oxygen lance position can be

optimized. So, this level of NOx increase may not be typical.

Four papers on oxygen enrichment in the US provide information on the

capabilities of oxygen enrichment as it is applied to cement kilns. The results and

citations are shown in Table 2. In each of these cases oxygen enrichment was performed

for the purpose of increasing the kiln capacity because at that time US cement demand far

exceeded domestic supply. In each case kiln capacity was increased. And, in one case a

decrease in fuel use was reported. The other three papers did not report a decrease in fuel

use. However, ECRA has reported that an estimated reduction of 100-200 MJ/tonne of

clinker is achieved with an increase in electric demand of 10-35 kwh/tonne of clinker.

4 European Cement Research Academy, “Development of State of the Art Techniques in Cement

Manufacturing: Trying to Look Ahead (CSI/ECRA Technical Papers), March 2009.
Andover Technology Partners Page 8

The 100-200 MJ/tonne of fuel savings is roughly consistent with the 3-5% fuel savings

reported by the Hercules plant.

Table 2. Oxygen Enrichment at four US cement plants

Plant Kiln

Type

Clinker

increase %

Marginal Clinker increase

Tons/ton O2

Fuel decrease

Midlothian Wet 6.5% Not Reported Not reported

Victorville PC 3.1% 3.5 t/t Not reported

Mojave PC 8.9% 4.4 Not reported

Hercules PH, PC 8-10% 4.7 3-5%

Sources:

Mayes, G., “Oxygen Enrichment at the TXI Midlothian Cement Plant”, Cement Industry Technical

Conference, IEEE/PCA Proceedings, 2001

Shafer, B., “Oxygen Enrichment at California Portland Cement’s Mojave Plant”, Cement Industry Technical

Conference, IEEE/PCA Proceedings, 2001

Gotro, C., “Oxygen Enrichment at CEMEX’s Victorville Plant”, Cement Industry Technical Conference,

IEEE/PCA Proceedings, 2001

Leger, C., Friday, J., “Oxygen Enrichment for Cement Kiln Firing”, Cement Industry Technical Conference,

IEEE/PCA Proceedings, 2001

At Cemex’s Victorville plant, higher total clinker increases were possible than

3.1% (estimated by 40 tpd of oxygen times 3.5 ton clinker/ton oxygen/4550 tpd of

clinker), but at a lower clinker production rate per unit of oxygen (tons of clinker per ton

of oxygen).

Cost of oxygen enrichment will depend upon whether or not a permanent system

is desired or if a temporary system is desired. Liquid oxygen can be delivered on site and

requires little equipment. However, this can cost about $0.30 per pound.5 A pressure

swing absorption system can provide oxygen for a price of $0.05 per pound. California

Portland Cement installed a 95 TPD VSA system at its Mojave plant and they reported

electrical demand of 0.01kwh/scf of oxygen (or 0.01kwh/0.089 lbm of oxygen, or 178

kwh per short ton of oxygen), and this cost (which reportedly represents virtually all of

the variable cost) is reported to be roughly 30% of the operating cost at the Mojave plant.

At 4.4 ton of additional clinker per ton of oxygen, his electrical demand of 178 kwh/short

ton of oxygen translates to about 40 kwh per short ton of clinker. ECRA reported an

5 Remedial and Natural Attenuation Services, Inc. http://www.rnasinc.com/o2zone/productdescriptiom/Cost_Of_Oxygen_Delivery, viewed July 3, 2009
Andover Technology Partners Page 9

electrical demand of 10-35 kwh/tonne of clinker; but, it is unclear whether this is per total

clinker or additional clinker from oxygen addition.

CAPITAL COST

According to the European Cement Research Academy, capital cost is estimated at

between 5 and 10 million euros for an oxygen enrichment system and an operational cost

ranging from 0.5 – 2.3 euros per tonne of clinker based on clinker capacity of 2 million

tonnes/year. Since the report is published in 2009, it is presumed that these are costs in

2009 euros. Using a conversion rate of $1.40/€ (July 3, 2009, Bloomberg), it is possible

to convert to dollars. Using the average cost of 7.5 million euros and a plant size of 2

million tonnes/yr, the cost can be shown to be $4.77/ton per year capacity.

Using the six tenths rule, the capital cost can be estimated to be:

(2 (10)6tonnes/yr x 1.1 ton/tonne)0,4 x 4.77 x TPYCLINKER 0.60

= 1642 * TPYCLINKER ^ 0.6 E.5

Fixed Operating Cost

Fixed operating cost can be assumed to be a fraction of capital cost. For the

moment, we will assume, consistent with many other technologies, $59,200/year plus 4%

of capital cost.

Variable Operating Cost

Since this is predominantly power, it can be estimated as roughly 40 kwh/short

ton of additional clinker times the cost of power, or COSTKWH*40*TPYCLINKER *

O2ENRCLINKER. O2ENRCLINKER is the fraction by which clinker output increases.

Impacts

Clinker output: The average clinker increase from Table 2 is 6.88%, making

O2ENRCLINKER = 0.0688.

Based on the ECRA estimates and the fact that the impacts are probably mainly

realized in the kiln, fuel impacts are estimated at about 0.1 million Btu/ton clinker heat

input reduction for precalciner kilns and about 0.2 million Btu/ton clinker heat input

reduction for other kilns.
Andover Technology Partners Page 10

CO2 from fuel will be reduced in direct proportion to the reduction of fuel as

discussed above. There will be no impact on CO2 from calcination except that increased

clinker output will increase the total CO2 from calcination in proportion to clinker output.

Total CO2 from fuel and from calcination per ton of clinker, however, will drop

somewhat due to the reduction in CO2 from fuel.

Electricity demand will increase by around 20 kwh per ton of clinker (using the

average of the ECRA estimates) or around 40 kwh per ton of clinker (using the data from

Mojave plant).

NOx emissions will increase. TXI reported a 50% increase from a wet kiln. It is

unclear how representative this is. Assuming about half of the fuel is burned in the

calciner in a precalciner kiln, assume about 25% increase in NOx for precalciner kilns

and 50% for others. Hopefully, we can find some data in the future that will give us a

better picture.

Negligible impacts are expected for other emissions.
Andover Technology Partners Page 11

Supplementary Cementitious Materials (SCMs)

Supplementary Cementitious Materials (SCMs) are materials that can be used as a

substitute for Portland cement and can be blended into concrete to reduce the amount of

Portland cement that is used. These materials are broadly divided into two types:

cementitious materials; and pozzolans, as shown in Table 3.

Table 3. Cements and Supplementary Cementitious Materials

Cementitious Pozzolanic (or materials with pozzolanic characteristics)

Hydraulic

Cements

Portland cement

Blended cement

Supplementary

cementitious

materials

GGBF Slag GGBF Slag

Class C fly ash Class C fly ash

Class F fly ash

Natural pozzolans (diatomite, calcined clay, calcined shale,

metakaolin), Silica fume

Portland cement clinker is a manufactured hydraulic cement – which is a cement

that sets and hardens by chemical interaction with water and is capable of doing so

underwater. Hydraulic cement can also be produced by blending Portland cement with

SCMs. For example, Ground Granulated Blast Furnace Slag (GGBF slag – also called

Slag Cement) is a cementitious material that can be blended to make hydraulic cement.

Hydraulic cement can also be produced by combination of pozzolans with material

providing calcium hydroxide. Some fly ashes have adequate lime present to be

cementitious.

A pozzolan, in general, is a material that when mixed with calcium hydroxide will

exhibit cementitious properties. Most natural pozzolans are vitreous siliceous materials,

and these are often found in nature in volcanic rock formations. In the United States

these natural pozzolans are found principally west of the Mississippi River. Clays

(especially kaolin) and Shale can also be pozzolanic when calcined. 6 When combined

with calcium hydroxide they form calcium silicates similar to Portland cement. Silica

fume is a man-made pozzolan from silicon production that has limited availability; but, it

6 Actually, kaolin and shale undergo dehydroxilization rather than a release of CO2 when heated, and this

process makes them pozzalonic. So, this is different than what happens when limestone is

calcined.
Andover Technology Partners Page 12

is useful for very high strength cements. Metakaolins are produced by dehydroxilization

of kaolin clays and are mainly used in white cement.

Fly ash from coal combustion is a man-made pozzolan, and it can be blended with

Portland Cement. In fact, it is the SCM that is used in the greatest amounts because it is

available in many locations at a reasonable in price. Fly ash also improves the properties

of the final concrete for some applications, particularly with regard to sulfate resistance

and strength. Some coal fly ashes are pozzolans that also contain sufficient CaO that they

are cementitious.

The advantage of SCMs is that they can be blended into concrete or cement and

reduce the need for Portland cement, and therefore their use can mitigate the

environmental impact of Portland cement manufacturing. The use of these materials will

be limited by availability and the type of end use product that is desired. Of the SCMs

shown, fly ash, blast furnace slag and natural pozzolan, are the most likely to end up in

the final concrete product. Cement manufacturers generally only blend a small amount of

SCMs into their cements to aid with flow or grinding. On the other hand, concrete

suppliers often blend far more significant amounts of SCMs into the concrete that they

supply. As a result, increased use of SCMs in the final concrete will reduce the amount

of manufactured Portland cement necessary to fill the same concrete demand. However,

each of these SCMs impacts the product properties, such that the amount of SCM used

will differ by how the concrete is expected to be used.

The chemical compositions of the three most widely used cement substitutes - fly

ash, blast furnace slag, and natural pozzolanic materials - as compared to clinker are

shown in the ternary phase diagram of Figure 1. The three main mineral constituents are

CaO, SiO2 and Al2O. 7 Control of these three constituents will impact the characteristics

of the cement. The close chemical resemblance of blast furnace slag to Portland cement

is why it is a very useful SCM that can be blended at high rates. Fly ash is not quite as

close in chemical composition to Portland cement as blast furnace slag, and for that

reason fly ash is usually not cementitious unless there is also high CaO present in the fly

7 Claude LOREA – CEMBUREAU, “USE OF ALTERNATIVE FUELS AND MATERIALS IN THE

EUROPEAN CEMENT INDUSTRY”, IEA meeting-4 Sept 06-Use of alternative fuels-CL
Andover Technology Partners Page 13

ash (such as class C fly ash). Also, this is why fly ash is generally blended at lower

concentrations than blast furnace slag.

Figure 1. Ternary diagram of mineral constituents in Portland cement

A term that is used to characterize the amount of SCM in a cement is the clinker

ratio (CR), which is the percent of cement produced that is composed of clinker. A CR

of 95% is 100% Portland cement (95% clinker and 5% gypsum). Table 4 shows the CR

for five different cement grades sold by Cembureau (the European Cement Association).

The five cement types referred to in this table are:

Type I: Portland Cement

Type II: Portland Composite Cement

Type III: Blastfurnace slag Cement

Type IV: Pozzolana Cement

Type V: Composite Cement

The different cement types are formulated for various strength, hardening,

hydration or sulfate resistance characteristics.
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Table 4. Five different cement grades sold by CEMBRAU 8

In the United States Portland cement substitutes are added in very limited

amounts by the cement companies. So, such statistics as those in Table 3 for different

cement types are not available for the United States. These substitutes tend to be added

by the concrete companies when blending the concrete. The reason the blending occurs

to a greater extent by concrete companies than cement companies is likely due to the

market structure in the US, making it less expensive for concrete companies to add these

materials that may be available from local markets. Also, concrete companies can blend

the concrete to meet the specific needs of their market.

8 Claude LOREA – CEMBUREAU, “USE OF ALTERNATIVE FUELS AND MATERIALS IN THE

EUROPEAN CEMENT INDUSTRY”, IEA meeting-4 Sept 06-Use of alternative fuels-CL
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Ground Granulated Blast Furnace Slag (or Slag Cement)

Blast furnace slag is the SCM that most closely resembles Portland cement. For

this reason ground, granulated blast furnace slag (also known as slag cement) is a

cementitious material that can be blended to high rates. Slag cement is a product of the

conversion of iron ore to iron in a blast furnace. It is produced by rapidly cooling the

blast furnace slag with water as it leaves the furnace to form granules. The slag granules

are then dried and ground to make slag cement. Slag cement is frequently substituted for

manufactured cement in ratios of about 50% by weight and potentially higher ratios,

depending upon the application. Growth in the use of slag cement has been nearly 12%

annually over the last ten years, with peak slag cement shipments of 3.6 million metric

tons in 2006. 9There was a slight drop off in 2007 as shown in Figure 2. This was

likely due to the drop off in construction that started in late 2007.

Figure 2. US Slag Cement Shipments

(source: Slag Cement Association)

Cost of Slag Cement – Ground granulated blast furnace slag is reported by USGS

to cost in the range up to about $100/ton (2008$), although prices for slags on average are

less.10 This may not factor in shipping costs, which in some cases might make slag less

9

Slag Cement Association,

http://www.slagcement.org/shared/custompage/custompage.jsp?_event=view&_id=445505_U128801__14

8636

10 http://minerals.usgs.gov/minerals/pubs/commodity/iron_&_steel_slag/mcs-2009-fesla.pdf
Andover Technology Partners Page 16

attractive or cause actual price to be less to offset transportation costs. USGS reported

costs for granulated (not necessarily ground granulated) blast furnace slag between $19

and $95, with the average around $80 in 2006.11 Since over 90% of the granulated slag

was reported by USGS to be cementitious (likely slag cement), the average is probably

representative for this application. As a by-product of iron production, blast furnace slag

availability will be impacted by the production levels of the iron industry. As a result, if

demand for iron produced from iron ore drops, slag availability could drop as well.

Benefits of Slag Cement – Use of slag cement will offset the environmental

impacts of the Portland cement it replaces on a one-for-one basis. That is to say that one

ton of slag cement should offset the environmental impacts (NOx, SO2, CO2, PM and

other emissions) of manufacturing one ton of Portland cement (roughly 0.95 ton of

clinker). Slag cement is blended into concrete at levels of around 50%.

11 USGS: 2006 Minerals Yearbook, Slag – Iron and Steel
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Fly Ash

Fly ash from coal fired power plants is, by far, the most widely used SCM. In the

United States fly ash is generally not blended into the cement at the cement plant, but

added to the concrete during the blending of the concrete. In other countries it may be

mixed with the cement. Two major classes of fly ash are specified in ASTM C 618 on

the basis of their chemical composition; these are designated Class F and Class C. Class F

is fly ash most often (but not always) produced from burning anthracite or bituminous

coal, and Class C is most often (but not always) produced from the burning of

subbituminous coal and lignite (as are found in some of the western states of the United

States). Class C fly ash usually has cementitious properties in addition to pozzolanic

properties due to available free lime, whereas Class F is rarely cementitious when mixed

with water alone. In most states fly ash as an SCM is limited to about 15%-25% of the

concrete, at least for highway purposes. Both fly ashes, if high in quality, are desirable

for blending into concrete due to strength and sulfate resistance characteristics. 12

Fly ash mineral oxide constituents for different fly ash types are compared to

those of Portland Cement and Slag Cement in Table 5. 13 As shown, Class C fly ash has

a much higher CaO content than Class F fly ash, which is why it often has cementitious

properties while type F generally does not. For any given fly ash, actual mineral matter

content could differ significantly from these values.

Table 5. Comparison of Portland Cement, Slag Cement, Class C Fly Ash

and Class F Fly Ash

Source: Slag Cement Association

12 US DOT Federal Highway Administration, www.fwha.dot.gov/infrastruction/materialsgrp/flyash.htm

13 Slag Cement Association, “Slag Cement And Fly Ash”, www.slagcement.org
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According to the American Coal Ash Association’s 2007 Coal Combustion

Product Production & Use Survey, of about 72 million short tons of ash produced, almost

14 million short tons (19.4%) were used in concrete and concrete products. Another 3.6

million short tons were used in blended cement or as raw feed for clinker production. Of

the total fly ash produced, about 32 million short tons (44.4% of the total) were somehow

beneficially reused. The balance – about 40 million short tons – was not beneficially

reused.

Although this suggests an ample supply of fly ash for increased use in the

concrete or cement industry, usefulness for any particular application is limited by:

 Quality and consistency – for concrete blending the fly ash must meet

fairly stringent quality specifications and have good consistency. The best

quality fly ash generally comes from base loaded power plants that can

closely control conditions. When used as a feed material for a kiln (either

as a fuel or a raw material) fly ash quality is less critical, but is not

unimportant.

 Local market factors – Fly ash is heavy and can only be economically

used in regional markets. As a result, in some regions there may be a

shortage of fly ash while in other regions there might be more than

adequate supply.

Costs of Fly Ash as an SCM - According to Robl, 14 in 1997 the price of high

quality fly ash pozzolan was in the range of $25-$30 per ton while displacing an

equivalent weight of cement at a cost of about $70-$80/ton (all in 1997$). Since these

prices are market based, they likely move in proportion to the price of cement. And, this

does not factor in the cost of transport. According to Robl, in 1997 the cost of

transporting fly ash by pneumatically-loaded trailer truck was roughly $0.10 to $0.13 per

ton per mile. It is likely that these transportation costs are similar for other heavy,

powdery materials, such as cement or ground limestone.

Benefits of Fly Ash as an SCM – Since the fly ash is blended into concrete and

offsets Portland cement, it should reduce the environmental impact of Portland cement

manufacture when used roughly on a one-to-one basis. That is to say that one ton of fly

ash should offset the environmental impacts (NOx, SO2, CO2, PM and other emissions)

14 Robl, T., McCormick, C., “We Are Running Out of Fly Ash: The Nature of Regional Supply Problems”,

3

rd Conference on Unburned Carbon in Fly Ash, Pittsburgh, PA, May 13-14, 1997
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of manufacturing up to one ton of Portland cement (roughly 0.95 ton of clinker and 0.05

ton of gypsum). At very high replacement rates where it may be necessary to add

additional CaO, some of the benefits may not be on a one-for-one basis. However, fly

ash blending rates are limited, particularly for highway use. Fly ash is blended into

concrete at levels that are limited up to around 15%-25% in the US. So, a reasonable

average to assume is up to 20%. This is a somewhat lower blending rate than in Europe,

as demonstrated in the data of Table 3.
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Natural Pozzolans

Natural pozzolans are only available in certain locations and are therefore not as

widely used as slag cement or fly ash. Diatomite is one pozzolanic material that is often

used as an SCM. According to USGS, 26% of the 654,000 tons of diatomite, or 170,000

tons, was used for cement applications in 2008. This compares to millions of tons of fly

ash or slag cement that is used. There are 7 diatomite-producing companies with 12

mining areas and 9 processing facilities in California, Nevada, Oregon, and Washington.

15 Other pozzolans include calcined clay and shales. Clay and shale are normally used

(and in large quantities) in the production of Portland cement since calcination will be

performed in the cement kiln. 16

Cost of Natural Pozzolans

Diatomite was reported to cost under $9.00 per ton (FOB plant). Clay and Shale

was reported to cost about $11/ton (FOB plant).17

Benefits of Natural Pozzolans

These materials are not cementitious, and therefore require CaO to be provided by

other materials that provide the CaO. However, the addition of pozzolans should

mitigate the use and impacts of Portland cement on a roughly one-to-one basis. That is to

say that one ton of pozzolan should offset the environmental impacts (NOx, SO2, CO2,

PM and other emissions) of manufacturing one ton of Portland cement (roughly 0.95 ton

of clinker). At very high replacement rates where it may be necessary to supplement

CaO, some of the benefits may not be on a one-for-one basis. Because of their limited

availability and far less use than fly ash or slag cement as an SCM, it may not be useful to

include natural pozzolans in the ISIS model.

15 USGS: http://minerals.usgs.gov/minerals/pubs/commodity/diatomite/mcs-2009-diato.pdf

16 USGS: http://minerals.usgs.gov/minerals/pubs/commodity/clays/myb1-2007-clays.pdf

17 USGS: http://minerals.usgs.gov/minerals/pubs/commodity/clays/mcs-2009-clays.pdf
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Decarbonated Kiln Feedstocks (DKFs)

Previous work described the use of steelmaking slag as a substitute or additional

material for input to a kiln to produce clinker. Steelmaking slag as a kiln feed reduces the

amount of limestone that must be calcined per ton of clinker produced. This helps to

reduce CO2 emissions per ton of clinker produced by the kiln. Other sources of calcium

that do not require calcining will provide similar benefits as those described for steel slag

when using the CemStar process.

Table 6 shows data collected from the USGS regarding the amounts of materials

that were used in producing clinker and in producing cement in 2005 and 2006. Of all of

the calcareous materials, all require calcination (lime is calcined limestone and marl is

made of clay and calcium carbonate). A number of the siliceous materials can also be a

source of calcium, such as coal ash or slag, and these are highlighted in yellow.

The Costs of Using Decarbonated Kiln Feedstocks - Use of decarbonated kiln

feedstocks requires a relatively modest capital cost that is primarily for material handling

equipment. This is discussed in the description of the CemStar process in a previous

memo.18 The cost of steel slag will vary, as shown in Table 7 which shows the costs of

iron and steel slag reported to USGS in 2005 and 2006. 19 Blast Furnace Slag used as

kiln feed is likely less expensive than Slag cement, and the costs of the air cooled blast

furnace slag and the lower end of the cost of granulated blast furnace slag are probably

most representative for this application.

18 Memo J. Staudt to R. Srivatava, S. Vijay, E. Torres, Re: Cost and Performance of Controls – revised

from comments, dated March 10, 2009, pp 34-36

19 USGS: 2006 Minerals Yearbook, Slag – Iron and Steel
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TABLE 6
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TABLE 7

The value of fly ash used as a kiln feed is likely to be significantly less than fly

ash that is blended directly into concrete. To estimate this value, power plant fly ash

revenue data was evaluated. Figure 3 shows a plot of the cumulative percent of plants

that reported fly ash revenue received (in $/ton) at or below a given price. This data was

derived from reported data from the EIA 767 reports for the year 2005 (no later data is

available since DOE changed their reporting requirements) and fuel data from EIA 423

for that year. The median reported fly ash value is about $5=$10/ton or less, regardless

of fuel type, and this is consistent with the estimated cost of slag for this purpose. So,

$5-$10/ton may be a reasonable assumption for kiln additives. As shown, there are a

significant number of facilities that are capable of selling their fly ash at prices well

above the median price of $10/ton. These plants are most likely selling their ash for

blending with concrete.

Use of decarbonated kiln feedstocks will also reduce heat input by reducing the

amount of energy necessary to calcine the feed materials. This should result in energy

savings per unit of clinker produced. How to estimate this will be described in the

following section.
Andover Technology Partners Page 24

Figure 3. Cumulative Percent (by number) of Plants Reporting in 2005 Fly Ash

Revenue Below Value

Developed from EIA Form 767

Benefits of Decarbonated Kiln Feedstocks - Based upon the CaO content of

decarbonated feedstocks, it is possible to estimate the avoided CO2 emissions when

comparing their equivalent CaO content to that of Clinker (the end product) and that of

Limestone. Table 7 shows the amount of CO2 emissions from calcination that is avoided

using these feedstocks. These were calculated based upon the CaO content shown. Table

7 also shows the estimated energy reductions. Having CaO present already will mitigate

energy demands that would be necessary to dissociate CaCO3 from limestone. The

energy associated with dissociation of CaCO3 to CaO is 2.45 million Btu/ton of CaO.20

The reduction in CO2 released from the reduced heat input will be determined by the

particular fuel used. For example, in the case of a kiln burning Rosemont PRB coal,

which produces 187 lbs CO2/million Btu21, use of one ton of steel slag as a kiln feed

20 Hanson, G., “Energy Efficiency Improvement Ideas & Projects for Lime Recovery Kiln Systems”,

Virginia-Carolina & Southeastern TAPPI Local Sections, February 17, 2005, Rock Hill, SC.

21 Memo J. Staudt to R. Srivatava, S. Vijay, E. Torres, Re: NOx, SO2 and CO2 emissions from Cement

Kilns, dated March 10, 2009, Table 3
Andover Technology Partners Page 25

would reduce CO2 from calcination by 0.51 tons and would reduce CO2 from fuel

burning by 297 lbs, or another 0.15 tons, for a total reduction of 0.66 tons. Since one ton

of steel slag is equivalent in CaO to one ton of clinker, these reductions of CO2 per ton of

steel slag will equal the reductions of CO2 per ton of clinker.

From the data in Table 6, it is estimated that 2.8% of total feedstocks to produce

clinker were fly ash in 2006. The limit on the use of fly ash use will be determined by

the mineral content of the fly ash relative to the clinker and the other available feedstocks

at the site. So, it is hard to characterize how high the fly ash addition rate could be

increased. But, the upper limit would likely be less than that of the blending rate for

concretes. So, perhaps up to 10% kiln feed may be used.

CO2 reduction (tons) = CAR * TONSDKF E.6

Where TONSDKF are the tons of the decarbonated kiln feedstock used and CAR

is the CO2 Avoidance Ratio from Table 8.

Table 8. Limestone Equivalence Ration, Clinker Equivalence Ratio, Calcination

CO2 Avoidance Ratio, and Heat Input Reduced for various kiln feedstocks

% CaO

Limestone

Equiv. Ratio1

Clinker Equiv.

Ratio2

Calcination CO2

Avoid Ratio3

Heat input

reduced4

Variable designation %CAO LER CER CAR HIR

Clinker 65% 1.16 1.00 NA NA

Blast Furnace Slag 45% 0.80 0.69 0.35 1.10

Steel Slag 65% 1.16 1.00 0.51 1.59

Class C Fly Ash 25% 0.45 0.38 0.20 0.61

Class F Fly Ash 3% 0.05 0.05 0.02 0.07

Limestone (as CaCO3) 56% 1.00 0.86 0 0

1. tons of limestone (as CaCO3) that provide same CaO as ton of material

2. equiv. ton clinker produced per ton material used based on CaO equivalence

3. Tons of released calcined CO2 avoided per ton of material used in lieu of limestone

4. Million Btu reduced heat input for each ton of material used in lieu of limestone

In a previous memo a parameter called CASUB was used to estimate the amount

of CO2 reduced by DKFs. 22 CASUB can be estimated by dividing the limestone

equivalent of the substituted material divided by the total equivalent limestone (substitute

plus limestone added) to equal:

22 See equation E.4 of Memo J. Staudt to R. Srivatava, S. Vijay, E. Torres, Re: NOx, SO2 and CO2

emissions from Cement Kilns, dated March 10, 2009,
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CASUB = LER*TONSDKF/

[(LER*TONSDKF) +TONSLIMESTONE* PURLIMESTONE] E. 7

Where LER is the limestone equivalent ratio from Table 8, PURLIMESTONE is

the purity of the limestone as CaCO3, TONSLIMESTONE is the tons of limestone used.

Thie analysis in the previous memo regarding addition of steel slag, or

CEMSTAR,23 did not include the effect of energy savings on CO2 emissions or on energy

costs. CO2 emissions reduction can be determined by:

CO2 avoided from fuel (lbs per ton clinker) =

CASUB*(HIR / CER) * LBCO2MMBTU E.8

Tons CO2 avoided per ton of clinker (calcination and fuel) =

CASUB*[(HIR / 2000*CER) * LBCO2MMBTU + 0.51 * MCLINKER] E.9

Where from Equation E.6,

CASUB = LER*TONSDKF/[(LER*TONSDKF) +TONSLIMESTONE*

PURLIMESTONE]

CO2TOTAL (in short tons) =

MCLINKER*[(LBCO2MMBTU/2000)*(SFC- CASUB*(HIR / CER))

+0.51* (1–CASUB)] E.10

Fuel Cost Reduction – This was previously mentioned. This can be estimated by

Fuel Cost (in $/million Btu) * Heat input reduction (million Btu per ton clinker) = Fuel

Cost (in $/million Btu) CASUB*(HIR / CER) E.11

The value of the fuel savings is determined by multiplying the results of equation

E.10 by the cost of fuel. For example, there will be a fuel cost reduction as a result In the

case of the steel slag, the value of energy savings per unit of clinker produced will be the

CAR * 1.59 million Btu/ton of material *(1.0 ton of material per ton of clinker

produced)* the fuel cost in $/million Btu.

23 Memo J. Staudt to R. Srivatava, S. Vijay, E. Torres, Re: NOx, SO2 and CO2 emissions from Cement

Kilns, dated March 10, 2009. Also note corrections to some equations in this memo that are included in the

Appendices
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Carbon Capture and Sequestration

Carbon capture at a cement plant has not been performed. IEA has performed a

study of carbon capture, primarily focusing on new plants24. This is the most recent and

comprehensive study that I am aware of. The European Cement Research Academy

(ECRA) also undertook a study.25 The two technologies that were examined in greatest

detail are:

 Oxycombustion

 Solvent capture and stripping (Amine-based)

The Oxycombustion approach results in a concentrated CO2 exhaust stream from

the kiln that can be compressed and sequestered. In oxycombustion, nitrogen is removed

from the combustion air that is introduced to the kiln/precalciner with an air separation

unit. This leaves a stream that is high in CO2 after combustion and calcination. To avoid

overheating of materials, CO2 is recirculated to maintain a similar total volume of gas as

in a normal kiln. Because oxycombustion represents a major process modification to the

cement-making process, and it is unclear if the calcination reactions will be effective at

the same temperatures under a high CO2 partial pressure, the IEA study determined that

oxycombustion is best applied to new facilities.

Solvent capture and stripping entails removal of CO2 from the exhaust gas stream,

and re-release in a concentrated form that is subsequently compressed for storage.

Solvent capture, being an “end of pipe” technology, could potentially be retrofit to

existing facilities as well applied to new facilities. Solvents include monoethanolamine

(MEA), MEA with additives to improve performance, and related amine chemicals. The

solvent capture process requires extremely low SO2 and NOx concentrations to avoid

high solvent attrition. So, scrubbers and SCR may be necessary, and are assumed to be

so in the study by IEA. Further, solvent capture and stripping requires substantial steam

requirements to heat the solvent for release of CO2 and regeneration of the solvent. So,

24 IEA Greenhous Gas R&D Programme, “CO2 Capture in the Cement Industry”, Technical Study, Report

Number 2008/3, July 2008.

25 European Cement Research Academy, Technical Report TR 044/2007, “Carbon Capture Technology -

Options and Potentials for the Cement Industry”. 3 July 2007
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the EIA study assumed that a small combined heat and power (CHP) plant would have to

be provided on site.

Other technologies are under development and may offer advantages to these two

technologies once they are at a more mature state. However, at this point other

technologies are at development stages that are too early to develop reasonable cost and

performance estimates.

The EIA study estimated the capital cost, fixed operating cost, and variable

operating cost of a one million metric ton (tonnes) per year cement plant (910,000 tonnes

per year of clinker) three ways: 1) without carbon capture and storage; 2) with

oxycombustion carbon capture and storage; and 3) with solvent-based carbon capture and

storage. For these three plant conditions, the costs (in million € or €/year) are shown in

Table 9. Based upon these estimates, it is also possible to assume a cost of retrofit of a

Solvent CCS system to an existing one million tonne per year cement plant assuming that

there is a retrofit difficulty factor of 1.3 to account for the additional difficulties

associated with retrofitting an existing facility. This retrofit difficulty factor is typical of

what is used for power plant flue gas desulfurization systems.26 Because CCS has never

actually been applied to a cement kiln, these costs should all be regarded as preliminary.

Table 9. Costs of CCS Cement Plants (all in 2008 €)

Cost/Emissions New Cement Plant Total Cost Retrofit Cost

Base Plant Oxycomb. CCS Solvent CCS Solvent CCS

Capital cost

Million €

263 327 558 383.5

Fixed Operating Cost

Million €/year

19.1 22.8 35.3 16.2

Variable Operating Cost

Million €/year

16.8 21.9 30.9 14.1

CO2 Emitted, tonnes/y 1,256,158 282,853 188,424 188,424

CO2 Captured, tonnes/y 0 465,014 1,067,734 1,067,734

Percent Capture 0% 62% 85% 85%

Percent below Base 0% 73.5% 85% 85%

26 Staudt, J., Khan, S., “Updating Capital Cost of SO2 Control Technologies in the Integrated Planning

Model and the Coal Utility Environmental Cost Model”, EPRI-DOE-EPA Combined Utility Power Plant

Air Pollution Control Symposium, The MEGA Symposium, Baltimore, MD, August 28-31, 2006
Andover Technology Partners Page 29

We can develop algorithms from these estimated costs to estimate the cost of CCS

on cement kilns of various sizes. As noted above, the costs are preliminary. So, any

algorithms developed from these costs should be regarded as preliminary as well.

Since these costs are represented in € and are for plants sized in metric tonnes of

cement, we need make some adjustments. Data on page 5 of the EIA report shows a $/€

conversion rate of $161=€107. The costs are for a 910,000 tonne per year of clinker

plant, which is nearly exactly equal to a one million short ton per year of clinker plant.

Table 10 is derived by converting the values shown in Table 9.

Table 10. Costs CCS Cement Plants Based on IEA Study

(2008$ assuming $1.5=€1.0, one million tpy clinker)

variable cost

New Cement Plant Total Cost of

Plant and CCS

Retrofit Cost

CCS only

Base

Plant

Oxycomb.

CCS

Solvent

CCS

Solvent CCS

CAPCO2 Capital cost

Million $

$396 $492 $840 $577

FOCCO2 Fixed Operating Cost

Million $/year

$29 $34 $53 $24

VOCCO2 Variable Operating

Cost

Million $/year

$25 $33 $47 $21

The cost of the base plant does seem high when compared to costs from other

sources and described in a previous memo. 27 So, simply applying a conversion rate to

the cost reported in euros in the IEA study appears to overstate the cost in dollars. For

example, Essroc announced plans to spend $400 million to build a 1.8 million tpy cement

plant (probably about1.6-1.7 million tpy of clinker for about $233/tpy of clinker) in

Virginia28, or under 60% of what is indicated in the data in Table 10. The apparently

high capital cost shown in Table 10 is possibly because the use of the exchange rate to

convert the costs in Table 9 from euros to dollars assumes purchasing power parity

between the costs of cement plants in the EU and the US, and this likely is not actually

true in the case of cement plants because they are not goods that can be traded between

27 Memo J. Staudt to R. Srivatava, S. Vijay, E. Torres, Re: Cost and Performance of Controls – revised

from comments, dated March 10, 2009, pp 34-36

28 Umstead, M., “Cement plant expansion to cost about $400 million”, The Herald Mail, January 25, 2008;

http://www.herald-mail.com/?cmd=displaystory&story_id=184298&format=html
Andover Technology Partners Page 30

countries cheaply or easily. As a result, the exchange rate between euros and dollars at a

point in time may result in incorrect translation of cost from euros to US $ because the

exchange rate changes very quickly – far faster than the actual price of the cement plant

equipment. So, it may be necessary to revisit these numbers– scale them down perhaps.

But, that is something that should be discussed.

Capital Costs

In any event, capital cost algorithms can be developed in the form of scaling

capacity for a given kiln to the 0.6 power. For new plants, this is straightforward since

the algorithms were based on new plants. Using the capital costs from the above tables

(in million $), designated CAPCO2, the capital cost can be determined by the following:

Capital cost (in million $) = CAPCO2*(1,000,000)^0.4*(TPYCLINKER)^0.6

Capital cost (in million $) = CAPCO2*251*(TPYCLINKER)^0.6 E.12

Where TPYCLINKER is the rated capacity of the kiln in short tons per year.

For retrofit plants, which will only apply to solvent technology, like a wet FGD

system the capital cost is most likely proportional to the gas flow being treated, which

will differ by kiln type for any given kiln capacity. The simplest approach would be to

scale costs to the EGFW. 29 The costs on Table __ that are based on the IEA study

presume a modern precalciner kiln. So the costs should roughly be:

Capital cost (in million $) = CAPCO2*251*(TPYCLINKER*EGFW/44878)^0.6

Capital cost (in million $) = CAPCO2*0.406*(TPYCLINKER*EGFW)^0.6 E.13

Fixed Operating Costs

Fixed operating cost can be scaled in much the same way. For any new kilns,

Fixed Operating Cost (in million $ per year) =

FOCCO2*251*(TPYCLINKER)^0.6 E.14

For retrofit of Solvent technology on an existing kiln,

Fixed Operating Cost (in million $ per year =

FOCCO2*0.406*(TPYCLINKER*EGFW)^0.6 E.15

29 Memo J. Staudt to R. Srivatava, S. Vijay, E. Torres, Re: NOx, SO2 and CO2 emissions from Cement

Kilns, dated March 10, 2009, Table 1
Andover Technology Partners Page 31

Variable Operating Cost

Variable operating cost for oxycombustion should probably be scaled in direct

proportion to the size of the kiln. Since oxycombustion is assumed to be applicable only

to new kilns, kiln type is not likely to differ, and VOC for oxycombustion should be

directly proportional to the size of the kiln.

For Oxycombustion:

VOC (in million $ per year) = VOCCO2 * TPYCLINKER/1,000,000 E.16

For Solvent CCS:

For a new kiln, the total VOC for the plant when a kiln is equipped with solventbased CCS can also be regarded as proportional to the size of the kiln, and Equation E.15

can be used.

For a retrofit of the solvent CCS, another approach is needed for estimating the

VOC associated with the CCS. For the Solvent technology, it is reasonable to assume

that the variable operating costs are roughly in proportion to the CO2 levels being treated.

Recalling from a previous memo:30

CO2FUEL (in Short tons) = (LBCO2MMBTU/2000) * SFC * MCLINKER

And,

CO2CLINKER (in Short tons) = 0.51 * MCLINKER * (1 – CASUB)

So that the annual tpy of uncontrolled CO2 emissions are:

CO2UNCYR =

TPYCLINKER * [LBCO2MMBTU/2000) * SFC + 0.51 * (1 – CASUB)] E.17

With CO2UNCYR expressed in short tons per year. VOC for Solvent capture

should be roughly in proportion to CO2UNCYR. So, that

Variable Operating Costs (in million $/year) = VOCCO2 * CO2UNCYR/(1,256,158*1.1)

Variable Operating Costs (in million $/year) =

VOCCO2 * CO2UNCYR/(1,381,774) E.18

30 Memo J. Staudt to R. Srivatava, S. Vijay, E. Torres, Re: NOx, SO2 and CO2 emissions from Cement

Kilns, dated March 10, 2009,
Andover Technology Partners Page 32

Other effects

Both of the carbon capture approaches have other impacts on emissions and

resource or energy consumption. The cost impacts of the resource or energy

consumption are incorporated into the costs described earlier.

Oxycombustion Impacts –

Oxycombustion requires a substantial increase in electricity consumption over the

base new cement plant. The base cement plant consumes about 80,809 MWh per year

(80.8 KWh/ton of clinker) as compared to about 174,562 million MWh per year (174.6

KWh/ton of clinker) for the oxycombustion plant – or more than doubling of electric

load.

Solvent CCS Impacts –

For solvent CCS systems, the addition of the combined heat and power plant

increases coal usage but results in less net electricity demand due to the on-site

generation.

Table 11 shows the resource use for cement kilns using different carbon capture

methods from the IEA study. For cooling water, seawater used for cooling was combined

with other cooling water.

Other emission impacts

NOx and SO2 emissions are near zero for each of the CO2 capture methods. NOx

and SO2 must be very low for solvent-based systems to work effectively. Therefore,

upstream NOx and SO2 removal – which are incorporated into the cost of the solvent

CCS system - reduce the concentration of NOx and SO2 to very low levels. For

oxycombustion, the NOx and SO2 are condensed into acids during compression of the

CO2-rich exhaust gas.
Andover Technology Partners Page 33

Table 11. Resource Usage of Cement Kiln

(one million tpy clinker)

variable Resource

New Plant Retrofit

CCS only

Base

Plant

Oxycomb.

CCS

Solvent

CCS

Solvent CCS

COALPY Coal or Coke (tpy) 105,000 109,000 356,000 251,000

ELECPY Electricity

(MWh per yr)

80,809 174,562 -22,734 -103,543

H2OPY Process Water

(tpy)

264,000 - 429,000 165,000

H2OCOOLPY Cooling Water

(million tpy)

- 14.3 4.82 4.82

All mass units in short tons, converted from data in EIA Study

Power for solvent CCS is net of cement plant and combined heat and power plant
Andover Technology Partners Page 34

Appendices
Andover Technology Partners Page 35

Capital Cost Algorithmns

The following elucidates the derivation of the scale-up factors used for the

calculation of capital costs for energy saving methods.

Equation E.1

Capital Cost ($2008) = Scale-Up Factors * Capacity0.6 (A1)

is based on the sixth-tenth factor rule for computing capital costs. This rule

appears as follows (Peter & Timmerhaus, 1991, p. 169):

Cost of Equip. a = Cost of Equip. b * (Capac. Equip. a / Capac. Equip. b)0.6 (A2)

where Cost of Equip. a = Cost of Equipment a

Cost of Equip. b = Cost of Equipment b

Capac. Equip. a = Capacity of Equipment a

Capac. Equip. b = Capacity of Equipment b

The scale-up factor is defined as:

Scale-up Factor = Cost of Equip. b / (Capac. Equip. b)0.6 (A3)

This can alternatively be determined by:

Scale-up Factor = ($/annual capacity) * (Base capacity)^0.4 (A4)

Where the Base Capacity is equal to the capacity level that is the basis of the $/annual

capacity estimate.

As indicated in Equation A3, two variables are essential to the calculation of the scale-up

factor: the cost of a base equipment, which is shown as Cost of Equip. b, and the capacity

of this equipment. For the determination of the scale-up factor in Equation 1, the base

capacity is the median or the average capacity of cement kilns in the U.S. These

capacities are organized by kiln types as follows:

Kiln Type Unit Average Capacity

Wet Tons per year 338,740

Long Dry Tons per year 310,064

Preheater Tons per year 498,301

Precalciner Tons per year 1,101,045

All Dry Tons per year 643,039

All Kilns Tons per year 559,440

Note: Determined from Cap+Trade Workbook
Andover Technology Partners Page 36

To calculate the Cost of Equip. b, this project obtains the capital cost of the

equipment in $/ton of cement and multiplies it with the base capacity. To find the capital

cost ($/ton of cement), the project relies on two methods. The primary method is to use

the payback period. The capital cost can be computed from the payback period as shown:

Capital Cost = Payback Period X Total Savings (A5)

where

Capital Cost = $/ton of cement

Payback Period = Time it takes to pay off an investment, year

Total Savings = Fuel Saving + Electricity Saving, $/( ton of cement *

year)

As noted in the Summary section, the payback period along with the savings of fuel and

electricity consumptions are contained in Worrell & Galitsky. The document, however,

expressed the savings for fuel as MMBtu/ton of cement and for electricity as kWhr/ton of

cement (Worrell & Galitsky, 2008, pp. 47-48). Since it is $/ton of cement that is

required, both the fuel and electricity savings have to be adjusted. The fuel was assumed

to be coal. So, the fuel savings were multiplied with the price of coal ($/short ton), its

high heating value (Btu/lb), and various conversion factors to obtain $/ton of cement:

Fuel Savings = Fuel Savingso * Price of Fuel (A6)

(HHV of Fuel) * CF1 * CF2

where

Fuel Savings = Savings in coal consumption, $/ton of cement

Fuel Savingso = Savings in coal consumption, MMBtu/ton of

cement

Price of Fuel = Price of Coal, $/ton of coal

HHV of Fuel = High Heating Value of Coal, Btu/lb

CF1 = Conversion factor1, 2000 lb/ton

CF2 = Conversion factor2, MMBtu/106 Btu

The high heating value (HHV) of coal, Btu/lb, was calculated by multiplying the

average HHV of two coal ranks, bituminous and sub-bituminous coals, with their

proportion:

Average Coal HHV =

Σ [(Average HHV of Coal Rank) * (Proportion of Coal Rank)] (A7) 
Andover Technology Partners Page 37

The HHV of the individual coal rank was obtained by averaging its range. For

bituminous coal, the HHV range is 10,500 – 14,000 Btu/lb; for sub-bituminous coal, it is

8,300 – 11, 500 Btu/lb (Kitto & Stultz, 2005, p. 9-6). The proportion of each coal rank

was assumed to be 50% each. As a result of Equation A7, the average coal HHV was

determined to be 11,075 Btu/lb.

The price of coal used is the average delivered coal price to other industrial users.

They are contained in the Coal Annual Report generated by the EIA and in the website

http://www.eia.doe.gov/cneaf/coal/page/acr/backissues.html.

For electricity consumption, to change the savings from kWhr/ton of cement to

$/ton of cement, the former was multiplied with the price of electricity ($/kWhr):

Electricity Saving = Electricity Savingo * Price of electricity (A8)

where

Electricity Saving = Saving in electricity, $/ton of cement

Electricity Savingo = Saving in electricity, kWhr/ton of cement

Price of electricity = Retail price to industrial users, $/kWhr

The price of electricity is the average retail price to industrial users and is found

on the EIA website in two separate tables. One table has the average price of electricity

from 1940 to 2007. (EIA, n.d.). For the latest 2008 data, the project consulted another

table (EIA, 2009).

Note that the prices of electricity and fuel correspond to the year in which the

savings or the payback period was determined. The time period, however, cannot always

be gauged precisely. For some of the measures, it had to be estimated or assumed. This

is because the source document did not always specify a time period for the savings and

the payback period. When dates are missing for all three variables, the project used the

publication year of the source document 2008 for its calculation. In cases where the date

of only one variable is known (i.e. the date of the electricity saving is given, but not for

the fuel savings or the payback period), this one available date is utilized. Furthermore,

the project assumed that the date is applicable to the other two variables as well. In some

instances, there is a surplus of dates. Various years of publication can be given for a
Andover Technology Partners Page 38

single variable. In this situation, the project chose the latest date. The same approach is

adopted when the dates of the variables are not compatible with one another.

Before multiplying the total savings with the payback period in Equation A4, the

project also estimated the value of the individual measure’s savings. The source

document expressed the fuel savings as either an average value or a range of values. If

the savings appeared as a range, the project averaged them. For the payback period,

Worrell & Galitzky listed it in one of four ways: (a) an average, (b) a range (c) a

minimum or (d) a maximum. The project treated the payback period range the same way

as it did the savings. With the minimum and maximum payback period, the project used

them as the minimum or maximum floors for calculations.

As a result of the variation in the expression of the payback period, there is no

consistent interpretation of the capital cost in this project. The interpretation varies

depending on how the cost was calculated from the payback period. If the maximum

payback period was utilized, the capital cost would be the maximum required and

conversely, if the minimum was utilized, the capital cost would be the minimum required.

Besides averaging the data ranges, the project also modified the high and low

values of one energy efficiency measure. According to the source document, the

electricity savings on the process control of a vertical mill was 0.8 – 1 kWhr/ton of raw

material (Worrell & Galitsky, 2008, p. 22). This same figure, however, was also used to

reflect the electricity savings in kWhr/ton of cement (p. 47). To convert the savings to

ton of cement, this project utilized a factor of 1.7. This led to a saving of 1.4-1.7

kWhr/ton of cement.

The factor of 1.7 was derived from the basis that one ton (750 grams) of clinker

can be obtained from 1.53 tons (1150 grams) of dry raw materials (European

Commission, 2001, p. 22). This basis excluded gypsum and moisture. Accounting for

gypsum, which made up about 5% of the cement product, the dry raw material usage

increased to 1.61 tons per ton of cement. After moisture, which was assumed to

constitute approximately 5% of raw materials, was incorporated, the final raw material

requirement grew to roughly 1.7 tons per ton of cement.
Andover Technology Partners Page 39

The application of the 1.7 factor is consistent with how the authors of the source

document treated the savings of other measures of dry processes that are expressed in

kWh/ton of raw materials. As shown in Table A.2, the electricity savings of these

measures increase by a factor of 1.7 when their unit was changed from $/ton of raw

materials to $/ton of cement.

Table A.2

Differences between Values Reported in $/tons of raw materials and $/ton of cement for

Dry Processes

Efficiency

Measure

Reported

Value

Unit Table

Value

Unit

Efficient

Transport

System

1.9 $/tons of raw

materials

3.2 $/ton of cement

Raw Meal

Blending

0.9-2.3 $/tons of raw

materials

1.5-3.9 $/ton of cement

High Efficiency

Roller Mill

6-7 $/tons of raw

materials

10.2-11.9 $/ton of cement

High Efficiency

Classifiers

2.5-3.4 $/tons of raw

materials

4.3-5.8 $/ton of cement

Note: Both the reported and the table values are from Energy Efficiency Improvement and Cost Saving Opportunities

for Cement Making: An Energy Star Guide for Energy and Plant Managers (pp. 21-23, 47-48) by Ernst Worrell and

Christina Galitsky, 2008, Berkeley, California: U.S. Environmental Protection Agency, Environmental Energy

Technology Division. The energy efficiency measures pertain to raw materials preparation.

The resulting capital cost from Equation A5 is multiplied with the average

capacity of the kilns for both dry and wet processes to generate the cost of the base

equipment:

Cost of Equip. b = Capital Cost ($/ton of cement) * (Capac. of Equip. b) (A9)

where

Cost of Equip. b = Cost of the base equipment, $

Capital Cost = Cost of capital, $/ton of cement

Capac. of Equip. b = Median or average kiln capacity (Table 5), tons per year
Andover Technology Partners Page 40

Once the Cost of Equip. b is known, the scale-up factor can be determined using

Equation A3. The equation, however, will only generate a scale-up factor that is valid for

the time period specified by the savings and/or the payback period. To obtain current

2008 value for the capital outlay, a GDP deflator is incorporated into Equation A3 as

follows:

Scale-up Factor = [Cost of Equip. b / (Capac. Equip. b)0.6 ] * GDP Def.2008 (A10)

GDP Def.Payback/Savings

where

Scale-up Factor = Calculated factor to determine capital cost, $2008/ton of cement

Cost of Equip. b = Cost of base equipment, $

Capac. Equip. b = Average or median kiln capacity, tons per year

GDP Def.2008 = GDP Deflator for the year 2008

GDP Def.Payback/Savings = GDP Deflator of the year payback period or savings were

estimated

The website of the Bureau of Economic Analysis (2009, Table) supplied the data

for the GDP deflator.

The second method for computing capital cost is to directly apply the reported

value of the installation cost ($/ton of cement) from the source document. This was done

only if the payback approach was not feasible. Two scale-up factors were obtained in

this manner. The first was based on a single system installation in California. A plant in

Colton that installed the indirect firing system in 1997 furnished the retrofit cost of $5

million per 680,000 tonnes (Worrell & Galitsky, 2008, p. 26). This translates to a capital

investment of $6.67/ton of cement.

The second pertains to the cost of the raw meal blending measure, which the

source document indicated to be $3.30/ton of raw materials. Since the scale-up factor

requires ton of cement instead of raw materials, the conversion factor of 1.7 was applied.

This generated a value of $5.61/ton of cement. As mentioned previously in this section,

using the conversion factor is an approach consistent with how the authors, Worrell &
Andover Technology Partners Page 41

Galitsky, treated the installed cost of the Raw Material Preparation efficiency measures

of the dry processes.

A number of the measures do not have scale-up factors. For a few of those

measures, such as Fuel Preparation: Roller Mills, and Efficient Lighting, the lack of data,

especially the payback period and the capital cost/ton of cement, prevented the

development of the factors. The project also did not attempt to make any computations

when more precise algorithms are available. For example, the measures of conversion of

one form of kiln into another and the use of secondary fuel, can be obtained from my

previous memo (see footnote 3) to the EPA (2009, pp.6-9, 34-43). And finally, not all

the measures in Worrell & Galitsky are applicable to all the kiln types. Those listed on

Table 1 under the Raw Meal Preparation, for instance, are divided into wet and dry types.

The first four measures pertain to dry processes, and the next two are unique to wet kilns.

.
Andover Technology Partners Page 42

Corrections to equations in Revised Memo.

I discovered errors on equations in the preciously revised memo that were

introduced when I included the effects of the purity of the limestone into some of the

equations. However, in some of the equations where I introduced the purity effects, this

was not correct. The correct equations for the “Cost and Performance of Controls –

revised from comments, dated March 10, 2009 are:

CO2CLINKER (in Short tons)= 0.51 * MCLINKER (in short tons) E.3

And, this can be modified to address substitutes for limestone as follows:

CO2CLINKER (in Short tons)= 0.51 * MCLINKER * (1 – CASUB) E.4

CO2TOTAL (in short tons) =

MCLINKER*[(LBCO2MMBTU/2000)*SFC+0.51* (1–CASUB)] E.11
Andover Technology Partners Page 43

List of Terms

Term Definition

CAPACITYHR

CAP CO2

CAR

CASUB

CC

CER

CO2FUEL

CO2CLINKER

CO2UNCYR

COALPY

COSTKWH

COSTNH3LB

COSTNH3YR

CR

DLI

DSCFM

EGFW

ELECPY

FC

FFi

FOCCO2

H

H2OPY

H2OCOOLPY

HIR

LBCO2MMBTU

LBMCO2MMBTU

LBH20MMBTU

LBMH20MMBTU

LBNOXHR

LBSO2CLINKER

LER

LNB

LWS

MAT

MCLINKER

MCLINKERHR

MKF

MLIMESTONE

MLIMESTONEHR

MSLAG

MSLAGHR

NOXMMBTU

PEC

PP

PURLIMESTONE

Q

S

Clinker capacity of the kiln in tons per hour

Capital Cost Factor for CO2 Control Tech

Calcination CO2 Avoidance Ratio

Calcium Substitution Fraction – fraction of total calcium from material that

does not need to be calcined

Capital Cost

Clinker Equivalence Ratio

Fuel CO2 in pounds that is released from fuel combustion

CO2 produced by calcination during clinker production

Uncontrolled CO2 per year

Coal used per year (in tons)

Cost of Power in $/KWhr

Cost of ammonia in $ per pound

Cost of ammonia in $ per year

CR

Dry Lime Injection

Dry standard cubic feet per minute

Exhaust Gas Flowrate (wet) in standard cubic feet per short ton of clinker

Electricity Used per Year (MWhr)

Fixed Cost

Fuel Fraction of ith component of fuel on a Btu basis

Fixed Operating Cost Factor for CO2 Capture Technology

Burner heat duty in MM Btu/hr

Process Water used per year (tons)

Cooling Water used per year (tons)

Heat Input Reduced

Pounds (mass) of CO2 per MMBtu of fuel input

Pound moles of CO2 per MMBtu of fuel input

Pounds (mass) of H2O per MMBtu of fuel

Pound moles of H2O per MMBtu of fuel

Pounds (mass) of NOx per hour

Lbs of SO2 per ton of clinker

Limestone Equivalence Ratio

Low NOx Burner

Limestone Wet Scrubber

Mixing Air Technology

Mass of clinker in tons

Mass rate of clinker in tons per hour

Mid Kiln Firing

Mass of limestone, tons

Mass rate of limestone, tons/hr

Mass of slag, tons

Mass rate of slag, tons/hr

NOx lb/MMBtu for the kiln

Purchased Equipment Cost

Payback Period

Purity of the limestone at CaCO3

Flow in actual cubic meters per hour

Savings (energy)
Andover Technology Partners Page 44

SCC

SCR

SFC

SCFH

SNCR

TDFPC

TONSDKF

TPY

TPYCEMENT

VC

VOCCO2

Staged Combustion Calciner

Selective Catalytic Reduction

Specific Fuel Consumption in MMBtu per ton of clinker

Standard Cubic Feet per Hour of gas

Selective Non-Catalytic Reduction

Tire Derived Fuel Precalciner

Tons of Decarbonated Feedstock

Tons per Year of clinker

Tons per Year of Cement

Variable Cost

Variable Operating Cost of CO2 capture technology














































































RTI International is a trade name of Research Triangle Institute

TO: Elineth Torres, U.S. EPA

FROM: Brooks Depro

DATE: March 31, 2010

SUBJECT: ISIS Cement Production Costs

This memorandum documents the data and methods used to develop unit costs ($/short ton) for

raw materials, repair and maintenance, electricity use, and labor. This documentation

accompanies the XL spreadsheet entitled “ISIS_cement_production_costs.xlsx.”

Raw Materials

In Table 1, we report the quantities of raw material inputs, cement production, and the raw

material input ratio for the U.S. cement industry. In 2005, approximately 1.67 metric tons of raw

materials were required to make a short ton of clinker. State-level material prices (approximated

by USGS data for crushed stone) range between $4 and $13 per metric ton (Table 2). To

calculate raw material costs, we multiplied state-level average prices by the material input ratio

(1.67).

Table 1. Raw Material Input Ratios for the U.S. Cement Industry: 2005

Variable Description Short Tons (103)

Raw material inputa160,937

Clinker productionb96,348

Material Input Ratio 1.67

aU.S. Department of the Interior, U.S. Geological Survey. 2006a. 2005 Minerals Yearbook, Cement. Table 6.

Washington, DC: U.S. Department of the Interior.

bU.S. Department of the Interior, U.S. Geological Survey. 2006a. 2005 Minerals Yearbook, Cement. Table 3.

Washington, DC: U.S. Department of the Interior.
Elineth Torres

March 19, 2010

Page 2

Table 2. Raw Material Prices by State: 2005

State

Price of Raw Materials

($/short ton)a

AL $5.96

AK $5.99

AZ $5.22

AR $5.71

CA $7.59

CO $6.21

CT $8.34

DE $6.25

FL $7.87

GA $6.92

HI $12.10

ID $4.87

IL $6.50

IN $4.90

IA $6.60

KS $6.53

KY $6.57

LA $7.42

ME $6.21

MD $7.51

MA $8.34

MI $3.53

MN $7.53

MS $10.80

MO $6.69

MT $4.32

NE $6.44

NV $6.50

NH $7.28

NJ $6.39

NM $6.05

NY $7.66

NC $7.79

ND $4.04

OH $5.28

OK $5.14

OR $5.45

PA $6.05

RI $7.02

SC $6.90

SD $4.17

TN $6.85

TX $5.58

UT $5.06

VT $6.12

VA $8.19

(continued)
Elineth Torres

March 19, 2010

Page 3

Table 2. Raw Material Costs by Market and State: 2005 (continued)

State

Price of Raw Materials

($/short ton)

WA $6.28

WV $6.22

WI $5.29

WY $5.15

Source: U.S. Department of the Interior, U.S. Geological Survey. 2006a. 2005 Minerals Yearbook, Crushed Stone

Table 4. Washington, DC: U.S. Department of the Interior.
Elineth Torres

March 19, 2010

Page 4

Repair and Maintenance

Rock Products (1994) magazine reports the 1993 values of annual repair and maintenance costs

by kiln capacity (Table 3). Since more recent repair and maintenance costs are not available, we

adjusted these costs to 2005 dollars using the Chemical Engineering Plant Cost Index (Chemical

Engineering, 1999 and Lowzoski, 2010).

Table 3. Annual Repair and Maintenance Costs by Kiln Capacity: 2005

Annual Kiln Clinker

Capacity (short tons) 1993 ($/short ton) 2005 ($/short ton)a

<500,000 $9.56 $12.46

500,000 to 750,000 $7.53 $9.81

>750,000 $5.31 $6.92

Sources: Rock Products. 1994. “Cement Plant Operating Cost Study.” Overland Park, KS: Intertec Publishing.

September, p. 19. Chemical Engineering. 1999. Economic Indicators. Chemical Engineering,January, 154.

Lowzoski, Dorothy. 2010. Economic Indicators. Chemical Engineering, January, 63.
Elineth Torres

March 19, 2010

Page 5

Electricity

To reflect the differences in electricity consumption process type and kiln size, we collected

energy consumption (kWh) and clinker production data (Table 4). To calculate electricity costs,

we multiplied state electricity prices (Table 5) by the electricity input ratio (last column of Table

4)

Table 4. Electricity Consumption of U.S. Cement Producers by Process Type: 2005

Process Type

Clinker

Production (Short

tons)

Electricity

Consumption

(1,000 kWh)

Electricity

Input Ratio

(kWh per short

ton)a

Wet Process

≤500,000 ton capacity 3,558,496 515,451 145

>500,000-ton capacity 8,627,124 1,144,757 133

Dry Process

≤500,000-ton capacity 1,723,766 276,358 160

>500,000-ton capacity 75,561,840 10,501,877 139

Precalciner 48,827,792 6,725,423 138

Preheater 17,704,683 2,499,535 141

Source: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005.

Skokie, IL: PCA’s Economic Research Department.
Elineth Torres

March 19, 2010

Page 6

Table 5. Electricity Price by Market and State: 2005

State Cents per kWh

AL 4.52

AK 9.29

AZ 5.85

AR 4.74

CA 9.55

CO 5.74

CT 9.40

DE 6.21

FL 6.46

GA 5.28

HI 15.79

ID 3.91

IL 4.61

IN 4.42

IA 4.56

KS 4.85

KY 3.60

LA 6.71

ME 7.28

MD 7.01

MA 9.22

MI 5.32

MN 5.02

MS 5.37

MO 4.54

MT 4.83

NE 4.43

NV 7.71

NH 11.48

NJ 9.76

NM 5.61

NY 8.23

NC 5.04

ND 4.32

OH 5.10

OK 5.11

OR 4.83

PA 6.29

RI 10.01

SC 4.55

SD 4.95

TN 4.73

TX 7.14

UT 4.24

(continued)
Elineth Torres

March 19, 2010

Page 7

Table 5. Electricity Price by Market and State: 2005 (continued)

State Cents per kWh

VT 7.77

VA 4.46

WA 4.27

WV 3.85

WI 5.39

WY 3.99

Source: U.S. Energy Information Administration. 2006. Electric Power Annual 2005. Figure 7-7. Washington, DC:

U.S. Energy Information Administration.
Elineth Torres

March 19, 2010

Page 8

Labor

To calculate labor costs, we grouped labor into two categories:

 Direct or “variable” labor costs ($/short of clinker production): Labor costs that vary

with clinker production levels.

 Other or “fixed” labor costs ($/short ton of clinker capacity): Labor costs that do not vary

with clinker production levels but can be avoided by closing a cement plant.

Direct Labor

To reflect the differences in labor use, we collected direct labor use and clinker production data

(Table 6). To calculate electricity costs, we multiplied state average hourly manufacturing wage

rates1 (Table 7) by the direct input ratio (last column of Table 6). To better account for

additional labor costs, we approximated these costs by adding PCA estimates of plant payroll

taxes ($0.90 per short ton) and fringe ($1.50 per short ton) to the direct labor calculations.

Table 6. Direct Labor Use: 2005

Process Type

Clinker

Production (Short

tons)

Direct Labor

(hours)

Direct Labor

Input Ratio

(hours per

short ton)

Wet Process

≤500,000 ton capacity 3,558,496 1,453,200 0.41

>500,000-ton capacity 8,627,124 2,516,988 0.29

Dry Process

≤500,000-ton capacity 1,723,766 780,390 0.45

>500,000-ton capacity 75,561,840 18,403,466 0.24

Precalciner 48,827,792 11,115,457 0.23

Preheater 17,704,683 5,149,221 0.29

Source: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005.

Skokie, IL: PCA’s Economic Research Department.

1 BLS only has a complete state-level series for manufacturing. We compared BLS 2005 national earnings data for the

manufacturing ($16.56 per hour) with the cement and concrete products industry ($16.68 per hour) and found they were

comparable (BLS, 2010b). To account for the small differences, we adjusted the manufacturing data by +0.7 percent (column

3, Table 5).
Elineth Torres

March 19, 2010

Page 9

Table 5. Average Hourly Earnings by Market and State: 2005

State

Manufacturing

Average Hourly Earnings ($/hour)

Estimated Cement Industry

Average Hourly Earnings ($/hour)

AL $14.93 $15.04

AK $14.22 $14.32

AZ $14.55 $14.66

AR $13.71 $13.81

CA $15.70 $15.81

CO $15.91 $16.03

CT $18.96 $19.10

DE $17.74 $17.87

FL $13.89 $13.99

GA $14.56 $14.67

HI $14.34 $14.44

ID $14.96 $15.07

IL $15.84 $15.95

IN $18.14 $18.27

IA $16.25 $16.37

KS $17.14 $17.26

KY $16.65 $16.77

LA $17.30 $17.43

ME $17.28 $17.41

MD $16.98 $17.10

MA $17.66 $17.79

MI $21.50 $21.66

MN $16.63 $16.75

MS $13.53 $13.63

MO $17.42 $17.55

MT $15.62 $15.73

NE $15.44 $15.55

NV $14.98 $15.09

NH $15.87 $15.99

NJ $16.33 $16.45

NM $13.66 $13.76

NY $17.77 $17.90

NC $14.38 $14.48

ND $15.29 $15.40

OH $19.07 $19.21

OK $14.56 $14.67

OR $15.49 $15.60

PA $15.26 $15.37

RI $13.12 $13.22

(continued)
Elineth Torres

March 19, 2010

Page 10

Table 5. Average Hourly Earnings by Market and State: 2005 (continued)

State

Manufacturing

Average Hourly Earnings ($/hour)

Estimated Cement Industry

Average Hourly Earnings ($/hour)

SC $15.23 $15.34

SD $13.47 $13.57

TN $14.02 $14.12

TX $14.03 $14.13

UT $14.73 $14.84

VT $15.06 $15.17

VA $16.40 $16.52

WA $18.83 $18.97

WV $17.14 $17.26

WI $16.29 $16.41

WY $17.08 $17.20

Source: U.S. Department of Labor, Bureau of Labor Statistics. 2010a. “Employment, Hours, and Earnings from the

Current Employment Statistics survey (State)” Washington, DC: U.S. Department of Labor. Accessed February

11, 2010.

Other Labor

To reflect other labor operating costs, we also added PCA estimates of salaried employee costs

($2.80 per short ton of clinker capacity), and other payroll expenses (SG&A) ($3.20 per short ton

of clinker capacity).
Elineth Torres

March 19, 2010

Page 11

REFERENCES

Chemical Engineering. 1999. Economic Indicators. Chemical Engineering, January, 154.

Lowzoski, Dorothy. 2010. Economic Indicators. Chemical Engineering, January, 63.

Portland Cement Association (PCA). December 2005. U.S. and Canadian Labor-Energy Input

Survey 2005. Skokie, IL: Portland Cement Association Economic Research Department.

Rock Products. 1994. “Cement Plant Operating Cost Study.” Overland Park, KS: Intertec

Publishing. September, p. 19.

U.S. Department of the Interior, U.S. Geological Survey. 2006a. 2005 Minerals Yearbook,

Cement. Tables 3, 4, and 6. Washington, DC: U.S. Department of the Interior.

U.S. Department of the Interior, U.S. Geological Survey. 2006b. 2005 Minerals Yearbook,

Crushed Stone. Table 4. Washington, DC: U.S. Department of the Interior.

U.S. Department of Labor, Bureau of Labor Statistics. 2010a. Employment, Hours, and Earnings

from the Current Employment Statistics survey (State). Washington, DC: U.S.

Department of Labor. Accessed February 11, 2010.

U.S. Department of Labor, Bureau of Labor Statistics. 2010b. “Employment, Hours, and

Earnings from the Current Employment Statistics survey (National).” Washington, DC:

U.S. Department of Labor. Accessed February 11, 2010.

U.S. Energy Information Administration. 2006. Electric Power Annual 2005. Figure 7-7.

Washington, DC: U.S. Energy Information Administration.
TO: Keith Barnett, U.S. EPA 

CC: Michael Laney, RTI International

Ravi Srivastava, U.S. EPA

Elineth Torres, U.S. EPA

Samudra Vijay, U.S. EPA

Tom Walton, U.S. EPA

FROM: Brooks Depro, RTI International

Alex Rogozhin, RTI International

Dallas Wood, RTI International

DATE: August 31, 2007

SUBJECT: Draft: Documentation for Portland Cement Kiln Cost Functions (2005)

This memorandum documents how RTI International updated Portland cement average variable 

cost (AVC) functions. The update process currently excludes estimates of fixed operating and 

maintenance (O&M) costs that may be paid by kilns to cover any costs that are not a function of 

overall cement production. These data (expressed in terms of dollars per ton of annual capacity) 

are currently not available.

The following elements are included in the analysis: the peer-reviewed kiln-level average cost 

function method used in the Cement Kiln Dust Rulemaking RIA (U.S. Environmental Protection 

Agency [EPA], 1998), all the data RTI used in the analysis, and new kiln-level AVC estimates. 

BACKGROUND

Previous economic impact analyses (EPA, 1998) used a kiln-level AVC function developed in a

microeconomic study of kiln use and retirement (Das, 1992). Das describes five variable inputs 

in cement production: raw materials, repair and maintenance, labor, electricity, and fuel. Raw 

materials serve as the kiln feed, repair and maintenance are required for periodic upkeep of the 

kiln, labor is used in the quarry and for packing, electricity is consumed mainly by the auxiliary

equipment, and fuel is largely consumed by the kilns.

The AVC function (expressed in dollars per metric ton of cement) can be written as

AVC = AVMI *Pm + AVRI * Pr + AVLI * w + AVFI *Pf f + AVEI * Pe,

where AVRI, AVLI, AVFI, AVEI, and AVMI are the average variable inputs of materials, repair 

and maintenance, labor, fuel, and electricity, and Pm, Pr, w, Pf, and Pe are the prices of each 

variable input.
Keith Barnett

August 31, 2007

Page 2

For the emissions trading model, RTI collected data from the Portland Cement Association 

(PCA, 2005) and government statistical publications and updated selected cost function 

parameters. These updates are presented and discussed in this memorandum.

COST FUNCTION UPDATES FOR 2005

Raw Materials

In Table 1, we report the quantities of raw material inputs, cement production, and the raw 

material input ratio for the U.S. cement industry. In 2005, approximately 1.67 metric tons of raw 

materials were required to make a metric ton of cement. 

Table 1. Raw Material Input Ratios for the U.S. Cement Industry: 2005

Metric Tons (103)

Raw material inputa159,700

Cement productionb95,488

Ratio (AVMI) 1.67

aU.S. Department of the Interior, U.S. Geological Survey. 2006a. 2005 Minerals Yearbook, Cement. Table 6. 

Washington, DC: U.S. Department of the Interior.

bU.S. Department of the Interior, U.S. Geological Survey. 2006a. 2005 Minerals Yearbook, Cement. Table 3. 

Washington, DC: U.S. Department of the Interior.

State raw material prices for crushed stone range between $4 and $13 per metric ton (Table 2). 

To calculate raw material costs, we multiplied state raw material prices by the raw material input 

ratio (1.67).

Repair and Maintenance

Rock Products (1994) magazine reports the 1993 values of annual repair and maintenance costs 

by kiln capacity (Table 3). Since more recent repair and maintenance costs are not available, we 

adjusted these costs to 2005 dollars using the gross domestic product (GDP) deflator.

Labor

The Portland Cement Association reports labor productivity measures in terms of metric tons of 

cement per employee hour. We used these data to calculate the average variable labor inputs 

(AVLI): a measure of employee hours needed per ton of cement. AVLI estimates are computed 

by taking the inverse of each labor productivity measure. Table 4 shows labor productivity

continues to vary by process type and kiln size. Productivity is higher for dry process kilns 

compared with wet kilns; it is also higher for large-capacity kilns. 
Keith Barnett

August 31, 2007

Page 3

Table 2. Raw Material Costs by Market and State: 2005

State(s)

Price of Raw Materials 

($/metric ton)a

Cost of Raw Materials 

($/metric ton of cement)

AL $6.57 $10.99

AK $6.60 $11.04

AZ $5.75 $9.62

AR $6.29 $10.52

CA $8.37 $14.00

CO $6.85 $11.46

CT $9.19 $15.37

DE $6.89 $11.52

FL $8.67 $14.50

GA $7.63 $12.76

HI $13.34 $22.31

ID $5.37 $8.98

IL $7.16 $11.97

IN $5.40 $9.03

IA $7.27 $12.16

KS $7.20 $12.04

KY $7.24 $12.11

LA $8.18 $13.68

ME $6.85 $11.46

MD $8.28 $13.85

MA $9.19 $15.37

MI $3.89 $6.51

MN $8.30 $13.88

MS $11.90 $19.90

MO $7.37 $12.33

MT $4.76 $7.96

NE $7.10 $11.87

NV $7.17 $11.99

NH $8.02 $13.41

NJ $7.04 $11.77

NM $6.67 $11.16

NY $8.44 $14.12

NC $8.59 $14.37

ND $4.45 $7.44

OH $5.82 $9.73

OK $5.67 $9.48

OR $6.01 $10.05

PA $6.67 $11.16

RI $7.74 $12.94

SC $7.61 $12.73

SD $4.60 $7.69

TN $7.55 $12.63

TX $6.15 $10.29

UT $5.58 $9.33

VT $6.75 $11.29

VA $9.03 $15.10

WA $6.92 $11.57

(continued)
Keith Barnett

August 31, 2007

Page 4

Table 2. Raw Material Costs by Market and State: 2005 (continued)

State(s)

Price of Raw Materials 

($/metric ton)a

Cost of Raw Materials 

($/metric ton of cement)

WV $6.86 $11.47

WI $5.83 $9.75

WY $5.68 $9.50

aU.S. Department of the Interior, U.S. Geological Survey. 2006a. 2005 Minerals Yearbook, Crushed Stone. Table 4. 

Washington, DC: U.S. Department of the Interior.

Table 3. Annual Repair and Maintenance Costs by Kiln Capacity: 2005

Kiln Capacity (short tons) 1993 ($/metric ton) 2005 ($/metric ton)a

<500,000 tons $10.54 $15.46

500,000 to 750,000 tons $8.30 $12.17

>750,000 tons $5.85 $8.59

aAdjusted by chained (real) GDP obtained from the U.S. Department of Commerce, Bureau of Economic Analysis. 

2007. Current-Dollar and “Real” Gross Domestic Product. Available at 

http://www.bea.gov/national/xls/gdplev.xls.

Table 4. Labor Productivity Measuresfor the U.S. Cement Industry by Process Type and 

Kiln Size: 2005

Process Type Metric Tons per Employee Houra, b

AVLI

(employee hours 

per metric ton)

a

Wet Process 2.16 0.4630

•500,000-ton capacity 1.87 0.5348

>500,000-ton capacity 2.31 0.4329

Dry Process 3.14 0.3185

•500,000-ton capacity 1.93 0.5181

>500,000-ton capacity 3.19 0.3135

Precalciner 3.36 0.2976

Preheater 3.2 0.3125

All Plants 2.96 0.3378

a

Following the Portland Cement Association (PCA, 2005), the metric tons used to measure labor efficiency are an 

equivalent ton measure, composed of 85% clinker production and 15% finished cement production.

b

Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. Skokie, IL: 

PCA’s Economic Research Department. 
Keith Barnett

August 31, 2007

Page 5

State average hourly earnings for the durable goods industry are presented in Table 5. To 

calculate labor costs, we multiplied state average hourly earnings by labor hours per metric ton. 

Table 5. Average Hourly Earnings by Market and State: 2005

State

U.S. BLS

Series Report ID Average Hourly Earnings ($)

AL SMU0100000310000006 $15.33

AK SMU0200000300000006 $14.22

AZ SMU0400000310000006 $14.72

AR SMU0500000310000006 $13.98

CA SMU0600000310000006 $16.47

CO SMU0800000310000006 $16.17

CT SMU0900000310000006 $19.57

DE SMU1000000310000006 $23.20

FL SMU1200000310000006 $13.94

GA SMU1300000310000006 $15.96

HI SMU1500000300000006 $14.34

ID SMU1600000300000006 $14.96

IL SMU1700000310000006 $16.35

IN SMU1800000310000006 $18.90

IA SMU1900000310000006 $15.95

KS SMU2000000310000006 $17.82

KY SMU2100000310000006 $17.50

LA SMU2200000310000006 $16.62

ME SMU2300000310000006 $16.77

MD SMU2400000310000006 $18.67

MA SMU2500000310000006 $18.48

MI SMU2600000310000006 $23.07

MN SMU2700000310000006 $16.99

MS SMU2800000310000006 $14.26

MO SMU2900000310000006 $19.12

MT SMU3000000310000006 $14.87

NE SMU3100000310000006 $15.30

NV SMU3200000310000006 $15.29

NH SMU3300000310000006 $15.95

NJ SMU3400000310000006 $17.42

NM SMU3500000300000006 $13.66

NY SMU3600000310000006 $19.02

NC SMU3700000310000006 $15.04

ND SMU3800000310000006 $15.07

OH SMU3900000310000006 $20.62

OK SMU4000000310000006 $15.93

OR SMU4100000310000006 $15.76

PA SMU4200000310000006 $15.59

RI SMU4400000310000006 $13.08

SC SMU4500000310000006 $16.00

SD SMU4600000300000006 $13.47

TN SMU4700000310000006 $14.01

TX SMU4800000310000006 $13.53

(continued)
Keith Barnett

August 31, 2007

Page 6

Table 5. Average Hourly Earnings by Market and State: 2005 (continued)

State

U.S. BLS

Series Report ID Average Hourly Earnings ($)

UT SMU4900000310000006 $14.71

VT SMU5000000310000006 $15.45

VA SMU5100000310000006 $17.45

WA SMU5300000300000006 $18.83

WV SMU5400000310000006 $16.71

WI SMU5500000310000006 $17.04

WY SMU5600000300000006 $17.08

U.S. Department of Labor, Bureau of Labor Statistics. 2007. “State and Area Employment, Hours and Earnings 

2005.” Washington, DC: U.S. Department of Labor.

Electricity

To reflect the differences in electricity consumption process type and kiln size, we collected 

energy consumption (kWh) and production data (Table 6). We computed electricity consumption 

rates (AVEI) as follows: 

AVEI = kWh/(0.92*Clinker Production + 0.08*Finished Cement Production).

This approach follows the Portland Cement Association’s (PCA, 2005) energy efficiency

measures using “equivalent tons.” This measure is composed of 92% clinker production and 8%

finished cement production.

Table 6. Electricity Consumption of U.S. Cement Producers by Process Type: 2005

Process Type

Clinker 

Production 

(metric tons)

Finished Cement 

Production

(metric tons)

Electricity

Consumption

(1,000 kWh)

AVEI

(kWh per 

metric ton)a

Wet Process 11,054,608 12,150,780 1,660,208 149.0

•500,000-ton capacity 3,228,213 3,563,013 515,451 158.4

>500,000-ton capacity 7,826,395 8,587,767 1,144,757 145.1

Dry Process 70,112,322 77,036,853 10,778,235 152.5

•500,000-ton capacity 1,563,774 1,690,568 276,358 175.6

>500,000-ton capacity 68,548,548 75,346,285 10,501,877 152.0

Precalciner 44,295,828 48,559,311 6,725,423 150.7

Preheater 60,357,246 66,278,745 9,224,958 151.6

All Plants 81,166,930 89,187,633 12,438,443 152.0

a

Following the Portland Cement Association (PCA, 2005), the metric tons used to measure energy efficiency are an 

equivalent ton measure composed of 92% clinker production and 8% finished cement production.

Source: Portland Cement Association. December 2005. U.S. and Canadian Labor-Energy Input Survey 2005. 

Skokie, IL: PCA’s Economic Research Department. 
Keith Barnett

August 31, 2007

Page 7

Table 7 presents state electricity prices. To calculate electricity costs, we multiplied these prices 

by AVEI.

Table 7. Electricity Price by Market and State: 2005

State Cents per kWh

AL 4.52

AK 9.29

AZ 5.85

AR 4.74

CA 9.55

CO 5.74

CT 9.40

DE 6.21

FL 6.46

GA 5.28

HI 15.79

ID 3.91

IL 4.61

IN 4.42

IA 4.56

KS 4.85

KY 3.60

LA 6.71

ME 7.28

MD 7.01

MA 9.22

MI 5.32

MN 5.02

MS 5.37

MO 4.54

MT 4.83

NE 4.43

NV 7.71

NH 11.48

NJ 9.76

NM 5.61

NY 8.23

NC 5.04

ND 4.32

OH 5.10

OK 5.11

OR 4.83

PA 6.29

RI 10.01

SC 4.55

SD 4.95

TN 4.73

TX 7.14

UT 4.24

(continued)
Keith Barnett

August 31, 2007

Page 8

Table 7. Electricity Price by Market and State: 2005 (continued)

State Cents per kWh

VT 7.77

VA 4.46

WA 4.27

WV 3.85

WI 5.39

WY 3.99

U.S. Energy Information Administration. 2006a. Electric Power Annual 2005. Figure 7-7. Washington, DC: U.S. 

Energy Information Administration.

Fuel 

Das (1992) represents average variable fuel input rates using the following expression:

AVFI = (1 + constant)Age in 2005 * average variable fuel input of a new kiln.

We used the calibration procedures described from EPA’s 1998 cement study (EPA, 1998) to 

compute a revised average variable primary fuel input of a new kiln. The constant used remains

0.0087, and Table 8 reports the average variable fuel input of a new kiln by its process type and 

its capacity. 

Table 8. Calibrated Average Variable Primary Fuel Input for New Kiln by Process Type: 

2005

Process Type

Average Variable Fuel Input for a New Kiln

(million Btu per metric ton)

Wet Process

•500,000-ton capacity 2.84

>500,000-ton capacity 3.34

Dry Process

•500,000-ton capacity 3.43

>500,000-ton capacity 2.61

Precalciner 2.97

Preheater 3.01

The AEO 2007 reports other industrial coal, natural gas, and residual fuel prices by census 

region (Table 9) (EIA, 2007). For petroleum coke price, we used the procedure outlined in the 

EIA’s State Energy Consumption, Price, and Expenditure Estimates (SEDS): Technical Notes 

Prices and Expenditures Section 4 (EIA, 2006b). In the process described there, the average 

price was calculated by dividing the sum of the value of calcined and uncalcined petroleum coke 

exports by the sum of export quantities. We collected these data from the U.S. International 
Keith Barnett

August 31, 2007

Page 9

Table 9. Fuel Prices by Census Region, State, and Fuel Type: 2005

Fuel Price ($/million Btu)

Census Region State

Other Industrial 

Coal a

Petroleum 

Cokeb Natural Gasa Residual Oila

East South Central AL $2.47 $1.80 $8.51 $7.81

Pacific AK $2.24 $1.80 $7.46 $7.98

Mountain AZ $1.73 $1.80 $7.71 $7.59

West South Central AR $1.79 $1.80 $7.80 $7.02

Pacific CA $2.24 $1.80 $7.46 $7.98

Mountain CO $1.73 $1.80 $7.71 $7.59

New England CT $3.71 $1.80 $10.47 $7.79

Middle Atlantic DE $2.13 $1.80 $9.65 $8.33

South Atlantic FL $2.83 $1.80 $9.11 $7.93

South Atlantic GA $2.83 $1.80 $9.11 $7.93

Pacific HI $2.24 $1.80 $7.46 $7.98

Mountain ID $1.73 $1.80 $7.71 $7.59

East North Central IL $2.24 $1.80 $8.52 $7.65

East North Central IN $2.24 $1.80 $8.52 $7.65

West North Central IA $1.21 $1.80 $7.86 $7.18

West North Central KS $1.21 $1.80 $7.86 $7.18

East South Central KY $2.47 $1.80 $8.51 $7.81

West South Central LA $1.79 $1.80 $7.80 $7.02

New England ME $3.71 $1.80 $10.47 $7.79

South Atlantic MD $2.83 $1.80 $9.11 $7.93

New England MA $3.71 $1.80 $10.47 $7.79

East North Central MI $2.24 $1.80 $8.52 $7.65

West North Central MN $1.21 $1.80 $7.86 $7.18

East South Central MS $2.47 $1.80 $8.51 $7.81

West North Central MO $1.21 $1.80 $7.86 $7.18

Mountain MT $1.73 $1.80 $7.71 $7.59

West North Central NE $1.21 $1.80 $7.86 $7.18

Mountain NV $1.73 $1.80 $7.71 $7.59

New England NH $3.71 $1.80 $10.47 $7.79

Middle Atlantic NJ $2.13 $1.80 $9.65 $8.33

Mountain NM $1.73 $1.80 $7.71 $7.59

East North Central NY $2.24 $1.80 $8.52 $7.65

South Atlantic NC $2.83 $1.80 $9.11 $7.93

West North Central ND $1.21 $1.80 $7.86 $7.18

East North Central OH $2.24 $1.80 $8.52 $7.65

West South Central OK $1.79 $1.80 $7.80 $7.02

Pacific OR $2.24 $1.80 $7.46 $7.98

Middle Atlantic PA $2.13 $1.80 $9.65 $8.33

New England RI $3.71 $1.80 $10.47 $7.79

South Atlantic SC $2.83 $1.80 $9.11 $7.93

West North Central SD $1.21 $1.80 $7.86 $7.18

East South Central TN $2.47 $1.80 $8.51 $7.81

East South Central TX $2.47 $1.80 $8.51 $7.81

Mountain UT $1.73 $1.80 $7.71 $7.59

(continued)
Keith Barnett

August 31, 2007

Page 10

Table 9. Fuel Prices by Census Region, State, and Fuel Type: 2005 (continued)

Fuel Price ($/million Btu)

Census Region State

Other Industrial 

Coal a

Petroleum 

Coke b Natural Gas a Residual Oil a

New England VT $3.71 $1.80 $10.47 $7.79

South Atlantic VA $2.83 $1.80 $9.11 $7.93

Pacific WA $2.24 $1.80 $7.46 $7.98

South Atlantic WV $2.83 $1.80 $9.11 $7.93

East North Central WI $2.24 $1.80 $8.52 $7.65

Mountain WY $1.73 $1.80 $7.71 $7.59

aOther industrial coal, natural gas, and residual oil: U.S. Energy Information Administration. 2007. Annual Energy 

Outlook 2007. Supplemental Tables 12, 15, and 16. Washington, DC: U.S. Energy Information Administration.

For kilns using waste oil as a primary fuel, we used the residual fuel oil price as a proxy.

b

Petroleum coke: Export trade data come from USITC Interactive Tariff and Trade DataWeb (2007)

<http://dataweb.usitc.gov/scripts/user_set.asp >. HTS codes: 271311 and 271312. The price calculation is 

described in State Energy Consumption, Price, and Expenditure Estimates (SEDS): Technical Notes Prices and 

Expenditures Section 4 (EIA, 2006b). <http://www.eia.doe.gov/emeu/states/sep_prices/notes/pr_petrol.pdf.>.

Trade Commissions Database (USITC, 2007). To convert units to million Btu, we converted 

from metric tons to barrels by multiplying by 5.51, and we converted from barrels to million Btu 

by multiplying by 6.024.

To calculate fuel costs, we used the primary fuel used by the kiln reported by the Portland 

Cement Association (2004) and multiplied fuel prices by the fuel input equation described above.

RESULTS

RTI used the methods described above to revise the kiln-level AVC functions and to reflect the 

cost differences across kiln technologies and kiln size. The new AVC estimates for each kiln in 

the Atlanta and Baltimore/Philadelphia markets are reported in Table 10. We report them in 

reported in dollars per metric ton and dollars per short ton.
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

GA101 Atlanta Lafarge North 

America

Atlanta Plant Atlanta GA 42 C Dry 315 $60 $54

TN101 Atlanta Buzzi Unicem 

USA, Inc.

Chattanooga Plant Chattanooga TN 5 K Dry-C 771 $38 $35

GA201 Atlanta Cemex Clinchfield Plant Clinchfield GA 6 C Dry-X 705 $48 $43

VA101 Atlanta Titan America LLC Roanoke Plant Cloverdale VA 9 C Dry-C 1,120 $45 $41

SC101 Atlanta Giant Cement 

Holding, Inc.

Harleyville Plant Harleyville SC 53 C Wet 164 $57 $51

SC102 Atlanta Giant Cement 

Holding, Inc.

Harleyville Plant Harleyville SC 49 C Wet 156 $56 $51

SC103 Atlanta Giant Cement 

Holding, Inc.

Harleyville Plant Harleyville SC 45 C Wet 164 $56 $51

SC104 Atlanta Giant Cement 

Holding, Inc.

Harleyville Plant Harleyville SC 33 C Wet 164 $55 $50

SC201 Atlanta Lafarge North 

America

Harleyville Plant Harleyville SC 17 C Dry-C 972 $43 $39

SC401 Atlanta Holcim (US) Inc. Holly Hill Plant Holly Hill SC 2 C Dry-C 1,860 $41 $38

TN201 Atlanta Cemex Knoxville Plant Knoxville TN 27 C Dry-C 667 $45 $41

PA601 Baltimore/ 

Philadelphia

Giant Cement 

Holding, Inc.

Bath Plant Bath PA 49 C Wet 103 $54 $49

PA602 Baltimore/ 

Philadelphia

Giant Cement 

Holding, Inc.

Bath Plant Bath PA 49 C Wet 506 $50 $45

PA801 Baltimore/ 

Philadelphia

Lehigh Cement 

Company

Allentown Plant Blandon PA 40 C Dry 470 $56 $51

PA802 Baltimore/ 

Philadelphia

Lehigh Cement 

Company

Allentown Plant Blandon PA 40 C Dry 470 $56 $51

MD101 Baltimore/ 

Philadelphia

Essroc Cement 

Corp.

Frederick Plant Buckeystown MD 47 C Wet 155 $62 $57

MD102 Baltimore/ 

Philadelphia

Essroc Cement 

Corp.

Frederick Plant Buckeystown MD 47 C Wet 155 $62 $57

MD301 Baltimore/ 

Philadelphia

St. Lawrence 

Cement Company

Hagerstown Plant Hagerstown MD 34 C Dry 513 $52 $48

(continued)
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005 (continued)

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

WV101 Baltimore/ 

Philadelphia

Essroc Cement 

Corp.

Martinsburg Plant Martinsburg WV 50 C Wet 177 $54 $49

WV102 Baltimore/ 

Philadelphia

Essroc Cement 

Corp.

Martinsburg Plant Martinsburg WV 45 C Wet 177 $54 $49

WV103 Baltimore/ 

Philadelphia

Essroc Cement 

Corp.

Martinsburg Plant Martinsburg WV 40 C Wet 354 $53 $48

PA501 Baltimore/ 

Philadelphia

Essroc Cement 

Corp.

Nazareth Plant I Nazareth PA 27 C Dry-X 1,116 $43 $39

PA502 Baltimore/ 

Philadelphia

Essroc Cement 

Corp.

Nazareth Plant III Nazareth PA 48 C Dry 150 $57 $52

PA503 Baltimore/ 

Philadelphia

Essroc Cement 

Corp.

Nazareth Plant III Nazareth PA 48 C Dry 155 $57 $52

PA504 Baltimore/ 

Philadelphia

Essroc Cement 

Corp.

Nazareth Plant III Nazareth PA 56 C Dry 107 $58 $52

PA505 Baltimore/ 

Philadelphia

Essroc Cement 

Corp.

Nazareth Plant III Nazareth PA 56 C Dry 107 $58 $52

PA201 Baltimore/ 

Philadelphia

Buzzi Unicem 

USA, Inc.

Stockertown Plant Stockertown PA 26 K Dry-X 365 $49 $44

PA202 Baltimore/ 

Philadelphia

Buzzi Unicem 

USA, Inc.

Stockertown Plant Stockertown PA 12 K Dry-X 543 $45 $40

MD201 Baltimore/ 

Philadelphia

Lehigh Cement 

Company

Union Bridge Union Bridge MD 4 C Dry-C 1,715 $47 $43

PA701 Baltimore/ 

Philadelphia

Lafarge North 

America

Whitehall Plant Whitehall PA 40 C Dry-X 419 $51 $46

PA702 Baltimore/ 

Philadelphia

Lafarge North 

America

Whitehall Plant Whitehall PA 30 C Dry-X 283 $50 $46

MS101 Birmingham Holcim (US) Inc. Artesia Plant Artesia MS 31 C Wet 419 $61 $55

AL301 Birmingham Lafarge North 

America

Roberta Plant Calera AL 3 C Dry-C 1,498 $38 $35

AL101 Birmingham Cemex Demopolis Plant Demopolis AL 28 C Dry-X 821 $41 $38

AL401 Birmingham Lehigh Cement 

Company

Leeds Plant Leeds AL 29 C Dry-X 716 $45 $41

(continued)
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005 (continued)

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

AL501 Birmingham National Cement 

Co. of Alabama

Ragland Plant Ragland AL 30 C Dry-C 900 $40 $37

AL201 Birmingham Holcim (US) Inc. Theodore Plant Theodore AL 24 C Dry-C 1,440 $40 $36

IA201 Chicago Lafarge North 

America

Buffalo Plant Buffalo IA 24 C Dry-C 946 $37 $33

IL201 Chicago Cemex Dixon Plant Dixon IL 50 K Dry-X 120 $49 $44

IL202 Chicago Cemex Dixon Plant Dixon IL 50 K Dry-X 120 $49 $44

IL203 Chicago Cemex Dixon Plant Dixon IL 50 K Dry-X 120 $49 $44

IL204 Chicago Cemex Dixon Plant Dixon IL 40 K Dry 184 $53 $48

IN101 Chicago Buzzi Unicem 

USA, Inc.

Greencastle Plant Greencastle IN 5 C Dry-C 1,190 $37 $33

IL301 Chicago Eagle Materials La Salle Plant La Salle IL 31 K Dry-X 602 $44 $40

IN201 Chicago Essroc Cement 

Corp.

Logansport Plant Logansport IN 43 K Wet 202 $49 $44

IN202 Chicago Essroc Cement 

Corp.

Logansport Plant Logansport IN 43 K Wet 202 $49 $44

IL101 Chicago Buzzi Unicem 

USA, Inc.

Oglesby Plant Oglesby IL 33 C Dry 569 $44 $40

KY101 Cincinnati Cemex Kosmosdale Plant Louisville KY 5 C Dry-C 1,365 $39 $35

IN402 Cincinnati Lehigh Cement 

Company

Mitchell Plant Mitchell IN 55 C Dry-X 251 $49 $44

IN401 Cincinnati Lehigh Cement 

Company

Mitchell Plant Mitchell IN 45 C Dry-X 251 $48 $44

IN403 Cincinnati Lehigh Cement 

Company

Mitchell Plant Mitchell IN 29 C Dry-X 274 $47 $42

IN301 Cincinnati Essroc Cement 

Corp.

Speed Plant Speed IN 33 C Dry 279 $52 $47

IN302 Cincinnati Essroc Cement 

Corp.

Speed Plant Speed IN 27 C Dry-X 542 $43 $39

OH101 Cincinnati Cemex Fairborn Plant Xenia OH 31 K Dry-X 661 $44 $40

(continued)
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005 (continued)

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

OK201 Dallas Holcim (US) Inc. Ada Plant Ada OK 47 C Wet 278 $49 $45

OK202 Dallas Holcim (US) Inc. Ada Plant Ada OK 47 C Wet 278 $49 $45

AR101 Dallas Ash Grove Cement 

Company

Foreman Plant Foreman AR 47 C Wet 246 $49 $44

AR102 Dallas Ash Grove Cement 

Company

Foreman Plant Foreman AR 43 C Wet 246 $48 $44

AR103 Dallas Ash Grove Cement 

Company

Foreman Plant Foreman AR 41 C Wet 339 $48 $44

TX201 Dallas Ash Grove Texas, 

L.P.

Midiothian Plant Midiothian TX 39 C Wet 283 $54 $49

TX202 Dallas Ash Grove Texas, 

L.P.

Midiothian Plant Midiothian TX 36 C Wet 283 $54 $49

TX203 Dallas Ash Grove Texas, 

L.P.

Midiothian Plant Midiothian TX 33 C Wet 283 $54 $49

TX701 Dallas Holcim (US) Inc. Holnam Texas L.P. Midiothian TX 18 C Dry-C 1,036 $42 $38

TX702 Dallas Holcim (US) Inc. Holnam Texas L.P. Midiothian TX 5 C Dry-C 990 $41 $37

TX901 Dallas Texas Industries 

Inc.

Midiothian Plant Midiothian TX 45 C Wet 286 $55 $50

TX902 Dallas Texas Industries 

Inc.

Midiothian Plant Midiothian TX 42 C Wet 286 $54 $49

TX903 Dallas Texas Industries 

Inc.

Midiothian Plant Midiothian TX 38 C Wet 286 $54 $49

TX904 Dallas Texas Industries 

Inc.

Midiothian Plant Midiothian TX 33 C Wet 286 $54 $49

TX905 Dallas Texas Industries 

Inc.

Midiothian Plant Midiothian TX 4 C Dry-C 1,964 $41 $37

OK301 Dallas Lafarge North 

America

Tulsa Plant Tulsa OK 44 C Dry 347 $51 $46

OK302 Dallas Lafarge North 

America

Tulsa Plant Tulsa OK 42 C Dry 341 $51 $46

(continued)
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005 (continued)

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

CO201 Denver Holcim (US) Inc. Portland Plant Florence CO 4 C Dry-C 1,631 $39 $35

WY101 Denver Eagle Materials Laramie Plant Laramie WY 9 C Dry 181 $47 $43

WY102 Denver Eagle Materials Laramie Plant Laramie WY 6 C Dry-X 416 $43 $39

CO101 Denver Cemex Lyons Plant Lyons CO 25 C Dry-X 486 $48 $44

SD101 Denver GCC of America, 

Inc.

Dacotah Cement Rapid City SD 50 C Wet 147 $44 $39

SD102 Denver GCC of America, 

Inc.

Dacotah Cement Rapid City SD 48 C Wet 147 $43 $39

SD103 Denver GCC of America, 

Inc.

Dacotah Cement Rapid City SD 10 C Dry-X 557 $36 $33

MI301 Detroit Lafarge North 

America

Alpena Plant Alpena MI 43 C Dry 364 $54 $49

MI302 Detroit Lafarge North 

America

Alpena Plant Alpena MI 40 C Dry 364 $54 $49

MI303 Detroit Lafarge North 

America

Alpena Plant Alpena MI 40 C Dry 364 $54 $49

MI304 Detroit Lafarge North 

America

Alpena Plant Alpena MI 40 C Dry 536 $42 $38

MI305 Detroit Lafarge North 

America

Alpena Plant Alpena MI 40 C Dry 536 $42 $38

MI101 Detroit Cemex Charlevoix Plant Charlevoix MI 26 C Dry-C 1,211 $38 $35

MI201 Detroit Holcim (US) Inc. Dundee Plant Dundee MI 46 C Wet 431 $52 $47

MI202 Detroit Holcim (US) Inc. Dundee Plant Dundee MI 46 C Wet 437 $52 $47

OH201 Detroit Lafarge North 

America

Paulding Plant Paulding OH 49 A Wet 226 $78 $70

OH202 Detroit Lafarge North 

America

Paulding Plant Paulding OH 49 A Wet 228 $78 $70

FL501 Florida Suwannee 

American Cement

Branford FL 2 C Dry-C 682 $49 $45

FL101 Florida Cemex Brooksville Plant Brooksville FL 29 C Dry-X 550 $53 $48

FL102 Florida Cemex Brooksville Plant Brooksville FL 20 C Dry-X 506 $52 $47

FL301 Florida Rinker Materials Brooksville Plant Brooksville FL 18 C Dry-X 605 $52 $47

(continued)
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005 (continued)

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

FL601 Florida Titan America LLC Pennsuco Plant Medley FL 1 C Dry-C 1,492 $45 $41

FL401 Florida Rinker Materials Miami Plant Miami FL 5 C Dry-C 928 $46 $41

FL201 Florida Florida Rock 

Industries

Thompson S. Baker 

Plant

Newberry FL 6 C Dry-C 726 $49 $45

KS101 Kansas City Ash Grove Cement 

Company

Chanute Plant Chanute KS 4 C Dry-C 1,308 $37 $34

KS301 Kansas City Lafarge North 

America

Fredonia Plant Fredonia KS 69 A Wet 169 $82 $74

KS302 Kansas City Lafarge North 

America

Fredonia Plant Fredonia KS 49 A Wet 243 $76 $69

KS401 Kansas City Monarch Cement 

Company

Humbolt Plant Humbolt KS 32 C Dry-X 284 $46 $42

KS402 Kansas City Monarch Cement 

Company

Humbolt Plant Humbolt KS 30 C Dry-C 503 $42 $38

KS201 Kansas City Buzzi Unicem 

USA, Inc.

Independence Plant Independence KS 88 K Dry 82 $58 $53

KS202 Kansas City Buzzi Unicem 

USA, Inc.

Independence Plant Independence KS 88 K Dry 82 $58 $53

KS203 Kansas City Buzzi Unicem 

USA, Inc.

Independence Plant Independence KS 88 K Dry 82 $58 $53

KS204 Kansas City Buzzi Unicem 

USA, Inc.

Independence Plant Independence KS 88 K Dry 82 $58 $53

NE101 Kansas City Ash Grove Cement 

Company

Louisville Plant Louisville NE 29 C Dry-X 338 $44 $40

NE102 Kansas City Ash Grove Cement 

Company

Louisville Plant Louisville NE 23 C Dry-C 507 $40 $36

OK101 Kansas City Buzzi Unicem 

USA, Inc.

Pryor Plant Pryor OK 45 C Dry 189 $51 $46

OK102 Kansas City Buzzi Unicem 

USA, Inc.

Pryor Plant Pryor OK 43 C Dry 186 $51 $46

OK103 Kansas City Buzzi Unicem 

USA, Inc.

Pryor Plant Pryor OK 25 C Dry 250 $50 $45

(continued)
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005 (continued)

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

MO501 Kansas City Lafarge North 

America

Sugar Creek Plant Sugar Creek MO 4 C Dry-C 930 $37 $34

CA101 Los Angeles California Portland 

Cement

Colton Plant Colton CA 43 C Dry 340 $66 $60

CA102 Los Angeles California Portland 

Cement

Colton Plant Colton CA 43 C Dry 340 $66 $60

CA801 Los Angeles National Cement 

Co. of California

Lebec Plant Lebec CA 6 K Dry-C 1,033 $48 $43

CA701 Los Angeles Mitsubishi Cement 

Corporation

Cushenbury Plant Lucerne Valley CA 23 C Dry-C 1,543 $50 $45

CA201 Los Angeles California Portland 

Cement

Mojave Plant Mojave CA 24 C Dry-C 1,363 $50 $45

CA1101 Los Angeles Texas Industries 

Inc.

Oro Grande Plant Oro Grande CA 57 C Dry 155 $67 $61

CA1102 Los Angeles Texas Industries 

Inc.

Oro Grande Plant Oro Grande CA 57 C Dry 155 $67 $61

CA1103 Los Angeles Texas Industries 

Inc.

Oro Grande Plant Oro Grande CA 57 C Dry 155 $67 $61

CA1104 Los Angeles Texas Industries 

Inc.

Oro Grande Plant Oro Grande CA 53 C Dry 155 $67 $61

CA1105 Los Angeles Texas Industries 

Inc.

Oro Grande Plant Oro Grande CA 53 C Dry 155 $67 $61

CA1106 Los Angeles Texas Industries 

Inc.

Oro Grande Plant Oro Grande CA 46 C Dry 155 $66 $60

CA1107 Los Angeles Texas Industries 

Inc.

Oro Grande Plant Oro Grande CA 46 C Dry 155 $66 $60

CA601 Los Angeles Lehigh Southwest 

Cement Company

Tehachapi Plant Tehachapi CA 14 C Dry-C 958 $49 $45

CA301 Los Angeles Cemex Victorville Plant Victorville CA 22 C Dry-X 1,046 $51 $47

CA302 Los Angeles Cemex Victorville Plant Victorville CA 4 C Dry-X 1,681 $50 $46

IA101 Minneapolis Holcim (US) Inc. Mason City Plant Mason City IA 39 C Dry 600 $41 $37

IA102 Minneapolis Holcim (US) Inc. Mason City Plant Mason City IA 39 C Dry 371 $50 $45

(continued)
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005 (continued)

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

IA301 Minneapolis Lehigh Cement 

Company

Lehigh-Mason City Mason City IA 27 C Dry-C 755 $37 $33

NY301 New York/ 

Boston

St. Lawrence 

Cement Company

Catskill Plant Catskill NY 41 C Wet 580 $57 $52

NY101 New York/ 

Boston

Glens Fall Lehigh 

Cement Co.

Glens Falls Plant Glens Falls NY 32 C Dry-X 586 $55 $50

NY201 New York/ 

Boston

Lafarge North 

America

Ravena Plant Ravena NY 43 C Wet 852 $54 $49

NY202 New York/ 

Boston

Lafarge North 

America

Ravena Plant Ravena NY 43 C Wet 868 $54 $49

ME101 New York/ 

Boston

Dragon Products 

Company

Thomaston Plant Thomaston ME 34 C Wet 392 $62 $56

AZ201 Phoenix Phoenix Cement 

Company

Clarkdale Plant Clarkdale AZ 46 C Dry-C 187 $46 $42

AZ202 Phoenix Phoenix Cement 

Company

Clarkdale Plant Clarkdale AZ 46 C Dry-C 187 $46 $42

AZ203 Phoenix Phoenix Cement 

Company

Clarkdale Plant Clarkdale AZ 44 C Dry-C 191 $46 $42

AZ204 Phoenix Phoenix Cement 

Company

Clarkdale Plant Clarkdale AZ 3 C Dry-C 912 $37 $33

UT101 Phoenix Ash Grove Cement 

Company

Leamington Plant Nephi UT 24 C Dry-C 810 $35 $32

AZ101 Phoenix California Portland 

Cement

Rillito Plant Rillito AZ 56 C Dry 121 $53 $48

AZ102 Phoenix California Portland 

Cement

Rillito Plant Rillito AZ 54 C Dry 121 $52 $48

AZ103 Phoenix California Portland 

Cement

Rillito Plant Rillito AZ 50 C Dry 121 $52 $47

AZ104 Phoenix California Portland 

Cement

Rillito Plant Rillito AZ 3 C Dry-C 969 $37 $33

NM101 Phoenix GCC of America, 

Inc.

Tijeras Plant Tijeras NM 46 C Dry-X 216 $48 $43

(continued)
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005 (continued)

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

NM102 Phoenix GCC of America, 

Inc.

Tijeras Plant Tijeras NM 45 C Dry-X 216 $48 $43

PA401 Pittsburgh Essroc Cement 

Corp.

Bessemer Plant Bessemer PA 44 C Wet 225 $54 $49

PA402 Pittsburgh Essroc Cement 

Corp.

Bessemer Plant Bessemer PA 41 C Wet 339 $54 $49

PA101 Pittsburgh Armstrong Cement 

& Sup. Corp.

Cabot Plant Cabot PA 79 C Wet 143 $57 $52

PA102 Pittsburgh Armstrong Cement 

& Sup. Corp.

Cabot Plant Cabot PA 79 C Wet 143 $57 $52

PA301 Pittsburgh Cemex Wampum Plant Wampum PA 47 C Dry 215 $57 $51

PA302 Pittsburgh Cemex Wampum Plant Wampum PA 47 C Dry 205 $57 $51

PA303 Pittsburgh Cemex Wampum Plant Wampum PA 47 C Dry 238 $57 $51

OR101 Salt Lake 

City

Ash Grove Cement 

Company

Durkee Plant Durkee OR 7 C Dry-C 816 $38 $34

ID101 Salt Lake 

City

Ash Grove Cement 

Company

Inkom Plant Inkom ID 77 C Wet 115 $48 $44

ID102 Salt Lake 

City

Ash Grove Cement 

Company

Inkom Plant Inkom ID 54 C Wet 145 $46 $42

MT101 Salt Lake 

City

Ash Grove Cement 

Company

Montana City Plant Montana City MT 42 C Wet 293 $46 $42

UT201 Salt Lake 

City

Holcim (US) Inc. Devil’s Slide Plant Morgan UT 8 C Dry-C 704 $38 $34

MT201 Salt Lake 

City

Holcim (US) Inc. Trident Plant Three Forks MT 32 C Wet 280 $45 $41

TX1201 San Antonio Texas-Lehigh 

Cement Company

Buda Plant Buda TX 27 C Dry-C 1,125 $43 $39

TX501 San Antonio Cemex Balcones Plant New Braunfels TX 25 K Dry-X 1,005 $41 $38

TX1001 San Antonio Texas Industries 

Inc.

Hunter Cement Plant New Braunfels TX 26 C Dry-C 780 $43 $39

TX601 San Antonio Cemex Odessa Plant Odessa TX 47 C Dry 256 $58 $53

TX602 San Antonio Cemex Odessa Plant Odessa TX 26 C Dry-X 285 $51 $46

(continued)
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005 (continued)

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

TX101 San Antonio Alamo Cement 

Company

1604 Plant San Antonio TX 24 C Dry-C 821 $43 $39

TX401 San Antonio Capitol 

Aggregates, Ltd.

Capitol Cement 

Division

San Antonio TX 40 C Wet 269 $54 $49

TX402 San Antonio Capitol 

Aggregates, Ltd.

Capitol Cement 

Division

San Antonio TX 22 C Dry-C 599 $46 $42

TX301 San Antonio Buzzi Unicem 

USA, Inc.

Maryneal Plant Sweetwater TX 34 C Dry-X 153 $52 $47

TX302 San Antonio Buzzi Unicem 

USA, Inc.

Maryneal Plant Sweetwater TX 34 C Dry-X 153 $52 $47

TX303 San Antonio Buzzi Unicem 

USA, Inc.

Maryneal Plant Sweetwater TX 34 C Dry-X 162 $52 $47

CA401 San 

Francisco

Hanson 

Permanente 

Cemente

Permanente Plant Cupertino CA 24 C Dry-C 1,497 $50 $45

CA901 San 

Francisco

RMC Pacific 

Materials

Santa Cruz Plant Davenport CA 24 C Dry-C 812 $50 $45

NV101 San 

Francisco

Eagle Materials Nevada Cement Femley NV 41 C Dry 226 $57 $52

NV102 San 

Francisco

Eagle Materials Nevada Cement Femley NV 19 C Dry-X 226 $51 $46

CA501 San 

Francisco

Lehigh Southwest 

Cement Company

Redding Plant Redding CA 24 C Dry-C 592 $54 $49

WA101 Seattle Ash Grove Cement 

Company

Seattle Plant Seattle WA 13 C Dry-C 675 $43 $39

WA201 Seattle Lafarge North 

America

Seattle Plant Seattle WA 38 K Wet 425 $51 $46

MO101 St. Louis Buzzi Unicem 

USA, Inc.

Cape Girardeau Plant Cape Girardeau MO 24 C Dry-C 1,265 $38 $34

MO401 St. Louis Holcim (US) Inc. Clarksville Plant Clarksville MO 38 C Wet 1,241 $41 $38

MO201 St. Louis Buzzi Unicem 

USA, Inc.

Festus Plant Festus MO 40 K Dry 592 $44 $40

(continued)
August 31, 2007

Table 10. Average Variable Cost by Kiln: 2005 (continued)

Emissions 

Trading 

Kiln ID

Market 

Name Company Name Plant Name City State

Age in 

2005

Primary 

Fuel IDa Processb

Clinker 

Capacity 

1,000 Metric 

Tons/Year

Average 

Variable Costs 

($/Metric Ton)

Variable 

Costs 

$/Short Ton

MO202 St. Louis Buzzi Unicem 

USA, Inc.

Festus Plant Festus MO 36 K Dry 592 $44 $40

IL401 St. Louis Lafarge North 

America

Joppa Plant Grand Chain IL 42 K Dry 443 $53 $48

IL402 St. Louis Lafarge North 

America

Joppa Plant Grand Chain IL 30 K Dry 612 $42 $38

MO301 St. Louis Continental 

Cement Co., Inc.

Hannibal Plant Hannibal MO 39 C Wet 549 $45 $41

CA1001 White 

Cement

Texas Industries 

Inc.

Crestmore Riverside CA 47 O Dry 51 $96 $87

CA1002 White 

Cement

Texas Industries 

Inc.

Crestmore Riverside CA 45 O Dry 51 $95 $86

TX801 White 

Cement

Lehigh Cement 

Company

Waco Plant Waco TX 37 G Wet 97 $78 $70

PA901 White 

Cement

Lehigh Cement 

Company

York Plant York PA 43 O Wet 112 $79 $72

a

Primary Fuel ID codes are coal (C), coke (K), oil (O), and waste (A).

b

Process codes are preheater (X) and precalciner (C).
Keith Barnett

August 31, 2007

Page 22

REFERENCES

Das, S. 1992. “A Micro-Econometric Model of Capital Utilization and Retirement: The Case of 

the U.S. Cement Industry.” Review of Economic Studies 59(2):277-297. 

Portland Cement Association (PCA). December 2004. U.S. and Canadian Portland Cement 

Industry: Plant Information Summary. Skokie, IL: Portland Cement Association 

Economic Research Department. 

Portland Cement Association (PCA). December 2005. U.S. and Canadian Labor-Energy Input 

Survey 2005. Skokie, IL: Portland Cement Association Economic Research Department. 

Rock Products. 1994. “Cement Plant Operating Cost Study.” Overland Park, KS: Intertec 

Publishing. September, p. 19.

U.S. Department of Commerce, Bureau of Economic Analysis. 2007. Current-Dollar and 

“Real” Gross Domestic Product. Available at 

http://www.bea.gov/national/xls/gdplev.xls.

U.S. Department of the Interior, U.S. Geological Survey. 2006a. 2005 Minerals Yearbook, 

Cement. Tables 3, 4, and 6. Washington, DC: U.S. Department of the Interior.

U.S. Department of the Interior, U.S. Geological Survey. 2006b. 2005 Minerals Yearbook, 

Crushed Stone. Table 4. Washington, DC: U.S. Department of the Interior.

U.S. Department of Labor, Bureau of Labor Statistics. 2007. “State and Area Employment, 

Hours and Earnings 2005.” Washington, DC: U.S. Department of Labor.

U.S. Energy Information Administration (EIA). 2006a. Electric Power Annual 2005. Figure 7-7. 

Washington, DC: U.S. Energy Information Administration.

U.S. Energy Information Administration (EIA). 2006b. State Energy Consumption, Price, and 

Expenditure Estimates (SEDS): Technical Notes Prices and Expenditures Section 4.

Washington, DC: U.S. Energy Information Administration. Available at 

http://www.eia.doe.gov/emeu/states/sep_prices/notes/pr_petrol.pdf.

U.S. Energy Information Administration (EIA). 2007. Annual Energy Outlook 2007. 

Supplemental Tables 12, 15, and 16. Washington, DC: U.S. Energy Information 

Administration.

U.S. Environmental Protection Agency. June 1998. Regulatory Impact Analysis of Cement Kiln 

Dust Rulemaking. Appendix C, p. 12. Washington, DC: U.S. Environmental Protection 

Agency. Available at http://www.epa.gov/epaoswer/other/ckd/ckd/ckdriafn.pdf.

U.S. International Trade Commission. 2007. USITC Interactive Tariff and Trade DataWeb. 

Available at http://dataweb.usitc.gov/scripts/user_set.asp. HTS codes: 271311 and 

271312.
08/0412010

Technical Merm: Comparison ofAnalytical Tools

Used to Evaluate the Final Cement NESHAP and NSPS Rulemakings

For the National Emission Standards for Hazardous Air Pollutants (NESHAP) from the

Portland Cement Manufacturing Industry and the New Source Perfonnance Standards (NSPS)

for Portland Cement Plants, EPA utilized three models to evaluate the impacts ofthe regulation

on the industry and the economy. The models include: the Control Cost Model from the

Engineering Analysis, the Economic Impact Analysis model, and the Industrial Sector Integrated

Solutions (ISIS) model.

Typically in a regulatory analysis, EPA dctennines the regulatory options suitable to

meet statutory obligations under the Clean Air Act. Based on the stringency ofthose options,

EPA then detennines the control technologies and monitoring requirements that may be selected

to comply with the regulation. This is conducted in an Engineering Analysis. The selected

control technologies and monitoring requirements are then evaluated in a cost model to

determine the total annualized control costs. The annualized control costs serve as inputs to an

Economic Impact Analysis model that evaluates the impacts ofthose costs on the industry and

society as a whole.

For the Portland cement NESHAP and NSPS, EPA also employed the Industrial Sectors

Integrated Solution Model (ISIS) which conducts both the engineering cost analysis and the

economic analysis in a single modeling system. The ISIS Model is a dynamic and integrated

model that simulates potential decisions made in the cement industry to meet an environmental

policy under a regulatory scenario. ISIS simultaneously estimates I) optimal industry operation

to meet the demand and emission reduction requirements, 2) the suite of control technologies

needed to meet the emission limit, 3) the engineering cost ofcontrols, and 4) economic response

ofthe policy. The objective function in ISIS maximizes the total surplus for a sector while

meeting projected demand for applicable commodities and emissions constraints over the time

horizon ofinterest. ISIS has been designed to analyze the impacts of control strategies upon the

cement manufacturing industry. The model has the capability to estimate the change in cost of
production, prices, and output for the cement industry resulting from implementation ofa

regulation, and determining applicable control technology installations.

The costs ofthe Engineering Analysis are estimated based on the type ofcontrol device

that is assumed necessary for a source to comply with the proposed emission standards. Based on

a kiln's baseline emissions ofpollutants ofinterest and the removal efficiency necessary to

comply with the proposed emission limit for each regulated pollutant an appropriate control

device, or control devices, is identified. In assigning control devices to each kiln where more

than one control device would reduce emissions of a particular regulated pollutant below the

limit, it is assumed that the least costly control would be installed. However, in some instances, a

more expensive technology is considered appropriate because the selected control reduced

emissions ofmultiple pollutants. For many kilns, this analysis assumes that multiple controls will

have to be added because more than one control will be needed to control all regulated pollutants

under analysis. This approach and the resulting cost estimates are considered conservative in

that it is not possible to consider what other alternatives, in lieu ofmultiple add·on controls, may

be available to cement plants to comply with the proposed emission limits. Because individual

cement manufacturing plants may implement process or other changes to reduce emissions, the

costs ofthe proposed amendments likely overstate the costs that actually will be incurred by

individual cement plants.

The Cement Economic Impact Analysis (EIA) model uses a single-period static partialequilibrium model to compare a pre-policy cement market baseline with expected post-policy

outcomes in cement markets. This model was used in previous EPA analyses ofthe Portland

cement industry (EPA, 1998; EPA, 1999b) and for the proposal. The time horizon for the

analysis is for the intermediate run when producers have some constraints on their flexibility to

adjust factors of production. This time horizon allows us to capture important transitory impacts

ofthe program on existing producers. The model uses traditional engineering costs analysis as

"exogenous" inputs (i.e. determined outside ofthe economic model) and computes the associated

economic impacts ofthe proposed regulation.

This memo provides a comparison ofparameters in these models to provide an evaluation

ofhow the differences between the models may impact the results presented in the supporting

documentation to the regulation.

2
Engineering Analysis

In table I, we present the comparison ofthe Engineering Analysis and the Cost Module

ofISIS. With regard to data inputs, both models used the same control technologies inputs such

as control efficiency, and installation and operation cost, as well as used the same 2009 database

of kilns. A key difference regarding the two models is that while the Engineering Analysis

assumes the kiln population to be constant for its analysis, ISIS takes into account change in

operation, e.g., closure, expansion, retirements, and new units that operate more efficiently and

thus emit lower levels of pollution. Another difference between these two models resides in the

way the total cost of controls is calculated. While, the Engineering Analysis identify the specific

controls required to meet the proposed emissions limits for each kiln, and then developed capital

and annualized control costs, ISIS uses a dynamic optimization approach to select control

technologies to meet the emission limits at each kiln in a manner that considers cost, co-pollutant

reductions, and economics ofoperation, simultaneously. For example, ISIS allows for

optimization at the kiln and plant level. Ifone plant operates two kilns, ISIS might detennine

that it is more cost effective to decrease production at one kiln to a level that meets the emission

limit, and increase production at the second kiln while applying control technology. In this

example, the Engineering Analysis would apply controls at both kilns, while ISIS applies

controls at only one kiln. Overall, because ofthese differences, we would expect the total

control costs from ISIS to be lower than that ofthe Engineering Analysis.

Table 1: Comparison of the Analytical Models: Engineering Cost Model Dimension

Parameter Engineering Analysis ISIS

Kiln Population

-Existing Non hazardous waste kilns in All kilns in existence in 2009 (148

existence in 2009 (154 kilns) non hazardous waste kilns' and 20

hazardous waste kilns 2

)

No retirements, expansions or Model allows for replacement and

replacements from existing kilns retirements ofexisting kilns when

optimizing production costs in a

given year.

-New A total of9 new kilns are estimated A total of9 new kilns are estimated

based on industry growth projected based on industry growth projected

between 201 Oand 20J3. Capacity 0 f between 2010 and 2013. Capacity of

I ISIS does nol include two white cement kilns and 2 gray cement kilns in Puerto Rico.

2 Hazardous waste kilns in ISIS are nol subject to the NESHAP limits.

3
each kiln is based on the Portland each kiln is based on PCA's Capacity

Cement Association (PCA) Capacity Report (October, 2009).

Report (October, 2009).

Control Detennined by engineering Endogenously detennined in the

technology judgment assuming operation ofthe model based on maximizing total

application kiln constant at a 2009 levels. surplus while meeting the demand

and emission reduction requirements.

Cost ofControl Based on empirical cost data, EPA Based on empirical cost data, EPA

Technology Control Cost Manual, published Control Cost Manual, published data,

data, Manufacturer Data and Manufacturer Data and emission test

emission test data when available. data when available.

Total Control Summation ofcapital and Uses same technology costs as the

Cost annualized costs for each control Engineering Analysis for calculating

technology applied to the kilns total control costs. However, the

comes from EPA reports such as control cost also takes into account

EPA Air Pollution Control Cost the operation ofthe kiln. Further, the

Manual, Alternative Control model allows for fuel-switching,

Technology (ACT) reports, and which could affect flue gas volume

others. and control costs.

Costs for existing sources are Costs on new and existing sources are

estimated separately from new estimated simultaneously.

sources.

Total Control Costs are summed and Total Control Costs are summed and

amortized using a 7% discount rate. the net present value is calculated

using a 7% discount rate.

Major vs. Area Known area sources not subject to Will allow unaffected area sources to

Source Kilns emission limits are factored into increase production to meet demand.

engineering cost estimates.

Has the capability to apply

differential regulatory requirements

on major point and area sources

which can affect supply curve in a

given demand center.

Hazardous Waste Kilns not subject to emission limits Kilns not subject to emission limits

Kilns under this analysis. under this analysis.

Will allow unaffected hazardous

waste kilns to increase production to

meet demand which can affect supply

curve in a given demand center.

4
Emission Emission reductions are based of Begins with baseline emissions in

reductions emissions in 2009. Baseline 2009 and considers growth and

emissions at each kiln/facility are capacity changes out to the year of

then multiplied by the control promulgation ofthe rule (2013).

efficiency measures for each

pollutant reduced by the selected

control technology.

Estimates ex-ante and ex post

emissions ofpollutants under

consideration.

Evaluates collateral impacts, such as

reductions (or increases) in criteria

pollutants emissions as a result of

I pOlicy-induced control applications.

Economic Impact Analysis

In table 2, we present the comparison ofthe Cement EIA Model and the Economic

framework ofthe ISIS Model. With regard to the characterization ofthe cement industry, both

models treat the cement industry as regional in nature due to the costs oftransporting the

commodity across long distances (i.e., cost prohibitive conditions creates regional markets).. A

key difference between the models is in the treatment ofmarkets and supply points (i.e., kilns

and import districts). In the Cement EJA Model, each kiln is modeled in one of20 cement

markets and each cement market is an independent market (i.e., markets are treated as isolated

regions). In ISIS, each ofthe 50 demand centers can be supplied by any ofthe kilns or import

districts in the U.S. In ISIS, the decision to supply from a kiln or an import district takes in to

account the cost oftransportation, which in turn is dependent on the relevant mode of

transportation and applicable distance. The EIA's model ofindependent markets is consistent

with review ofrecent economic literature on cement market behavior undertaken prior to the

running ofthis model for this regulation and documented in the RIA. On the other hand, the

trading that takes place within ISIS is consistent with the market behavior reported by the

Portland Cement Association (PCA) (APCA, 1997)3. The EIA model characterizes the cement

industry as an olig~poly while the ISIS model characterizes the industry as perfectly competitive

with supplies to demand centers affected by transportation costs. Because the oligopoly structure

3 ISIS characterization ofseller behavior takes into account peA comments that producers "rather than restricting

output to control price they seek to produce whatever amount of cement their customers demand at the prevailing

price levels. arranging for imported cement when domestic production capacity is insufficient to meet demand"

(APCA. 1997).

5
models finn choices in a way that takes into account the impact ofthe firm choice on price rather

than as a price taker as ISIS does, production changes are expected to be larger for a given

policy than ifthe market were modeled as perfectly competitive.

Regarding imports, both models characterize imports as a perfect substitute for domestically

produced cement. Both models treats imports as elastic but use different values for supply

elasticity (see Table 2 below). ISIS also includes capacity constraints associated with import

districts.

Table 2: Comparison of Analytical Models: Economic Model Dimension

Characteristic Cement EIA Model ISIS Model

Kiln Population Kilns in existence in 2005 and six All kilns in existence in 2009 plus

kilns that have since replaced kilns kilns projected to come on-line from

in existence in 2005 are modeled. 20 tOto 2013 are modeled. This

13 other kilns on-line after 2005 includes hazardous waste kilns.

and 7 NSPS kilns are treated

outside ofthe model.

Direct Compliance Costs Assigned to kiln based on Choice of control technology

(before market Engineering Analysis. determined within the model.

adjustments)

Markets 20 markets 50 demand centers

Cement Supply & Demand Single period Single Period

Baseline year: 2005 Baseline Year: Average projected

PCA's demand from 2013-2018

Model Horizon: 2013

USGS Data USGS combined with PCA future

year projections

Supply & Demand adjust with Supply & Demand adjusts with

cement price changes cement price changes

Imports Perfect substitution Perfect substitution

USGS custom district data USGS custom district data

Import elasticity = 2.4. Import elasticity = 3.94

Seller behavior Coumot multi-firm oligopoly Perfect comoetition with suonlies

6
affected by transportation costs

With a small number of firms in Firms lake the market price as given

each region, firm's recognize their

production decision influences the

market price

Seller behavior implies there is a Assumes no pre~existing distortion in pre~existing distortion in the market the market

(e.g. price is not equal to marginal (e.g. price is equal marginal costs)

costs)

Energy Use No Fuel Switching Fuel Switching is pennitted, which

gives additional flexibility to a finn to

meet the emission requirements

Closures No closures modeled. Ifthe model A kiln is idling if in the policy case its

predicts for a kiln that profits will capacity utilization rale is zero

be negative and utilization will drop

significantly the kiln is identified to

be to be in danger of being idled

until market conditions imorove.

Regional Market Each regional market is Each kiln or import district can

Interactions independent o(the other with no supply any ofthe 50 demand centers,

cross boundary trades subject to cost of production, cost of

imports, and cost oftransportation.

Total Regulatory Cost Measure - Measure - Direct Compliance Costs

Social cost + total cost to meet the demand (cost

Social cost is estimated based on of production, imports and intertraditional social weifare economics market transportation)

(changes in producer and consumer

surplus) in reaction to the direct

compliance costs.

Job losses The employment effect (the demand Calculated in the post processing of

effect) is estimated assuming that the model based on the difference

employment is proportional to between base case employee hour/ton

I production. ofclinker to the Dolicv case.

Further details on how economic impacts are estimated by these models can be found in

the Regulatory Impact Analysis (RIA) and the ISIS Technical Support Document (TSD) for the

final NESHAP. In addition, EPA conducted a peer review ofISIS, which can be found in the

docket to this rulemaking.

7
APPENDIX A

PARTIAL EQUILIBRIUM ANALYSIS: COMPARISON OF MODEL FEATURES

EPA considered the rule's economic effects using partial equilibrium analysis; the

approach is highlighted as an important assessment tool in EPA's Guidelines for Preparing

Economic Analyses (EPA, 2000). However, partial equilibrium models can he implemented in a

variety ofways. As a result, it is important to discuss key model features when presenting a

range of estimated economic effects.

Since the Economic Impact Model and the ISIS Model put partial equilibrium analysis

into practice in different ways, this appendix provides additional information about two model

features that can help key stakeholders better understand and interpret the analysis. First, we

discuss how the two approaches define a cement company's "best" production decision given the

market definition and the existing number ofplants producing cement in these markets. Second,

we discuss differences in the ways the approaches allow regional markets to interact.

A.I Cement Production Decision and Marginal Analysis

Both economic models assume that the cement company acts in the best interest ofits

shareholders and maximizes profits. When deciding whether to make another ton ofcement, the

company considers the production effect on profits by comparing the current market price of

cement and the marginal cost; if price is above marginal cost, producing and selling the extra ton

of cement increases profit. The company continues to produce additional cement until the profit

from producing an extra ton ofcement is zero (price equals marginal cost) or capacity constraints

are reached. The ISIS Model uses this decision rule and is consistent with the assumption of pure

competition.

Now consider an alternative view used in the Economic Impact Model that examines the

possible implications of a different market structure; one that has few companies but sells similar

or identical products. 4 In this case, the cement company continues to consider the production

effect described above but adds another dimension to the decision-making process; it considers

the market price e.ffect that is associated with producing an additional ton ofcement. Given the

small number ofcement producers, adding an extra ton ofcement to the market may lower the

market price of cement and reduce the profits on all the other cement sold. lithe price effect is

large enough, it may be more profitable for the company to reduce production below the levels

4 This model is formally known as a multi-finn Coumot Oligopoly model.

8
implied by pure competition. As the number of producers expands, the cement company would

become less concerned about the market price effect, and the production decision becomes more

consistent with decisions made in pure competition.

A.l.l Market-Level Effects

What are the consequences ofthese differences as they relate to measures ofthe projected

market-level price and quantity changes? To provide some intuition about the size ofthe

differences, we present a simple market analysis with two kilns operated by different companies.

Assume the marginal cost of cement production for each plant is $98 and $1 ()() per metric ton.

The market demand for cement is met by both kilns; the least expensive kiln operates at full

capacity, and the highest cost kiln operates at 85% capacity. The baseline cement price is

equivalent to the high-cost kiln ($1 (0).

Three examples show the mechanics ofhow the pure competition model projects market

price changes in response to the regulation. The market price changes are driven by underlying

differences in the baseline kiln production costs and the distribution ofincremental compliance

costs across kilns.

Example I: Both kilns face an additional $1 per ton in compliance costs. The marginal

cost of production for each kiln is now $99 and $101. In equilibrium, the new market price is

$101 (I % increase). Assuming a demand elasticity of-I, the quantity demanded with regulation

falls by 1%.

Example 2: Next, only the least-cost kiln faces an additional $1 per ton in compliance

costs. The marginal cost of production for each kiln is now $99 and $100. In equilibrium, there is

no change in the market price ($100), and there is no change in the quantity demanded.

Example 3: Now the least-cost kiln faces an additional $3 per ton in compliance costs.

The marginal cost of production for each kiln is now SI 01 and $100. In equilibrium, the new

market price is SIOI (1 % increase). Assuming a demand elasticity of-1. the quantity demanded

with regulation falls by 1%. Note the similarity with the market outcomes in Example 1.

Now consider the same market in the Economic Impact Model. As discussed earlier. the

company considers the market price effect ofthe production decision. For example, when

9
making the decision to raise production by one ton of cement, the change in marginal revenue

(dMRi) must equal the change in the marginal cost ofproducing cement:s

6Marginal Revenue = 6Marginal Cost

where the change in marginal revenue depends on the market price effects brought on by

producing an additional ton of cement. To estimate price and quantity changes in this framework,

we used the model equations described in Appendix C ofthe RIA.

Example 1: Both kilns face an additional $1 per ton in compliance costs. In equilibrium,

the new market price is $102 (2% increase). Assuming a demand elasticity of-I, the quantity

demanded with regulation falls by 2%.

Example 2: Next, only the least-cost kiln faces an additional $1 per ton in compliance

costs. In equilibrium, the new market price is $101 (l % increase). Assuming a demand elasticity

of-I. the quantity demanded with regulation falls by 1%.

Example 3: The least-cost kiln faces an additional $3 per ton in compliance costs. In

equilibrium, the new market price is $103 (3% increase). Assuming a demand elasticity of-I,

the quantity demanded with regulation falls by 3%.

Table A-I compares the projected market-level changes in these examples. As expected,

the projected relative price and quantity changes are higher under the multi-frrm Coumot

oligopoly model.

S The equation is fonnally discussed in Appendix A and Appendix B ofthe RIA.

10
Table A-I. Market-Level Effects Comparisons

Baseline Price Change Quantity Change

Marginal (Percent) (percent) Cosl Incremental

($Imetric Compliance Cost P"", Cournol Pur< Cournot

Type Ion) (Slmetric ton) Competition Oligopoly Competition Oligopoly

Example 1

Kiln I $98 $1

1.0% 2.0% -1.0010 -2.()D1o

Kiln 2 $100 $1

Extlmple 2

Kiln I $98 $1

0.0% 1.0% 0.0% -1.0"10

Kiln 2 $100 $0

Example 3

Kiln 1 $98 $3

1.0-;' 3.0010 -1.0% -3.0010

Kiln 2 $100 $0

A.2 Cement Transportation and Trade between U.S. Cement Regions

The Economic Impact Model uses 20 regional markets and the ISIS Model uses the 50

regtonal demand centers. The Economic Impact Model assumes that transportation costs between

regions are high enough that interregional trade is unlikely to occur. at least in the short run. In

contrast. the ISIS Model adds some additional flexibility in the model that allows cement plants

to ship cement to other demand centers ifthe additional benefits ofselling cement to these

markets outweigh the costs oftransporting the cement outside the regional market. Details of

transportation cost assumptions ofthis ISIS Module are described in the ISIS technical

documentation.

A.Z.l Market-Level Effects

What are the consequences ofthe trade assumptions as they relate to measures ofthe

projected market-level price and quantity changes? Allowing trade in the Economic Impact

Model would expand the cement market definitions and increase the number of producers. As

discussed above, as the number ofproducers in a market increases, the production decision

becomes more consistent with decisions made in pure competition. The extent to which trade

across regions is feasible would be driven by the assumptions about transportation costs. The

examples in Table A-I show that increasing the number ofproducers participating in the market

could moderate projected price increases and subsequent consumption changes.

I I