# Industrial Sectors Integrated Solutions (ISIS) Mode l

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 comp leted without the support and advice of many individuals and organizations. We gratefully acknow ledge the following contributions. Professor Richar d 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 E P-D-06-118, Mr. Brooks Depro of RTI International developed the cost functions and othe r economic data for the cement industry. Also under EPA Contract EP-C-07-015, Dr. James Staudt of Andov er Technologies developed the technical information on controls and energy efficiency measu res used in the current version of ISIS cement mode l. 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 Associatio n (PCA) provided helpful comments on ISIS formulation , engaged in many fruitful discussions with the ISIS development team, and provided some needed dat a. The ISIS cement model was peer reviewed by a number of experts. These experts included Dr. Dalla s Burtraw of RFF, Dr. Hendrik G. van Oss of the U.S. Geological Survey, Mr. Charles Drummond, Mr. C hris Nichols, and Mr. Sikander Khan of the U.S. Department of Energy, Mr. Hector Ybanez of Holcim C ement, Mr. Kevin Culligan of EPA, Dr. Fereidun Feizollahi of California Air Resources Board (Econo mic Studies Section), Mr. Eric Smith of EPA, and Dr. Ernst Worrell of LBL. Mr. Charles Fulcher and M r. 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 fr om peer reviewers and helped with development of this documentation under EPA Contract EP-C-09-02 7.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 Applica ble 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......................

................................................... ......................3-1ii

3.2.2 Control Technologies and Emission Abatement Approac hes ............................................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 CO 2 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 ...........................

................................................... ...........................................5-10iii



### 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 an d 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 Industr y 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 Metr ic Tons, by Customs District in

2005...............................................

................................................... .................................3-7 Table 3-5. Specific Fuel Consumption and Total Exhaust Gas Flo w Rate (wet) for Various Kiln

Types ..............................................

................................................... ................................3-9 Table 3-6. Estimated Uncontrolled NO X Emission Intensities for Cement Kilns ............. ................3-10 Table 3-7. Average SO 2 Emissions for Each Kiln Type in Each State ........ .....................................3-11 Table 3-8. Approximate CO 2 and H 2 O Produced from Combustion of Fuels................ ...................3-12 Table 3-9. NO X Control Technologies for Cement Kilns.............

................................................... ..3-15 Table 3-10. SO 2 Control Technologies for Cement Kilns.............

................................................... ...3-18 Table 3-11. CO 2 Control Technologies for Cement Kilns .............

................................................... ..3-19 Table 3-12. HCl, Hg, and THC Control Technologies for Cement Ki lns...........................................3-20 Table 3-13. Multimedia Impacts of Process Capacity Replacement on Cement Kiln Operation 1 ......3-20 Table 3-14. Energy Efficiency Measures for Raw Materials Prepar ation...........................................3-2 2 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 Dis tricts ............................................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 Distr icts (2005)...................................4-7 Table 4-5. Reported and Calculated Cement Prices in USGS Distr icts (2006)...................................4-8 Table 4-6. Reported and Calculated Cement Prices in USGS Distr icts (2007)...................................4-9 Table 4-7. Aggregate production (reported and modeled) for 200 5-2007 ..........................................4-9 Table 5-1. CO 2 Caps Modeled in this Analysis.....................

................................................... ...........5-2iv



### 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 Pr eheater.........................................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 (PC A, 2009a)....................................1-9 Figure 1-6. Portland Cement Facilities and O 3 NAA, PM 2.5 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 Industria l Sector.......................................1-12 Figure 2-1. Consumer and Producer Surplus in a Market ..........

................................................... .......2-2 Figure 2-2. Broad Modules and Associated Information Flows Util ized 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 a s of 2009.......................................... .3-3 Figure 3-2. Commercial Fuel Use Profile by U.S. Cement Industry in 2005.......................................3-9 Figure 5-1. Projected CO 2 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 NO X , SO 2 , and CO 2 from the U.S. Cement Industry over the

Horizon 2013-2020..................................

................................................... .......................5-6 Figure 5-8. Projected CO 2 Emissions and Reductions from the U.S. Cement Indu stry Under the

BAU and the 10-percent CO 2 Reduction Cases. .................................. .............................5-7 Figure 5-9. Projected Control Technology Applications in the U. S. Cement Industry Under the

10-percent CO 2 Reduction Case....................................

................................................... .5-8


### Appendix A Andover Technology Partners and RTI International M e 

### mosv



### 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 Brus sels 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 Productvi

GHG Greenhouse Gas H 2 O 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 NO X 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 Ma nagement NESHAP National Emission Standards for Hazardous Ai r Pollutants NM New Mexico NO X 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 Budgetvii

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 Classifi ers 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 SO X 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 Develo pment WMCCC Wash Mills with Closed Circuit Classifierviii



### Conversion Table – English Units to SI Units To Obtain From Multiply by m ft 0.3048
m 2 ft 2 9.29 × 10 -2

m 3 ft 3 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/m 2 in. Hg 345.31 metric ton short ton 0.9071-1 

## 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 strate gy development should be based on integrated assessments that consider multiple pollutants, and that these integrated assessments should be conduct ed in a comprehensive and coordinated manner (NAS, 200 4). With these recommendations, EPA began to move toward establishing multipollutant and sector- based approaches to manage air quality and environmental protection. The benefits of multipoll utant and sector-based analyses and approaches include the ability to identify optimum strategies (considering feasibility, costs, and benefits acros s all pollutant types such as criteria, toxics, and other s), while streamlining administrative and complianc e complexities and reducing conflicting and redundant requirements. The development of policy options for managing emis sions and air quality can be made more effective and efficient through sophisticated analyses of rel evant technical and economic factors. Such analyses are greatly enhanced by the use of an appropriate model ing framework. Accordingly, the Industrial Sectors Integrated Solutions (ISIS) model has been develope d at EPA (ARCADIS, 2010). Currently, the ISIS model is populated with US cement manufacturing dat a, and efforts are underway to build representation s of the US pulp and paper sector and the US iron and steel sector. This document describes the framewor k of EPA’s ISIS model and its application to the US c ement 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. C ement is used in mortar (to bind together bricks or stones) and concrete (bulk rock-like building mater ial made from cement, aggregate, sand, and water). Concrete production uses the majority of cement pro duced.
Portland cement and blended cement are used in conc rete production, but portland cement is by far the most common type of cement used for concrete produc tion. By modifying the raw material mix and, to some degree, the temperatures utilized in manufactu ring, slight compositional variations can be achiev ed to produce portland cements with slightly different properties. In the U.S., the different varieties o f portland cement are denoted per the American Societ y for Testing and Materials (ASTM) Specification C-150. The ASTM standard C-150 recognizes eight typ es of portland cement:

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

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

• Type II generates less heat at a slower rate and ha s 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 st rength 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 sulfa te concentration in soil and groundwater. Portland cements are usually gray, but a more expen sive white portland cement (generally within the Type I or II designations) can be obtained by proce ssing only raw materials with very low iron and transition-elements content. In addition to the eig ht types of portland cement listed above, small vol umes of specialty cements are also manufactured. Other t ypes 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). T he common industry practice, and that of the US Geological Survey (USGS), includes, within the port land cement designation, a number of other cements not within ASTM C-150, which are composed largely o f portland cement and are used for similar applications (e.g., concrete) (USGS, 2005). These c oncretes include blended cement, block cement, expansive cement, oil well cement, regulated fast s etting cement, and waterproof cement. Plastic cemen ts 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 s ector can be adequately captured by describing the market processes associated with the production, di stribution, and use of portland cement, in this wor k the focus is on portland cement. In 2006, portland ceme nt’s market share in the U.S. was 94 percent, while masonry cement’s market share comprised the remaini ng 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 mi nerals may be added to adjust the raw material composition. The fine raw material is fed into a la rge rotary kiln (cylindrical furnace) where it is h eated to extremely high temperatures (about 2640 °F [about 1 450 °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 addi tives 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 th e dry process with cyclone preheater. The schematic f or a precalciner kiln would be very similar to that shown in Figure 1-2, with the addition of a calcine r 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 else where. These kilns are 6-8 m in diameter and 60 m t o well over 100 m long. The kilns are set at a slight incline and rotate at 1 to 3 revolutions per minut e. The kiln is fired at the lower end and the feed materia ls move toward the flame as the kiln rotates. The materials reach temperatures between 1400-1500 °C i n 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 CO 2 and calcium oxide (free lime) during calcination. Finally, the lime and other minerals in the raw materials re act to form calcium silicates and calcium aluminate s, the main components of clinker.1-3ClassifierStackCoalFeederClassifierFigure 1-1. Schematic of the Wet Cement ProcessSource: CEMBUREAU, 19991-4ClassifierStackCoalFeederClassifierFigure 1-2. Schematic of the Dry Cement Process with Cyclone PreheaterSource: CEMBUREAU, 19991-5



### 1.1.3 Kiln Types and Their Use Rotary kilns are broadly categorized as dry- and we t-process kilns, depending on how the raw materials are prepared. Wet-process kilns are fed raw materia l slurry with moisture content ranging between 30 a nd 40 percent. A wet-process kiln needs additional len gth to evaporate the water contained in the raw material feed. Nearly 33 -percent additional kiln e nergy is consumed in evaporating the water in the slurry. In dry-process kilns, raw material is fed as dry po wder. There are three major variations of dry-proce ss kilns in operation in the U.S.: long dry kilns, pre heater kilns, and preheater/precalciner kilns. In p reheater kilns and preheater/precalciner kilns, the early st ages of pyroprocessing occur before the materials e nter the rotary kiln. Preheater and preheater/precalcine r kilns have higher production capacities and great er fuel efficiency compared to other types of cement k ilns. Table 1-1 shows heat input in terms of millio ns of British Thermal Units (MMBtu)/ton 1 of clinker for various types of kilns. As the data clearly demonstrate, preheater/precalciner kilns provide gr eater fuel efficiency. The replacement of wet and (certain) dry process kiln capacity with modern kil n processes can yield, theoretically, substantial reductions in fuel use due to fuel efficiency gains . As the industry moves toward more efficient proce sses, replacement of wet and long dry process capacity wi th more efficient kiln process technologies is expected. Table 1-1. Typical Average Heat Input by Cement Kil n 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 ha s shown the replacement of lower capacity, ineffici ent wet and long dry kilns with bigger and more efficie nt kilns. This trend is expected to continue. In th e U.S., the overall number of kilns decreased by 11 p ercent from 1995 to 2004. During the same period, total clinker production capacity increased by 18.6 percent. The Portland Cement Association (PCA) dat a show that average kiln capacity also increased by 2 7 percent, from 405,000 to 556,000 tons per year between 1995 and 2004. The number of kilns operatin g in the U.S. in 2005 compared with the number of kilns in operation in 2009 is shown in Table 1-2. T he trend in kiln design and average kiln capacity i s shown in Figure 1-3.

1 PLEASE NOTE: for the purposes of this document, sho rt tons will be referred to simply as “tons” and represents 2000 lbs. A unit conversion table is pro vided 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 (2 009) 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 200 7 2008Average Capacity, thousands of metrictons/yearNumber 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 bie nnial 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 p roduced by plants with wet process kiln systems whi le 81 percent was produced by plants operating only dr y kilns of all three types (long-dry, preheater and preheater/precalciner). The remaining 5.5 percent w as 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., portla nd cement is a $12 billion industry (van Oss, 2008). I n 2005, the U.S. consumed a record 128 million metr ic 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. T he cement manufacturing sector in the U.S. is concentr ated among a relatively small number of companies, many of which are owned by or are subsidiaries of f oreign companies (USGS, 2007b). Together, ten companies accounted for about 80 percent of the tot al cement production in U.S. for 2005 (USGS, 2007b). California, Texas, Pennsylvania, Florida, a nd 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 m illion tons between 2008 and 2013, an 18 -percent increase in existing capacity from 2006. PCA’s proj ected 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 expansio ns is projected at $6.9 billion. Note that a typica l 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, 20 07). If permitting for mining and (re)construction is ac counted, a typical project can take as long as 4 to 6 years for a greenfield facility and up to 3 years f or a brownfield facility (Andover Technology Partne rs, 2009a).

Mississippi-Hudson River

Cement Plant Locations Largest Cement Import Custom Districts Cement Import Custom Districts

Figure 1-4. Portland Cement Plant Locations1-8



### 1.1.5 Imports of Portland Cement in the U.S. Portland cement is not only produced and consumed d omestically, 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 hyd raulic cement and clinker (USGS, 2007b: Tables 3 and 18). The level of imports to the U.S. is highly cyclical, with domestic producers importing primar ily when domestic plants are at full capacity and canno t meet excess demand. Imports also occur when domestic producers seek to capture market share. Ge nerally, 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 c linker suppliers to the U.S. were, in descending or der, Canada, China, Thailand, Greece, the Republic of Ko rea, Venezuela, Mexico, Colombia, Taiwan, and Sweden. The ten busiest ports of entry within custo ms districts existing in 2005 were, in descending order, New Orleans, Tampa, Los Angeles, Houston-Gal veston, San Francisco, Miami, Seattle, Detroit, New York, and Charleston (South Carolina) (USGS, 20 07b). 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 cemen t from 1998 to 2008. Table 1-3. Largest Hydraulic Cement and Clinker Imp ort 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)1-9Thousands 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 sta tespecific cement demand centers. PCA reports that th e 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 trans ported 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 Applica ble Regulations Criteria pollutants, hazardous air pollutants, and CO 2 are released during cement manufacturing. Nitrogen oxides (NO X ) emissions from cement kilns result primarily from the combustion process: oxidation of fuel nitrogen (fuel NO X ) and the oxidation of nitrogen in the combustion a ir (thermal NO X ). EPA’s 2005 National Air Toxics Assessment (NATA) Inventory rep orts that cement kilns released 181,000 metric tons (200,000 short tons) of NO X emissions from the combustion of fuels (EPA, 2009)

. Sulfur dioxide (SO 2 ) emissions 2 from cement kilns result from the combustion of s ulfur-bearing compounds in coal, oil, and petroleum coke, as well as sulfur compounds in raw materials. Sulfur in th e fuel will oxidize to SO 2 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 kil ns, preheater and precalciner kilns tend to be more eff ective at capturing fuel-generated SO 2 . Accordingly, oxidation of sulfur in the feed materials is likely to be the major component of total SO 2 emissions. The

2 Small amounts of SO 3 may be released in addition to bulk SO 2 but the SO 3 emissions are treated as SO 2 for computational purposes. Small amounts of CO may be released in ad dition to bulk CO 2 but CO emissions are treated as CO 2 for computational purposes.1-10

2005 NATA Inventory reflects that cement kilns rele ased 133,000 metric tons (147,000 short tons) of SO 2 emissions in 2005. Particulate matter (PM) emissions result from quarr ying operations (the crushing and grinding of raw materials and clinker) as well as kiln line operati ons. The 2005 NATA Inventory estimates show that, i n 2005, cement kilns released 10,000 tons of PM 10 emissions. The cement industry is also a source of HAPs (e.g., hydrochloric acid vapor and chlorine), as we ll as metals including but not limited to mercury, antimony, cadmium, and lead (EPA, 2008). The cement manufacturing process is also a source o f carbon dioxide (CO 2 ) emissions. CO 2 emissions are a product of the combustion of fuel as well as the calcination of the limestone in the raw meal. The C O 2 from fuel combustion can be calculated from heat in put and fuel characteristics combined with clinker production. The CO 2 from calcination can be calculated taking into acc ount the amount of limestone used as a source of clinker calcium. Limestone currently is the predominant source of calcium for the clink er. Pure limestone would produce 0.44 tons of CO 2 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 CO 2 emissions from clinker production. The CO 2 emissions reduction would be proportional to the a mount of substitute materials added. Detailed discussion of CO 2 emissions from cement kilns can be found in Append ix A. Multiple regulatory requirements to reduce criteria air pollutants and HAPs emissions currently apply to the cement industry sector. The New Source Performa nce Standards (NSPS) and the National Emissions Standards for Hazardous Air Pollutants (NESHAPs) ar e two of the federal requirements that apply to cement facilities. Additionally, state and local re gulatory requirements might apply to individual cem ent facilities depending on their locations. In 2008, 4 4 cement facilities were located within ozone (O 3 ) nonattainment areas (NAA), while 20 facilities were within PM 2.5 nonattainment areas. Seventeen facilities were found to be located in (or within 3 0 miles of) Class 1 areas, as shown in Figure 1-6. Class 1 areas are areas of special natural, scenic, recreat ional, or historic (national or regional) value for which the Prevention of Significant Deterioration (PSD) regul ations provide special protection. 

### 1.3 Overview of ISIS ISIS, a sector-based dynamic programming model, wi ll facilitate the analyses of emission reduction strategies for multiple pollutants while taking int o account plant-level economic and technical factor s such as the type of emission units (for cement –kil n), associated capacity, location, cost of producti on, applicable controls, and their costs. For each of t he emission reduction strategies under consideratio n, the model is able to provide information on the followi ng:

• Optimal (least cost) industry operation

• Cost-effective controls to meet the demand for ceme nt

• Emission reduction requirements over the time perio d of interest ISIS design allows for incorporating multiple indus tries 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 th e demand function to estimate area under the demand curve.1-11

Figure 1-6. Portland Cement Facilities and O 3 NAA, PM 2.5 NAA, and Class 1 Areas

The ISIS code is written in the General Algebraic M odeling System (GAMS) language. Input data, organized in various spreadsheets of a Microsoft Ex cel workbook, are passed on to the GAMS files. These input data consist of an industry database, w hich provides unit-level production, capacity, production cost, and emissions information. The con trols database provides information regarding applicable air pollution control technologies and t heir cost and emission control characteristics. A p olicy module is used to specify various parameters of int erest to the policy analyst, such as emissions cap, emission reduction scenarios, and discount rate. Th e input data, control data, and policy parameters a re then transmitted to the optimization part of the IS IS model, where they are used to solve the selected base and policy cases. After solving, the results are po st-processed to calculate values of various outputs of interest. The output data are exported to Excel spr eadsheets for further analyses and graphical representation of selected results. Within an industrial sector, generally emissions ar ise from four pathways: (1) on-site emissions due t o combustion of fossil fuels for energy at plants, (2 ) on-site emissions due to processing of certain ra w materials (e.g., limestone calcination in cement pl ants, non-energy uses of fossil fuels in chemical processing and metal smelting), (3) off-site emissi ons due to combustion of fossil fuels at power plan ts to generate the electricity needed by the industrial s ector, 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 pi cture presented in Figure 1-7 makes a compelling ca se for considering commodity production/supply activit ies 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 Fi gure 1-7. Example emission reduction policies that can be evaluated using ISIS are:

• Criteria pollutants (NO X , SO 2 , PM, CO) –emission limits and/or cap-and-trade

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

• CO 2 – cap-and-trade and/or emission taxes

• Long and short time horizons: CO 2 (decades), criteria pollutants (annual)

• Regional or national requirements

Pollution Controls (NOx, SO 2 ,PM, CO 2 , 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 Generat ion Pathways, Emissions Abatement Approaches, and Multimedia Impacts for an Industria l Sector1-13



### References for Chapter 1 APCA (1997). Comments on EPA’s Draft Economic Analy sis of Air Pollution Regulations for the Portland Cement Industry (May 1996), prepared for A merican 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 con tract number EP-C-09-027 for the U.S. Environmental Protection Agency, Air Pollution Prev ention 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 – NO X Emissions from New Cement Kilns. EPA-453/R-07-006, November 2007. http://www.epa.gov/ttn/catc/dir1/cement_updt_1107.p df , accessed October 21, 2008. EPA (2008). AP 42, Fifth Edition, Compilation of Ai r 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 St ates. National Research Council (U.S.), Committee on Air Quality Management in the United States, Nat ional 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 (Doc ket Number FMCSA-2004-19608), March 10, 2005. http://www.cement.org/exec/DHOS%20Comments percent2 03.10.05.pdf , accessed October 21, 2008. PCA (2006). U.S. and Canadian Portland Cement Indus try: Plant Information Summary. Portland Cement Association, Skokie, IL, 2006. PCA (2008). History & Manufacture of Portland Cemen t. Portland Cement Association. http://www.cement.org/basics/concretebasics_history .asp , accessed October 21, 2008. PCA (2009a). PCA Annual Yearbook 2009. North Ameri can Cement Industry. Portland Cement Association. PCA (2009b). PCA Capacity Report. Flash Report. P ortland Cement Association’s Economic Research Department. Updated October 19, 2009. Andover Technologies Partners (2009a). Memorandum: NO X , SO 2 and CO 2 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. 40-4 1, January 2007, http://minerals.usgs.gov/minerals/pubs/commodity/ce ment/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/ce ment/cemenmyb05.pdf , accessed September 24, 2009. van Oss, H.G. (2008). Personal communication from H endrik 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 pattern s are given full consideration, has been developed over the past 6 decades. In SPE network models, int erregional economies are simulated by finding the balance of demand, supply, and trade that will resu lt in competitive market equilibrium among the regions. Enke (1951) first demonstrated how the cos t 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 p rogramming formulation. In this type of formulation , the equilibrium for each market of a sector is equi valent to the quantities and prices that result whi le maximizing the sum of consumer and producer surplus es for each market of the sector. This sum is referred to as the total surplus or net social payo ff of the sector; McCarl and Spreen (1980) provide interpretation and justification. The linear progra mming 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 commodit y is the difference between the total revenue and the to tal cost of supplying the commodity. This surplus (gross profit) is given by the area under the horiz ontal line P1-P minus the area under the inverse su pply curve up to point P. Similarly, the consumers’ surp lus corresponding to a quantity Q is given by the a rea 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 wh o purchase the commodity at a price lower than the price they would have been willing to pay. It is ev ident from Figure 2-1 that the total surplus is maximized exactly when Q is equal to the equilibriu m 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 prod ucers (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 mu ltiple products, the description below is provided relative to one product to bring out the important elements of the formulation and not burden the read er with many details. Also, to make the description mo re readable and understandable, the input data (i.e ., those supplied by the user, or derived from user-su pplied data) are shown in bold and the variables, whose values are determined in the optimization pro cess, 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 prev ious 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 Q E



### P C

Figure 2-1. Consumer and Producer Surplus in a Mark et

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 Informatio n Flows Utilized in the ISIS Framework2-3



### 2.1 Indexes, Sets, and Mappings Before we start the description of the mathematical structure of ISIS, it is helpful to define the set s of relevant entities and mappings describing the relat ionships among various entities. Indexes (one-dimensional sets)

• ISIS data structures (sets and parameters), variabl es, 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, replac ement, 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 interes t;

• 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 lo cated. 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, applicabilit y, 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 applicabil ity of controls k to unit i and year t ;

• poltiee(t,i,ee) is the mapping relating availability and applicabil ity of energy efficiency measures ee to unit i and year t ;

• poltirk(t, ri(i,r),k) is the mapping relating availability and applicabil ity of controls k to unit i located in geographic region r and year t ; and2-4

• poltiree(t, ri(i,r),ee) is the mapping relating availability and applicabil ity 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 i n the ISIS model corresponds to maximizing the tota l surplus (or minimizing the negative of the total su rplus) 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



### dc) ddemand(t, ) price(t,dc ions(t,j) totalemiss ee) scost(t,i, teemeasure

st(t,i,k) tcontrolco st(t,id) timportsco

(t,dc) ttranscost )

dncost(t,i tannualpro z

dis(t) j)

alprice(t, dis(t) dis(t) dis(t) dis(t) dis(t) dis(t) Minimize

(2.2.1)
where the quantities appearing in the equation abov e are defined for year t as follows, dis(t) is the discount factor, tannualprodncost(t,i) is the annual production cost ($) for production u nit i , ttranscost(t,dc) is the cost ($) of transporting the product to dem and center dc , timportscost(t,id) is the cost ($) of importing foreign product in im port district id , tcontrolcost(t,i k) is each unit’s total control cost ($) using contro l technology k , teemeasurescost(t, i, ee) is each unit’s total cost ($) using the energy eff iciency 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 

### dc t ddemand dc t price )

,( ) ,( 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. Thi s 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 droppe d in the following explanation. The relationship between demand and price in a mark et dc is expressed as

σ 1 

### D0 P0      

= D D P ) ( (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 pr ice, respectively. Figure 2-3 shows a representation of the above equa tion and also reflects how a stepwise approximation of the price-demand curve can be created.

P ( d 1 )

P ( d 2 )
P ( d 3 ) P ( d 4 ) P 0

D min D 0 D max

d 1 d

2 d

3 d

4 P ( D )

D wid
D wid Price

Quantity

Figure 2-3. Stepwise Calculation of the Demand Curv e2-6

First the range of the demand-price curve is define d using

range)
- (1 D0 D min ⋅ = (2.2.3)

range) (1 D0 D max +

⋅ = (2.2.4)

where
range is a user-supplied parameter with value between 0 and 1. This parameter defines the interval D min - D max within which the new equilibrium demand quantity i s expected to be found. Note range should be large enough to ensure that the solution does fall in the interval D min - D max. On the other hand a smaller value of range can increase precision of the stepwise approximatio n. In ISIS a value of 0.5 is used for range
. Next, a series of steps is defined within the inter val D min - D max with the width of each step given by



### steps of Number D
- D dwidth min max 

### = (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, b ut will increase the model size. In ISIS, a value o f 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 (2 dwidth D D min dstep 

### ⋅

⋅ + = (2.2.6)

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

σ 1 dstep dstep 

### D0

D P0 D P 

      

= ) ( (2.2.7)

Finally, the approximated area under the price-dema nd curve is



## ) ( dstep dstep 

## D D P steps of Number

1 dsteps 

# ∑

= 

## ⋅ (2.2.8)2-7

Substituting (2.2.8) in (2.2.1), the objective func tion becomes



## ∑ ∑ ∑ ∑ ∑ ∑ ∑ ∑

     

⋅ ⋅ − +

⋅ ⋅ + ⋅ + ⋅ + ⋅ + ⋅ + ⋅ =

t dstep dc t,j t,i,ee t,i,k t,id t,dc t,i

dstep dc t demand ions(t,j) totalemiss ee) scost(t,i, teemeasure

st(t,i,k) tcontrolco st(t,id) timportsco

(t,dc) ttranscost )

dncost(t,i tannualpro z

dstep) dc, , P(demand(t dis(t) j) alprice(t, dis(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 optimu m solution. Equation (2.2.7) is used to generate the price-dema nd 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 ine lastic 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 fo r 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), pr esented in the next section. 

### 2.3 Supply The demand for a commodity in a market can be satis fied by domestic production and foreign imports as follows: .) , ,( ) , ,( ) ,,( 

# ∑ ∑ ∑ ≥ + dstep id i dstep dc t demand dc idt antity importedqu dc it ity prodnquant (2.3.1)

where prodnquantity(t,i,dc) is the quantity supplied from the domestic product ion unit i to demand center dc in year t , importedquantity(t,oi,id,dc) is the quantity of commodity received from the ori gin (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 dstep th level of the price-demand curve for the demand center dc in year t . The construction of the price-demand curves has b een explained above.2-8

Note that transportation of quantity from productio n 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,



### . prodn(t,i) ) ity(t,i,dc prodnquant dc 

### = 

# ∑ (2.3.2)

where prodn(t, i) is the production level (tons/year) of manufacturi ng 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 i n a subsequent section. Production of a unit is constrained by its capacity . So, 

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

where capacity(t, i) is the capacity (tons product/year) of manufacturi ng 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 di strict in that year. Then,



# ∑ ∑ = istep oi dc 

### id,istep) oi Imports(t, d,dc) antity(t,i importedqu

, 

### , (2.3.4)

where Importedquantity(t,id,dc) is the imported quantity supplied from import distr ict id to demand center dc in year t, and Imports(t,oi,id,istep) is the imported quantity available at the import d istrict id in year t at the istep th level of the imports curve for country oi . Construction of the country (or region) and impor t district specific imports-cost curves is explained below. Stepwise Approximation of Imports Curves in ISIS The treatment of imports is similar to the treatmen t for elastic demand curves. Again, for clarity, th e subscripts t, oi, and id corresponding to time, origin of imports, and impo rt district are dropped in the following explanation. The relationship between imports arriving from oi at id and their price is expressed as2-9 α 1 

### Imports0 Iprice0 

      

= Imports orts) Iprice(Imp (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 p rice, and {Imports0, Iprice0} is a point on the applicable quantity-price curve. First the range of the imports-price curve is defin ed using



### range)
- (1 Imports0 I min 

### ⋅ = (2.3.6)



### range) (1 Imports0 I max 

### +

⋅ = (2.3.7)

where
range is a user-supplied parameter with value between 0 and 1. This parameter defines the interval I min - I max 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 I min - I max. On the other hand a smaller value of range can increase precision of the stepwise approximatio n. In ISIS a value of 0.5 is used for range
. Next, a series of steps is defined within the inter val I min - I max with the width of each step given by



### steps of NumberII Iwidth min

max 

### = (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, b ut will increase the model size. In ISIS, a value o f 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 (2 Iwidth I Imports min

istep 

### ⋅

⋅ + = (2.3.9)2-10

Using the above information, the imports-price curv e is determined

α 1 istep istep 

### Imports0

Imports Iprice0 Imports Iprice        

= 

### ) ( (2.3.10)

Equation (2.3.10) is used to generate the imports-c ost curves for all time periods, origins of imports , and import districts. However, this equation needs spec ifications 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, origi ns of imports, and import districts. 

### 2.4 Production Capacity and Supply Costs The ISIS model includes constraints for ensuring th at endogenous production capacity changes occur in a realistic way. This section describes how the capac ity changes take place in the ISIS framework and th e treatment of related costs. Note that various cost elements (e.g., capital cost of a unit [$/ton clink er] in the cement sector) are escalated appropriately to refle ct 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,



## { }

. iNew(i) iExp(i) irepl(i) i ants); techlifepl (vyear(v) tyear(t) vyear(v) ;i), capacity(t

∪ ∪ ∈ +

< ≤ ⋅ +

− = 

# ∑

v 

### ) tcap(t,i,v i) timeblock, addcap(t ) addcap(t,i (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 peri od starting when the vintage comes on line and endi ng 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., replac ement units, expansion units, and new capacity) and projected units, (2) annual fixed operation and mai ntenance (FOM) costs, (3) annual variable costs associated with use of labor, raw material, fuel, e lectricity, and operation and maintenance, (4) annu al transportation costs, and (5) annual cost of import s. These costs are described below. Capital Recovery If the existing units are paid for and do not have capital recovery costs, then



## { }

. iE(i) i ∈ = ;0 st(t,i) plantcapco (2.4.3)

where plantcapcost(t,i) is the annual capital cost of a unit. For projected units, for which startup date is know n, annual capital cost is given by


## { }

. ; iP(i) i nts); ecolifepla (tstart(i) tyear(t) tstart(i) i) t, pcapcostt( i), capacity(t CRFplant

∈

+ ≤ ≤ ⋅

⋅ = st(t,i) plantcapco (2.4.4)

where pcapcostt(t,i) is the capital cost (e.g., $/ton of clinker for th e 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 exi sting and projected is 

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

capcostt(t ) ,( (2.4.5)2-12

where capcostt(t,i) is the capital cost (e.g., $/ton of clinker for th e cement sector) for the i th unit . Variable Costs
The annual variable cost at a unit is calculated us ing



## [ ]

. ) ,( ) ,( 

# ∑ + + + ⋅ + + + =

sw 

### (t,i,SW) SWdispcost ) tncost(t,i H2Oconsump it fc it prodn i) varcost(t, i) ELCt(t, i) LBRt(t, i) VOMt(t, i) RMTt(t, (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 pr oduct) 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 uni t i in year t.
The fuel cost for a unit is calculated as follows:



### . 1 ]] pollutants

of Number f)i,t, fuelcostt( y(i) [eintensit

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



### ⋅ ⋅ ⋅ = 

# ∑ ∑

,ee)

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

(2.4.7)

where eintensity(i) is the energy intensity (MMBtu/ton product) for un it 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 

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

With annual water consumption given by2-13 

### ). ,( 2 it prodn (t,i) Oconsumptn H
⋅

= ty(i) H2Ointensi (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 

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

With annual solids discharges given by 

### ). ,( it prodn sw) SWgen(t,i,
⋅

= sw)

on(i, SWgenerati (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 produc t 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



### . i) varcost(t, st(t,i) plantcapco ) dncost(t,i tannualpro +

= (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


## ( ) )]. , , ,( [

, isteps id oi t Imports st(t,id) timportsco

isteps oi 

# ∑

⋅ + + = d) handling(i (id) InsFreight isteps) id, oi, Iprice(t, (2.4.13)

where Iprice(t,oi,id,isteps) is the price (value) of importing commodity from o rigin oi (foreign country) at the import district dc , at the istep th level of the relevant price-import curve. InsFreight(id) is the insurance and freight at the import distric t.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



### )]. , ,( )] ,,(
dc idt quantity Imported dc it quantity prodn (t,dc) ttranscost

id i 

# ∑ ∑

⋅ + ⋅ = dc)

id, (t, sportcostt [imprttran dc) i, (t, sportcostt [prodntran

(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., CO 2 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 ass ociated with uncontrolled emissions, controlled emissions, pollution prevention from process modifi cations and energy efficiency measures, and any controls-related effects (e.g., generation of CO 2 in a wet SO 2 scrubber). These algorithms are described below.



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

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

− ⋅ ⋅ = (2.5.1)



### ee). , eHIdispl(i k) Ichange(i, secondaryH k) hange(i, primaryHIc (i) eintensity ee)

k, ity(i, modeintens

+ +

+ =

(2.5.2)

where polfuel(t,i,f,j,k,ee) is emission (tons pollutant per ton product) of po llutant 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., Be st Available Control Technology [BACT]) are already installed on the unit;2-15

modeintensity(i,k,ee) is the modified energy intensity (MMBtu per ton pro duct) 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) ne eded 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 k)/100 rcent(i, MTchangepe secondaryR k)/100 ent(i, changeperc primaryRMT 1 j) m, yrmt(i, emintensit ee) k, j, m, i, polrmt(t,

− ⋅       + + ⋅

=

(2.5.3)

where polrmt(t,i,m,j,k,ee) is emission (tons pollutant per ton product) of po llutant 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 con trol k at unit i; and secondaryRMTchangepercent (i,k) is the percent change in raw material input (tons r aw material per ton product) due to any secondary raw material addition resulting from application of control k at unit i

. Finally, 

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

= (2.5.4)

where2-16

pol(t,i,f,m,j,k,ee) is emission (tons pollutant per ton product) of po llutant 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 

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

# ∑

⋅ = ee)

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

### ee)

k, j, m, f,i, pol(t, ee k j m f it varprodn j it emissions (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 ee th 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 cli nker) 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 

### j

it emissions j t ions totalemiss (2.5.6)

where totalemissions(t,,j) are total emissions (tons of pollutant per ton of c linker) of pollutant j resulting from production activity in the entire sector. Note that production associated with each pollutant is the same and therefore



### ; ); , , ,,( ) , , ,,( jj j jj m f it prodnpol j m f it prodnpol ≠

= (2.5.7)

with, 

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

# ∑ =
ee

j k it poltikjee 

### ee k j m f it varprodn j m f it prodnpol (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 t hat endogenous applications of sector-based control s and energy efficiency options occur in a realistic manner. A description of these constraints and cost s for2-17

controls is presented in this section. The treatmen t 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.

. ntrols(k)] techlifeco + [(vyear(v) tyear(t) vyear(v) ;1

< ≤ ≤ 

# ∑

v t ,v)

t,i,k c(poltik ts

, )

(

_ (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 equation s. For t = 1,

. ntrols(k)) techlifeco (vyear(v) tyear(t) vyear(v) ;i), capacity(t ,
_ ,
+ < ≤ ⋅ = 

## ∑

v v) c(t,i,k ts k) (t,i ControlCap (2.6.2a)

For t > 1,



### . ntrols(k)) techlifeco (vyear(v) tyear(t) vyear(v) ;i), capacity(t ,
_ , ,
+ < ≤ ⋅ +

− = 

# ∑

v 

### v) c(t,i,k ts k) i timeblock, (t ControlCap k) (t,i ControlCap (2.6.2b)

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

### ) ,( ) ,,( ) ,,( it capacity kk it ControlCap k it ControlCap ≤

+ (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 th e control is installed on,2-18 

### tyear(t). vyear(v) ;i), capacity(t

_ pollutants of Number )

, , ,, (



### ≤ ⋅

≤ 

# ∑ ∑

v

ee j k it jee f,m,poltik 

### c(t,i,k,v) ts ,ee) ,i,f,m,j,k varprodn(t

(2.6.3)

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

. In general, the costs associated with controls comp rise the following components: (1) capital and fixe d operation and maintenance costs, (2) costs associat ed with any reagent and/or catalyst consumption, (3 ) costs associated with any reduction in fuel and/or raw material use, (4) cost associated with electric ity consumption, (5) cost associated with byproduct(s), and (6) costs associated with water use. The calculations of these costs are described below. No te that various cost elements (e.g., capital cost o f a control [$/ton clinker] for the cement sector) are escalated appropriately to use values in years of i nterest. Capital Recovery and Fixed Cost Annual recovery of capital cost of control k is given by



### trols(k)]. ecolifecon [vyear(v) tyear(t) vyear(v) ; (k) CRFcontrol k) i, alcostt(t, cntrlcapit _

+

< ≤ ⋅ ⋅ = (t,i,k) ControlCap , i,k)) c(poltik(t capcost

(2.6.4)
Similarly, annual FOM cost is given by



### . ntrols(k)] techlifeco [vyear(v) tyear(t) vyear(v) ; k) i, costt(t, cntrlfixed ) (

_

+ < ≤ ⋅ = (t,i,k) ControlCap i,k)

poltik(t, c FOMcost

(2.6.5)

where cntrlcapitalcostt(t,i,k) is the capital cost ($/ton of clinker) of applicat ion of control k on i th unit , cntrlfixedcostt(t,i,k) is the annual fixed operation and maintenance (FOM ) cost ($/ton of clinker) of application of control k on i th unit , capcost_c(t,i,k) is the annualized capital cost ($) of k th control application on i th unit, FOMcost_c(t,i,k) is the annual fixed operation and maintenance cost ($) 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

. ] [

) , ,, ( ) , ,, ( , ) ,(

pollutants

of Number f) r, t, fuelcostt( k) Ichange(i, secondaryH f r, t, fuelcostt( k) hange(i, primaryHIc 

# ∑

∑

⋅ ⋅ + ⋅ ⋅ =

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

k it poltikjee ,m f i fi ,ee) ,i,f,m,j,k varprodn(t )

,ee) ,i,f,m,j,k varprodn(t i,r),k)) tirk(t,ri( ange_c(pol fuelcostch

(2.6.6)

where primaryHIchange(i,k) is the primary heat input change (MMBtu per ton cli nker) with use of k th technology, secondaryHIchange(i,k) is the primary heat input change (MMBtu per ton cli nker) 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 bet ween kiln and their geographic locations, and kf(k,f) is a set with elements corresponding to mapping bet ween fuels and technologies. poltirk(t, ri(i,r),k) is a mapping described above. Change in raw material cost associated with applica tion of controls is given by

. ] 100 / 100 / [

) , ,,( ) ,( )

, ,,( ) ,(

pollutants of Number i) RMTt(t, rcent MTchangepe secondaryR i) RMTt(t, ent changeperc primaryRMT 

# ∑ ∑

⋅ ⋅ + ⋅ ⋅ =

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

k it ee ,m,poltikj f i fi ,ee) ,i,f,m,j,k varprodn(t ,ee) ,i,f,m,j,k varprodn(t ,r),k)) irk(t,ri(i nge_c(polt RMTcostcha

(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 correspond ing 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 c linker) in year t.2-20

Annual reagent consumption costs are



### rgnt). cet(t, reagentpri


### ⋅ = 

# ∑
rgnt 

### rgnt

k it c pt rgntconsum k it poltik c ptcost rgntconsum ) , ,,(

_ ))

,,( (

_ (2.6.8)
The annual consumption of a reagent given by

. ] [ ),

,,(

_

) , , ,, ( ) , ( , ) , , ,, ( ) , (

pollutants of Number rgnt) j, k, m, , sumptrmt(i reagentcon pollutants of Number rgnt) j, k, f, i, sumptfuel( reagentcon

] ,ee) ,i,f,m,j,k varprodn(t [ ,ee) ,i,f,m,j,k varprodn(t rgnt) k i t c(poltik pt rgntconsum

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

# ∑ ∑ ∑ ∑

⋅ + ⋅ =

(2.6.9)

where reagentconsumptfuel(i,f,k,j,rgnt) is the reagent consumption due to control (tons re agent/tons clinker) associated with fuel-based emission intens ity, reagentconsumptrmt(i,m,k,j,rgnt) is the reagent consumption due to control (tons re agent/tons clinker) associated with raw material-based emissio n 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 

### cat

k it c t catconsump )

k i t poltik c tcost catconsump cat ice(t, catalystpr (2.6.10)

with2-21 . ) ,,(

_

) , , ,,( ) ,( pollutants of Number EGFW(i) cat) j, k, nsumpt(i, catalystco 

# ∑ ∑

⋅ ⋅ =

j ee j k it ee ,m,poltikj f i fi ,ee)

,i,f,m,j,k varprodn(t 10000 ,cat)

k i t c(poltik t catconsump

(2.6.11)

where catalystconsumpt(i,k,j,cat) is the catalyst consumption rate (ft 3 catalyst/10000 ft 3 flue gas), EGFW(i) is the exhaust gas flow rate (scf/ton clinker), catalystpricet(t,cat) is the price of the catalyst ($/ft 3 ) in year t , catconsumpt_c(t,i,k,cat) is the catalyst consumption rate (ft 3 /year), and catconsumptcost_c(t,i,k) is the catalyst cost ($/year). Annual cash flow associated with byproduct generati on disposal/sale is



### byprod); ricet(t, byproductp


### ⋅ = 

# ∑ byprod 

### prod)

c(t,i,k,by en byproductg )

k it c(poltik ost byproductc

_ )

,,(

_ (2.6.12)
with annual generation of a byproduct given by

. ) ,,(

_

) , , ,, ( ) ,( )

, , ,, ( ) ,(

pollutants of Number byprod) j, k, m, rodnrmt(i, byproductp pollutants of Number byprod) j, k, f,, rodnfuel(i byproductp

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

k i t c(poltik en byproductg

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

# ∑ ∑ ∑ ∑

⋅ + ⋅ =

(2.6.13)

where byproductprodnfuel(i,f,k,j,byprod) is the byproduct generation due to control (tons r eagent/tons clinker) associated with fuel-based emission intens ity, byproductprodnrmt(i,m,k,j,byprod) is the byproduct generation due to control (tons r eagent/tons clinker) associated with raw material-based emissio n 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 cont rol is

. ] [ ) ,,( )

,( ) , , ,,( , pollutants of Number k) f, i, kWhperton( 

# ∑ ∑

⋅

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

,i,f,m,j,k varprodn(t ) k i t ik kwh_c(polt (2.6.14)
The cost of electricity consumption is 

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

,,( (2.6.15)

where KWhperton(i,f,k) is the electrical requirement (kWh per ton of clin ker) 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

) , , ,,( ) ,( )

, , ,,( ) ,(

pollutants of Number k) m, i, H2Ousermt( pollutants

of Number k) f, (i, H2Ousefuel

] ,ee) ,i,f,m,j,k varprodn(t [ ] ,ee) ,i,f,m,j,k varprodn(t [ ,i,k)) c(poltik(t Oconsumpt H

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

# ∑ ∑ ∑ ∑

⋅ + ⋅ =

(2.6.16)
The cost of water consumption is

. 1000 / ) H2Ocostt(t
⋅ = t_c(t,i,k) H2Oconsump ,k)) poltik(t,i H2Ocost_c( (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/ye ar).
Total Annual Cost Using the above costs, the total annual cost of con trols is



### t,i,k) H2Ocost_c( t,i,k) kWhcost_c( k) ost_c(t,i, byproductc i,k) tcost_c(t, catconsump ,i,k) ptcost_c(t rgntconsum k) nge_c(t,i, RMTcostcha ,k) ange_c(t,i fuelcostch t,i,k)

FOMcost_c( t,i,k) capcost_c(

st(t,i,k) tcontrolco

+ + + + + + + + =

(2.6.18)



### 2.7 Costs of Energy Efficiency Measures As described below, the treatment of energy efficie ncy measures in ISIS is similar to that for control

. Energy Efficiency Measures Related Constraints As for controls, constraints are needed to ensure r ealistic 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 th e vintage comes on line and ending with its technical life.



### (ee)]. eetechlife + [(vyear(v) tyear(t) vyear(v) ;1



### < ≤ ≤ 

# ∑

v t 

### ,v)

t,i,ee ee(poltiee ts

, 

### )

(

_ (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,

e)). techlife(e (vyear(v) tyear(t) vyear(v) ;i), capacity(t ), (

_ ,

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

v +

< ≤ ⋅ = 

## ∑ (2.7.2a)

For t > 1,



### e)). techlife(e (vyear(v) tyear(t) vyear(v) ;i), capacity(t ), (

_ , ,

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

v 

### + < ≤ ⋅ +

− = 

# ∑ (2.7.2b)

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

# [ ] 1 ; ) ,,(

_ ) ,,(

_ 

# ∑ ≤ +

v 

### ,v) eee it ee(poltiee ts ,v) ee it ee(poltiee ts (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,



### tyear(t) vyear(v) i); , capacity(t pollutants of Number ≤ ⋅

≤ 

# ∑ ∑

v j,ee k i jee(t f,m,poltik 

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

_ ) , ,,
(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 ar e given by Equations 2.7.4 and 2.7.5, respectively.



### ee)]. ecolifeee( [vyear(v) tyear(t) vyear(v) ; CRFee(ee) ee) i, stt(t, ecapitalco _

+

< ≤ ⋅ ⋅ = ee) EECap(t,i, (t, i,ee)) ee(poltiee capcost
(2.7.4)2-25 

### (ee)]. eetechlife [vyear(v) tyear(t) vyear(v) ; ee) i, t(t, efixedcost ) (

_

+ < ≤ ⋅ = ee) EECap(t,i, i,ee)

poltiee(t, ee FOMcost

(2.7.5)

where ecapitalcostt(t,i,ee) is the capital cost ($/ton of clinker) of ee th energy efficiency measure application on i th unit , efixedcostt(t,i,ee) is the annual fixed operation and maintenance (FOM ) cost ($/ton of clinker) of ee th 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



### pollutants of Number f r, t, fuelcostt( ee) , eHIdispi(i 

# ∑

⋅

⋅ =

) , , ,, ( 

### _

ee

j k it jee f,m,poltik 

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

),ee)) e(t,ri(i,r ee(poltire ange fuelcostch

(2.7.6)

where eHIdispi(i,ee) is the displacement of primary heat input (MMBtu pe r ton clinker) with use of ee th 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 bet ween 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 e nergy efficiency measure is2-26 

### pollutants of Number ee) i, kWhperton( 

# ∑

⋅

= ) , , ,, ( , , 

### ) ,,(

_ ee j k it poltikjee m f 

### ,ee)

,i,f,m,j,k varprodn(t e ) ee it ee(poltiee kwh (2.7.7)
The cost of electricity consumption is 

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

,,( (2.7.8)

where eKWhperton(i,f,ee) is the electrical requirement (kWh per ton of clin ker) 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 ene rgy efficiency measures is



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

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

scost(t,i, teemeasure

_

_ _ _ + +

+ =
(2.7.9)



### 2.8 Policy Options ISIS can help design and evaluate a number of emiss ions reduction policy options including cap-andtrade, emissions taxes, and emissions limits. Addit ionally, appropriate combinations of these options can also be evaluated. The policy options included in I SIS are described below. Cap-and-Trade Under this option, an emissions cap is set on the a mount of a pollutant that can be emitted. Sources, companies, or other groups are issued emission perm its (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 thos e banked in previous periods cannot exceed the cap in the current period. Sources or companies that need to increase their emissions must buy allowances fro m 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 rew arded for having reduced emissions by more than was needed. Thus, in theory, those that can easily redu ce emissions most cheaply will do so, achieving the pollution reduction at the lowest possible cost to society. Generally, annual caps have been utilized in ongoin g programs (e.g., Title IV SO 2 reduction program). However, ISIS does permit evaluation of potential p rograms with caps over user-selected time periods (e.g., 5-yearly caps). This evaluation is accomplis hed using, ) , ( ) ,( ) ,( j period t bnk j t bnk j t ions totalemiss +

− + ≤ j) ec(t, (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 requireme nt can be imposed using j) i,, ecpminer(t j) i,t, emissions( ≤ (2.8.2)

where ecpminer(t,i,j) is the unit-specific minimum emission reduction re quirement for pollutant j in year t

. Emissions Limits ISIS permits evaluation of the costs and emissions reductions associated with more traditional emissio n reduction programs utilizing unit-specific rate-bas ed emission limits. Such requirements are imposed using ) ,( ) ,,( it

prodn j it emissions
⋅

≤ j) el(t, (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 endogeno usly determining the level of emission reduction corresponding to a certain allowance price. This in formation may be useful, for example, in a situatio n where an allowance price is set for reducing emissi ons from many industrial sectors. In such a case th e levels of emission reductions corresponding to the same allowance price may be different for the secto rs under consideration. The emissions response to a gi ven allowance price is driven by the following term in the objective function (see section 2.1), ) ,( ) (

, j t ions totalemiss t dis j t 

# ∑

⋅ ⋅ 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 i s equivalent to emission-tax-based programs in whic h affected units or companies pay a tax for every uni t of pollution they produce. Thus this scheme can a lso 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 contr ols required to meet the constraints associated wit h commodity demand and emissions, while maximizing to tal surplus. Once the surplus maximization problem has been solved, the results are post-proce ssed to obtain parameters and level values of the variables of interest. The key variables of interes t are: production level of each production unit to meet regional demand, level of imports in each region, i nstallation of various controls, emissions, and var ious costs. Output data are written in appropriate works heets in an Excel workbook and further linked to various plots to enable visual presentation and ana lyses of the results.2-29



### References for Chapter 2 Duloy, J. H.; Norton, R.D. (1975). Prices and incom es in linear programming models. American Journal of Agricultural Economics . 57(4): 591.600. Enke, S. (1951). Equilibrium among spatially separa ted markets: solution by electric analogue. Econometrica . 19: 40.47. FPL-PELPS, A price endogenous linear programming sy stem 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 a nd 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 pol icy and economic parameters. The cement-specific data r equirements are discussed below. 

### 3.2 Cement-Specific Data The inputs to the ISIS model for the cement industr y can be broadly categorized into three main components:

• Industry production, fuel, and emissions

• Control technologies, and emission abatement approa ches

• 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 t o 2012, as shown in Table 3-1 (PCA, 2009a). Additionally, the model also includes potential kil n 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 Cem ent 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 l ocation, design (i.e., wet, dry, preheater, or prec alciner), daily and annual clinker capacity 3 , vintage, and retirement information when availabl e (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 n ormal downtime days, where daily capacity is the normal clinker output a kiln can produce per day given a realistic work pa ttern and normal downtime days are number of days of downtime required for ma intenance, 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 materia ls, 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 up keep of the kiln. Labor is used in the quarry, in t he 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 m aterials, labor, repair and maintenance, and electricity was determined following the methodolog y 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-spec ific demand centers. Figure 3-1 shows the distribution of portland cement kilns operating in 2009. Each state containing at least one kiln is sh aded. The ISIS-cement model simulates each cement plant’s ability to compete in each of the demand centers a s a function of the plant’s production cost and trans portation cost associated with supply to each deman d center. 3.2.1.4 Portland Cement Demand One of the key data inputs for the ISIS-cement mode l is the demand projection for each demand center. In general, this demand is a function of gross domesti c product (GDP) growth, interest rates, special construction projects (e.g., highways), and public sector construction spending. Portland cement deman d was 128 million metric tons in 2005. PCA expects ce ment demand will reach 192 million metric tons by 2035, which reflects an increase of nearly 64 milli on metric tons with a compound annual growth rate o f 1.4 percent. Cement demand through year 2035 is rep orted in the PCA Long-Term Cement Consumption Outlook (PCA, 2009b). PCA projections of cement dem and (in million metric tons) by state for 2005 through 2035, in 5-year increments, are shown in Ta ble 3-2.3-3

Figure 3-1. Distribution of Cement Kilns in the Uni ted States as of 20093-4

Table 3-2. Portland Cement Demand in Millions of Me tric Tons State Demand Center 2005 2010 2015 2020 2025 2030 2 035 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 4.083-5

State Demand Center 2005 2010 2015 2020 2025 2030 2 035 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 kil n and import district locations to demand centers. To dev elop these costs, information on distances between supply and demand points and costs of transportatio n modes (truck, rail, or water transport) was obtai ned. In particular, the Transportation Routing Analysis GIS (TRAGIS) model (TRAGIS, 2003) was used to develop the origin-destination distances as describ ed below. Also, in the matrix, the applicable lowes t cost transportation option is used to connect a sup ply point with a destination. While the cement dema nd centers are interlinked through a transportation ma trix, the competition is generally maintained on a regional level because the cost of transporting cem ent is relatively high. As mentioned above, the TRAGIS model was used to ca lculate transportation distances associated with delivery of portland cement from domestic U.S. manu facturing sites and import terminals to each of the demand centers. Operation of the TRAGIS program req uires the identification of a shipment’s origin and destinations, as well as the selection of mode of t ransportation (highway, rail, or water) for each or igindestination pair. The TRAGIS program does not use a ctual 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. citi es and towns. The locations of domestic portland cement manufacturers used correspond to the lists o f manufacturing plants published in the Cement3-6

Americas (Penton Media Inc.), 2009; North American Cement Directory (2008) (Cement Americas 2008); and the Portland Cement Association, U.S. an d 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 shipment s reach customers via terminals rather than direct. S hipments to terminals are more than 80 percent by r ail 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, transporta tion 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 origin

destination pair, the dominant mode (lowest cost of shipment) is used to determine the transportation cost for that route. The TRAGIS model was used to arriv e 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 To n per Mile)

Journal of Transport Geography Report, 2005 N&S American Cement, 2004 Mode BTS 1 WDOT 2 NACT 3 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 c overs 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 distric ts (USGS, 2007b). In ISIS-cement, international supplies from exporting countries to U.S. import di stricts are modeled using a supply elasticity and t hen these imports are transported to the demand centers

. The five largest international suppliers of cement and clinker to the U.S. are China, Thailand, Venezu ela, South Korea, and Greece. An econometric study was c onducted to provide an estimate of international supply elasticity for supplies from these countries and the rest of the world. The results of this stu dy (Burtraw, 2010) reflected that the best estimate of the international supply elasticity of cement and clinker from China, Thailand, Venezuela, South Korea, Greec e, and the rest of the world into the U.S. is 3.94. This value indicates that if the price of cement we re to increase by 1 percent within any import distr ict in the U.S., then, ceteris paribus , the quantity of cement imported from each of thes e five supply countries into that district would increase by 3.94 percent. Table 3-4 shows the import levels by USGS Customs D istrict, then state, for 2005, estimated by using USGS data (USGS, 2007b).3-7

Table 3-4. Portland Cement and Clinker Imports in M illion 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 Orleans a

1.75 Louisiana, New Orleans a

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 1.13-8

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 Bi rmingham and Dallas markets using baseline domestic production levels. Source: USGS, 2007b. Data do not include Puerto Ric o
3.2.1.7 Capacity Changes Cement plants have a relatively long lifespan, typi cally 50 years or more (FLSmidth, 2007). Various factors, including (but not limited to) raw materia l availability in the quarry, technology changes, productivity, efficiency, longevity, reliability, m aintenance, and long-term costs can affect the life span of a cement kiln/plant. In ISIS-cement, retirements fr om 2002 to 2009 and projected retirements after 200 9 of existing kilns were based on information from PC A 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 k iln capacity changes, an economic life of 25 years and an interest rate of 15 percent are used. Capital co sts 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 20 05 (EIA, 2008) is shown in Figure 3-2. In 2005, the cement sector consumed 451.2 trillion Btu (476.0 tr illion kJ) of energy (EIA, 2008). As shown in Figu re 3-2, the primary fuel being burned in kilns is coal . Coal is projected to remain the dominant fuel use d by the U.S. cement industry. However, there has been a n increasing trend towards using other fuels, particularly alternative fuels, such as coke, 4 waste tires, and other wastes, especially oily was tes.

4 PCA does not specify if “coke” is metallurgical co ke or petroleum coke. Authors believe it is the lat ter.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. Cem ent Industry in 2005. Source: EIA, 2008
In the ISIS-cement model, to determine the fuel int ensity of each kiln, correlations of kiln type to h eat 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 k iln. PCA’s data on heat input to various kilns by t ype were used to develop kilns’ fuel intensities. The d ata, expressed in heat input per unit of clinker (s pecific 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 Exha ust Gas Flow Rate (wet) for Various Kiln

Types SFC a EGFW b Kiln Type MMBtu/ton a Nm 3

/kg SCF/ton c 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, 200 7 (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 cos t of the chosen fuel and the kiln’s specific fuel intens ity.
3.2.1.9 Emission Intensities The design of the ISIS Model can accommodate any nu mber of pollutants of interest. In ISIS-cement, each kiln is characterized by its NO X , SO 2 , PM, hydrochloric acid (HCl), mercury, total hydro carbon3-10

(THC), and CO 2 emission intensities. These emission intensities w ere developed using available data (Andover Technology Partners, 2009a).
NO X emissions from cement kilns result primarily from the following combustion process: oxidation of fuel nitrogen (fuel NO X ) and the oxidation of nitrogen in the combustion a ir (thermal NO X ). Oxidation of nitrogen in the feed materials (feed NO X ) can also influence total NO X emissions. Table 3-6 shows NO X emission intensities for cement kilns in lb/ton of clinker and in lb/MMBtu (EPA, 2007).
Table 3-6. Estimated Uncontrolled NO X Emission Intensities for Cement Kilns
Uncontrolled NO X 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)

SO 2 emissions from cement kilns are the product of sul fur in the fuel as well as sulfur in the feed materials. Sulfur in the fuel will oxidize to SO 2 during pyroprocessing, and a significant amount is likely to be captured in the form of sulfates as the gas p asses through the calcination zone. Compared to lon g dry and wet kilns, preheater and preheater/precalci ner kilns tend to be more effective at capturing fu elgenerated SO 2 . Accordingly, oxidation of sulfur in the feed mate rials is likely to be a major component of total SO 2 emissions. Table 3-7 shows average SO 2 emissions for each kiln type for each state (Andov er Technology Partners, 2009a). State-specific emissio n intensities of SO 2 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 a ssigned to kilns in that state.
CO 2 emissions from cement kilns result from limestone calcination and fuel combustion. Appendix A explains how CO 2 emission intensities from calcination and combusti on are calculated in the ISIS model. Calcination releases 0.52 tons of CO 2 per ton of clinker produced, while fuel-based CO 2 emission factors range from 199.52 lb CO 2 /MMBtu for coal to 105.02 lb CO 2 /MMBtu for natural gas (Andover Technology Partners, 2009b, 2010a, and 2010b). Tabl e 3-8 shows approximate CO 2 and H 2 O produced from combustion for the most important fuels for ce ment kilns.3-11

Table 3-7. Average SO 2 Emissions for Each Kiln Type in Each State SO 2 (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 7.813-12 SO 2 (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 CO 2 and H 2 O Produced from Combustion of Fuels

Variable Name Flue Gas Tires Petroleum Cokes Heavy

Fuel Oil Rosemont

PRB a Logan,

WV BIT b Natural

Gas

LBMCO2MMBTU lbmoles

CO 2 /MMBtu 4.26 4.83 3.85 4.25 4.53 2.39
LBCO2MMBTU lb CO 2 /MMBtu 187.44 212.56 169.32 186.83 199.52 105.02
LBMH2OMMBTU lbmoles

H 2 O/MMBtu 2.76 2.76 2.23 3.23 4.14 2.77
LBH2OMMBTU lbs H 2 O/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 coal3-13



### 3.2.2 Control Technologies and Emission Abatement Approac hes ISIS-cement contains information on abatement appro aches for NO X , SO 2 , PM, HCl, mercury, THC, and CO 2 emissions described above. The three categories of abatement approaches included are: process modifications and upgrades, raw material and/or fue l substitution, and mitigation technologies. For ea ch emission abatement approach, where possible, inform ation on the following parameters was developed (Andover Technology Partners, 2009b and 2009c) and included in the model: capital cost, fixed operatin g cost, variable operating cost, emission reduction p erformance for all of the pollutants, impacts on fu el and/or raw material use, impact on electricity cons umption, byproduct generation and cost, and impact on water use. To estimate capital recovery factors for capital co sts associated with control technologies, economic life values of 15 and an interest rate of 7 percent are generally used, but different values can be selecte d by the user. Payback periods and technical life for th e energy efficiency measures shown in Tables 3-14 through 3-17 are given in Worrell and Galitsky (200 4). Economic life for each of these measures can be taken to be the average of the technical life and t he payback period. Again, an interest rate of 7 per cent can be used for capital recovery in the absence of more specific information. Tables 3-9 through 3-12 show the NO X , SO 2, CO 2 , 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. Multimed ia impacts of changes to the capacity of cement kil ns 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 P lant-Wide Measures, respectively. More details on cost, efficiency, and co-impacts fo r 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 evaluatio n. 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 with out banking of emissions. Further, a cap-and-trade poli cy 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 inter est. Furthermore, traditional policy scenarios (rate-bas ed policies) with unit specific emission reduction requirements specified by the user can be modeled i n ISIS. The user can specify the policy horizon (time perio d) to be used for the model runs. Since climate-rel ated simulation horizons can be long (e.g., 40 years), t he user may choose to run ISIS with blocks of years (e.g., 5-year blocks). The simulation horizon and b locks 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 eco nomic parameters are used: discount rate, escalatio n rates, and demand elasticity. For the ISIS-cement m odel, the default discount rate has been chosen as3-14

7-percent, as recommended by the U.S. Office of Man agement and Budget (OMB) for project evaluation (OMB, 1992). Escalation rates used can be found in the “ISIS_Inputs.xls” workbook. Escalation rates fo r labor are based on historical data from Bureau of L abor and Statistics (BLS). Raw materials escalation factors are calculated from historical price data o f crushed sandstone and gypsum from USGS. The escalation factors for variable operating and maint enance costs are based on Chemical Engineering Inde x. Escalation factors for various fuels and electricit y 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).3-15Table 3-9. NOXControl Technologies for Cement KilnsImpact on Emissions,±% changeElectricity Consumption,kWh/ton of cementControl TypeNOXSO2PM Hg Other GrindingKiln OperationWaterConsumption,gal/ton ofcementLow NOXBurners –Indirect Firing-20% to -30%1No impact203-1.2403Mid KilnFiring-Tires-20% to -40%1May vary5030603Low NOXBurner + MidKiln Firing- Tires-20% to -40%1May vary503-1.24,603Low NOXBurners + TireDerived Fuel-20% to -40%7May vary503-1.24,603Low NOXBurner +Selective Non CatalyticReduction-50%2No data5No data8No data8Nodata80 -1.24,9+1.259Low NOXBurner +Selective CatalyticReduction-90%2Oxidation10No data8Oxidation10Nodata80 -1.24,9+1.259Low NOXBurners +CemStara-30%11-1.3 (wetprocess);-1.9 (dryprocess)12-1.24,12from LNBand-1.5 (wet process)or-2.2 (dry process)from CemStar130CemStar/Fly Ash Injection-20%14-1.3 (wetprocess);-1.9 (dryprocess)13-1.5 (wet process)or-2.2 (dry process)from CemStar130Notes to Table 3-9 on following pages3-161.See EPA (2004), Table 5-1.2.See Andover Technology Partners 2009b memo.3.These technologies do not use and do not affect theconsumption of water in raw mix preparation or significantly affect electric powerconsumption in cement manufacturing processes.4.Conversion from direct firing systems typical of wet and dry process kilns and older preheater kilns to indirect firing systems, required toimplement low NOXburner (LNB) technology, could result in reductions in primary air fan and kiln-induced draft fan power requirements andconcomitant slight increases in coal conveying power requirements. A reduction in fan/blower power onthe order of 100 hp might beanticipated for a moderately sized (300,000 – 500,000 clinker tons per year) kiln converted from direct firing to indirect firing. No powersavings on adding an LNB would be anticipated if the kiln system is already indirect-fired.5.SO2emissions from cement kilns are strongly related to fuel and raw materials sulfur content and to method of kiln operation. Sulfur contentof TDF (typically, 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuelscommonly used in cement kilns, suchas coal or coke. Therefore it is not practical torelate SO2emissions to the use of these NOXcontrol methods. With respect to use of an SO2wet scrubber using ground limestone, it is assumedthat, for wet process kiln systems, uncontrolled SO2emissions of 8.2 lb per ton of clinker(8.9 lb/ton of cement) (EPA, 1995: Table 11.6-8) are treated by use of a stoichiometric amount (with respect to uncontrolled SO2) of limestoneof 90-percent purity in a 15-percent limestone slurry. Limestone and water consumption are 15.5 lb and87.7 lb, respectively, per ton ofcement produced. For a precalciner kiln, it is assumed that uncontrolled SO2emissions of 1.1 lb per ton of clinker (1.2 lb/tonof cement) (EPA,1995: Table 11.6-8) are treated similarly (stoichiometric amount limestone of 90% purity in a 15% limestone slurry). Limestone and waterconsumption 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 affectedby introduction of tires or tire-derivedfuel.7.Combined effect will vary. Low NOX20 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 pollutants in connectionwith NOXemission controls, statistics on such effects arenot available. See, generally, EPA (2007a), Chapter11.9.Assumes SNCR or SCR both with stoichiometric addition of NH3in 20-percent solution, typical precalciner NOXemissions of 4.2 lb/tonclinker before treatment (EPA 1995, Table 11.6-8),and 0.92 tons of clinker being used in each ton ofcement. It should also be noted thatSNCR is not applicable to wet process, long dry process, and many preheater kilns because the kiln gasexit temperatures are too low fromthose units for the necessary reactions to take place. See EPA (2007a). SNCR is assumed to achieve 63-percent reduction in NOXemissionsfrom applicable kiln systems. See EPA (2007a), Chapter 8. There may be an attendant small increase inkiln system electric power inconnection with injection of water into the kiln system due to an increase in gas volume handled by the kiln’s fan system. The increase is notconsidered in the calculation.10.Typically, up to about 1 percent of SO2can be oxidized to SO3.Elemental form of Hg oxidized across SCR providedthere is sufficientconcentration 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 reducedraw mix preparation costs in theevent that unground CemStar material is fed to therotary kiln, and reduced fan power requirements resulting from reduced kiln gas volumes in3-17connection with both combustion gases and raw mix calcination. Assuming a 5-percent replacement of rawmix with carbonate-free CemStarmaterial, a concomitant 5-percent reduction in rawmix preparation energy is also assumed. An approximate 5-percent reduction in kilnelectrical energy consumption is assumed based on aroughly 5-percent reduction in kiln exit gases from both calcination and combustion.13.CemStar and fly ash injection are expected to havesimilar effects on raw mix preparation and kiln process electrical energy requirements.14.See NESCAUM (2000) for effect of CemStar on NOXreductions from cement kilns. Investigation of thecombined effects of multipletechnologies on pollutant emissions was not carriedout for this summary.General NotesA. Cement kiln processes do not use steam.B. Cement kiln dust (CKD) disposal rates as functions of NOXor SO2emissions control technology has not been reported. Typical CKD disposalrates range from 0.042 to 0.115 tons CKD/ton clinker (0.046 to 0.125 tons CKD/ton cement assuming 0.92tons clinker per ton of cement).C. CKD disposal costs vary widely by region, CKD characteristics, CKD volumes, and location of disposal site (on-site or off-site). In addition,disposal costs are expected to be of comparable cost to transportation costs in connection with off-site disposal. For example, prices at threenorthern California landfills range from $26/shortton to $69/short ton of CKD plus transportation, which may range from approximately $500to $1,200 per truck load. Pers. Comm., E. Leamer (ARCADIS) Feb. 26, 2009. CKD disposal prices would beexpected to be generallyunaffected by the type of cement kiln pollution controls employed.References for Table 3-9 (references called out inthe above notes can be found at the end of the chapter)Air Pollution Controls and Efficiency Improvement Measures for Cement Kilns, Prepared by ARCADIS UnderContract No. EP-C-04-023, March31, 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. 1992.3-18Table 3-10. SO2Control Technologies for Cement KilnsImpact on Emissions,±% changeElectricity Consumption,kWh/ton of cementControl TypeNOXSO2PM Hg Other Grinding Kiln OperationWaterConsumption,gal/ton ofcementWet Scrubber-90% to -95%1-80%-50% for THC-99.9% for HCl+0.2(wet processkilns)2+52+12 (wetprocess); +1.5(dry process)3Dry Lime Injection-50%4-75% for HCl555Notes to Table 3-101.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 limestone to below 45 to74 micrometers in particle size and pumping limestone slurry; and 2) increased kiln exhaust gas fan power requirements. The latter energyincrease is caused by increased pressure drop demand on kiln ID fans due to conveying kiln exit gasesacross the scrubber spray tower plusincreased gas volume demand on kiln ID fans due tothe addition of water in the limestone slurry. Slurry preparation and pumping is assumedto require 20 kWh per ton of limestone. The spray tower pressure drop is assumed to add 500 hp to ID fan requirements on a 300,000 to500,000 ton per year cement kiln process.3.SO2emissions from cement kilns are strongly related to fuel and raw materials sulfur content and to method of kiln operation. Sulfur contentof TDF (typically 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuelscommonly used in cement kilns, such ascoal or coke. Therefore it is not practical to relate SO2emissions to the use of these NOXcontrol methods. With respect to use of an SO2wetscrubber using ground limestone, it is assumed that, for wet process kiln systems, uncontrolled SO2emissions of 8.2 lb per ton of clinker (8.9lb/ton of cement) (EPA, 1995: Table 11.6-8) are treated by use of a stoichiometric amount (with respect to uncontrolled SO2) of limestone of90-percent purity in a 15-percent limestone slurry.Limestone and water consumption are 15.5 lb and 87.7 lb, respectively, per ton of cementproduced. For a precalciner kiln, it is assumed that uncontrolled SO2emissions of 1.1 lb per ton of clinker (1.2 lb/tonof cement) (EPA, 1995:Table 11.6-8) are treated similarly (stoichiometricamount limestone of 90% purity in a 15% limestoneslurry). Limestone and waterconsumption 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. SeeDry Sorbent Injection report (ARCADIS, 1992).5.Dry lime injection technology is not well developedfor the cement industry and no statistics are available.General Notes:See Table 3-9 notes3-19References for Table 3-10 (references called out inthe 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. ARCADIS, 1992.EPA (1995). Compilation of Air Pollution Emission Factors - Volume 1: Stationary Point and Area Sources. Fifth Edition, Supplements A-F, AP42. U.S. Environmental Protection Agency, ResearchTriangle Park, North Carolina.Table 3-11. CO2Control Technologies for Cement KilnsImpact on Emissions,+/- % changeControl TypeNOXSO2PM Hg CO2OtherElectricityConsumption,MWh/ton ofclinkerProcess WaterConsumption,ton/ton ofclinkerCooling WaterConsumption,ton/ton of clinkerSolvent CCS1New -99% -99% -85% -0.022 0.429 4.82Solvent CCS1Retrofit -99% -99% -85% -0.103 0.160 4.82Oxy-combustion -99% -99% -85% 0.174 14.3Notes to Table 3-111.CCS = carbon capture and sequestration.References for Table 3-11Andover Technology Partners,, Cost and Performanceof Controls, March 10, 2009.EPA, Report to Congress on Cement Kiln Dust, Chapter 3: CKD Generation and Characteristics; US Environmental Protection Agency: 1993.3-20

Table 3-12. HCl, Hg, and THC Control Technologies f or Cement Kilns Impact on Emissions, ± % change Control Type 1 NO X SO 2 PM Hg Other ACI 2 -99.9% -90% -50% for THC Membrane Bag -99.9% RTO 3 -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 technolo gies 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 Operation 1 Electricity Consumption, kWh/ton of cement Waste Kiln Type Water

Consumption, gal/ton of cement 2 Grinding 3 Kiln Operation 4 Generation

Rate, ton/ton of cement 5 Disposal Cost,

$/ton of cement Wet to Precalciner -214 2,6 -12 8 -7 8 -0.072 see notes 9,10 Long Dry to Precalciner No change 2 Not available 7 Not

available 7 -0.062 see notes 9,10

Preheater to Precalciner No change 2 Not available 7 Not

available 7 0 11 see notes 9,10

Notes to Table 3-13. 1. Unless specifically noted otherwise, impacts are pr esented 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 cemen t kiln processes, except on an emergency basis to prevent damage to process equipment by hot gas o r solid process streams. Water is used in noncontact cooling processes that are common in both w et 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 a s cement grinding in both wet process and dry3-21

process plants. However, the nature and amount of s uch water consumption is not intrinsically different between wet process and dry process plant s. 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 p rocesses. 4. Air pollution control device contributions to elect ric power consumption data on cement kiln process systems are assumed to include only existing partic ulate matter control devices and not scrubbers for SO 2 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 d o 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 pro cess 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 Worr ell and Galitsky (2004). 8. As stated in note 7, industry data on electric powe r 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 ce ment plants doe not distinguish between preheater and precalciner kilns with respect to net CKD generation rates. 10. CKD disposal prices vary widely by location. Transp ortation of CKD is a significant component of the cost of disposal if disposal is off-site. For e xample, an estimated off-site disposal cost ranging from $26 to $69 per ton of CKD for disposal, plus t ransportation 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 cemen t kilns generate similar volumes of CKD. Older preheater cement kilns are similar in CKD generatio n to long dry process cement kilns.3-22

Table 3-14. Energy Efficiency Measures for Raw Mate rials 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 Measur es Electricity Consumption Change, kWh/ton clinker Heat Input Change, MMBtu/ton of clinker Energy Efficiency 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 -2.003-23

Table 3-16. Energy Efficiency Measures for Finish G rinding Electricity Consumption Change, kWh/ton clinker Energy Efficiency Improvement Method 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, kWh/ton clinker Energy Efficiency Improvement Method Dry Wet Preheater Precalciner PM* (Preventive Maintenance) -2.50 -2.50 -2.50 -2.5 0 HEM (High Efficiency Motors) -2.50 -2.50 -2.50 -2.5 0 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: NO X , SO 2 and CO 2 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 Vij ay, 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 A ppendix A of this document). ARCADIS (1992). Theoretical Approach for Enhanced M ass Transfer Effects in-Duct Flue Gas Desulfurization Processes. Final Report. ARCADIS, 1 992. ARCADIS (2009). Personal Comm. with E. Leamer of AR CADIS, 26 February, 2009. Burtraw, D. (2010). Supply Elasticity Estimation, m emorandum to Ravi Srivastava, U.S. EPA, March 10, 2010. Depro, Brooks, Rogozhin, Alex, and Wood, Dallas (20 07). 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 Cem ent Production Costs”, Memorandum to Elineth Torres, US EPA, March 31, 2010 (see Appendix A of t his document). Depro, Brooks and Lentz, Anthony. (2010). RTI Inter national. “ISIS Cement Production Costs”, Excel Spreadsheet Attachment to Memo of Same Name from Br ooks Depro (2010), February 16, 2010. BLS (2008). Bureau of Labor Statistics, Databases, Tables & Calculators by Subject, Major Sector Productivity and Costs Index. Series ID: PRS3000611 2, 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 F actors - 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 Rule making . Washington, DC: U.S. Environmental Protection Agency. http://www.epa.gov/osw/nonhaz/industrial/special/ck d/ckd/ckdcostt.pdf , accessed October 21, 2008. EPA (2004). Alternative Control Techniques Document - NO X 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.htm l , accessed October 21, 2008 EPA (2006). EPA 2005 National Emissions Inventory V ersion 2. http://www.epa.gov/ttn/chief/net/2005inventory.html accessed November 17, 2008 EPA (2007). November 2007. Alternative Control Techniques Document Update - NO X Emissions from New Cement Kilns. EPA-453/R-07-006 . Research Triangle Park, NC. US Environmental Prot ection Agency. FLSmidth & Co. A/S (2007). Q2 Report 2007. August 2 007 http://hugin.info/2106/R/1148414/219358.pdf , accessed October 21, 2008. NESCAUM (2000). Status Report on NO X Controls for Gas Turbines, Cement Kilns, Industria l Boilers, Internal Combustion Engines: Technologies & Cost Ef fectiveness. 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 Benef it-Cost Analysis of Federal Programs. OMB Circular No.A-94 (Revised). U.S. Office of Manageme nt and Budget, October 29, 1992. http://www.whitehouse.gov/omb/circulars/a094/a094.h tml#8 , accessed October 21, 2008. PCA (2004). Innovations in Portland Cement Manufact uring. Portland Cement Association. Edited by J. I. Bhatty, F. M. Miller, and S. H. Kosmatka. 2004. PCA (2004a). U.S. and Canadian Portland Cement Indu stry: Plant Information Summary of December 31, 2004. PCA (2006). U.S. and Canadian Portland Cement Indus try: Plant Information Summary. Portland Cement Association, Skokie, IL, 2006. PCA (2008). Forecast Report: Long-Term Cement Consu mption Outlook. By Ed Sullivan January 31, 2008 . http://www.cement.org/econ/pdf/Long-TermFlashwint er2007nonmember.pdf , accessed October 21, 2008. PCA (2009a). PCA Capacity Report. Flash Report. P ortland Cement Association’s Economic Research Department. Updated October 19, 2009.3-26

PCA (2009b). Forecast Report: Long-Term Cement Cons umption Outlook. By Ed Sullivan. October 28, 2009. Portland Cement Association. Ryan, S. (2006). The Costs of Environmental Regulat ion in a Concentrated Industry. Department of Economics, MIT, Cambridge, MA, October 5, 2006. http://econ-www.mit.edu/files/116 6 , accessed October 21, 2008. RTI (2010). ISIS Input Price Escalation Factors, Ex cel Spreadsheet. TRAGIS (2003). Transportation Routing Analysis Geog raphic 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/ce ment/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. G eological Survey (Table 17), pp. 16.21. http://minerals.usgs.gov/minerals/pubs/commodity/ce ment/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/ener gycitations/servlets/purl/821915-Re2kcK/native/. Yates, J.R. et al. (2003). CemStar Process and Tech nology for Lowering Greenhouse Gases and Other Emissions while Increasing Cement Production. http://www.hatch.ca/Environment_Community/Sustainab le_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 extensiv e amount of data which comes from different sources. This chapter outlines calibration methodology that was used, discusses data used for calibration, pres ents calibration results, and gives further recommendations. 

### 4.1 Calibration Methodology Calibration methodology utilizes the concept of cal ibration constant. The calibration constant has bee n 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 pric es (which are marginal values of the supply equatio n) 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 dis trict, except for those of Mexico and Canada. In the next step, impact of changing the calibratio n constant is monitored. This impact of the calibra tion constant is assessed on estimated production quanti ties to keep the difference between reported and mo del predicted production values within reasonable limit s 5 . The calibration constant modifies each kiln’s variable cost of production. “Calibration” workshee t within the Inputs workbook has values of calibrat ion 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 obt ain values of the calibration parameter calconst(i) for the year. Next, calconst(i) values for 2005 are used as starting point and obt ain values for the same parameter for 2006. Similarly, the process is repeated to obt ain 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 w orksheet “ Calibration ” of the “ISIS_Inputs.xls” workbook. Calibration is a dynamic process, and it is recomme nded that model calibration be performed periodical ly. In this fashion, any available new production, impo rts, or price data could be utilized in t he model.

. 

### 4.2 Data for Calibration Production quantities in various USGS production di stricts, import levels in the import districts and reported cement prices for USGS districts are the k ey quantities used for calibration of the model.

5 There is no standard method to guide user in deter mining 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, er ror in the reported data, or other unknowns, the ga p in the estimated and reported values may be higher in certain markets.4-2

Reported data for years 2005, 2006 and 2007 were us ed 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 ar e shown in Table 4-1 for years 2005 through 2007. For a given year, the reported cement price shows s ignificant variation across USGS districts. For mos t districts the cement price has increased over the t hree 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 district s is shown in Table 4-2 for years 2005 through 2007. For a given year, the reported cement product ion shows significant variation across USGS distric ts. 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 d istricts is shown in Table 4-3 for years 2005 throu gh 2007. For a given year, the reported cement imports show a significant variation across import distric ts. 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, respec tively. As can be seen from these tables, in most o f 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 different ials are smaller in the year 2005 and have increase d in 2007. In some USGS districts the reported and calcu lated prices are different than the model predicted prices due to several factors, including error in r eported prices, discontinuities in the transportati on matrix, and/or unique factors for some specific mar kets. Specifically, the Arizona and New Mexico USGS distr ict 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 hig h price for cement in this market. Similarly, Orego n and Washington USGS District is dependent on supplies f rom 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 aberrati ons are explained by the demand-supply gap and transpor tation 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 prod uction level is also tracked to make sure that it i s within reasonable limits. The reported and predicted aggre gate 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 ava ilable in the “Calibration” worksheet in the “ISIS_Inputs. xls” workbook.4-4

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

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 90.724-5

Table 4-2. Cement Production (short tons) for Vario us 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 6,047,58 8 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 2,902,13 1 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 2,103,4814-6

Table 4-3. Cement Imports (except from Canada and M exico) 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 1,180,30 0 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 427,900 416,900 284,9004-7

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 -134-8

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 -184-9

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 model ed) 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 -5.754-10



### 4.4 Recommendations If any of the key input parameters, specifically th ose relating to production quantities and costs, ar e refined or otherwise modified or additional observe d data becomes available, the calibration of the m odel should be repeated. Transportation matrix, modes, a nd cost of transportation also have significant imp act on the behavior of production distribution across t he districts and production prices. Therefore, if a ny further modifications or refinements are made to th e transportation matrix, the model needs to be recalibrated.
At the time of calibration of the current version o f the model, production, import and price values fo r only 2005 to 2007 were available. As discussed above, wh en the new values become available, the model needs to be calibrated again. Calibration of the mo del should be repeated as soon as new information o r new observed data become available. Due to practica l limitations it is recommended that the calibratio n of the model be repeated every two years. Further, sin ce the calibration data is available only for three years, equal weight was given to the parameters obtained f or each year. Once larger data-set is available, a modified weighing system can be adopted to give hig hest 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/ce ment/ United States Geological Service. Mineral Yearbook 2008. Table 3. http://minerals.usgs.gov/minerals/pubs/commodity/ce ment/ United States Geological Service. Mineral Yearbook 2008. Table 11. http://minerals.usgs.gov/minerals/pubs/commodity/ce ment/5-1 

## Chapter 5 An Example Policy Analysis
5.1 An Example Analysis of Potential Reductions in CO 2 

### 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 r eductions in emissions of CO 2 and other pollutants, associated costs, and industry operation. The regul atory mechanism selected for analysis was cap-andtrade of CO 2 . This mechanism has been used in many of the recen t policy proposals addressing reduction of CO 2 emissions. In a cap and trade framework, CO 2 allowance price is a metric of primary importance for deciding on CO 2 reduction levels. For a reduction range selected f or analysis to be practicable, CO 2 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 CO 2 emission reduction is determined to be practicable, the model projects ch aracteristics of the industry for this emission red uction range. The analysis as described above is an exampl e of the type of sector-specific analyses that may be needed for developing GHG reduction policies for in dustrial sectors. Two broad questions were investigated in this examp le analysis: 1) what range of CO 2 reduction options may be practicable in the near-term (i.e., the deca de ending 2020), and (2) for that range, what may b e the market characteristics for the U.S. cement industry ? The first question is relevant because in the abs ence 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. Th e second question speaks to industry operation unde r the potentially practicable options determined whil e answering the first question. To investigate the above questions, CO 2 emissions targets corresponding to a range of redu ctions from projected CO 2 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 mate rial 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 analy sis because at this time ISIS-cement does not inclu de calculation procedures for the measures shown in Ta bles 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 con sumption (by about 15%) and this fuel consumption reduction is included in ISIS-cement. Relative to C emStar, 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 co sts for energy efficiency measures are described in And over 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 ener gy efficiency measures in Tables 3-15 and 3-17 coul d be applied on existing kiln capacity was obtained ( Xu, 2010) and included in ISIS-cement. These penetration numbers ranged from 5 percent for CGC t o 88 percent for preventive maintenance

. Also for this example analysis it was assumed that each of C emStar and MKF-tires cannot be applied on more than 25 percent of the existing and unretired kiln capac ity over the horizon 2013-2020.5-2

Table 5-1. CO 2 Caps Modeled in this Analysis CO 2 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 cor responding results are presented in Figures 5-1 thr ough 5-7. In a cap-and-trade framework, CO 2 allowance price is a primary metric for deciding o n CO 2 reduction levels. Figure 5-1 reflects that CO 2 allowance price (in 2013 for example) for various reduction levels ranging from about 10 to 35 $ per short ton of CO 2 . A recent analysis of the American Clean Energy an d Security Act of 2009 reflects a price range of 13-2 4 $ per metric ton in 2015 (EPA, 2009). Considering this indication, a reduction range of 5 to 15 perce nt 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 25.0CO2Allowance Price ($/short ton)

CO 2 Reduction (%)

Figure 5-1. Projected CO 2 Allowance Prices5-3

Two metrics for additional evaluation of potential emission reductions levels could be: (1) U.S. indus try revenue under policy, and (2) cement price under p olicy. Arguably, these metrics speak to the interes ts of both the industry and consumers. An increase in cement price under a CO 2 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 dem and 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 t heir needs at higher prices. And the drop in demand will contribute to reduction in revenue for the industr y. 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 ra nging from 5 to 25 percent. Figure 5-2 shows that t he 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 re duction range, Figure 5-3 reflects that the increas e in cement price ranges from about 5 to 12 percent. Fig ure 5-4 shows the drop in demand due to rising pric es under increasing CO 2 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 25.0Change in U.S. Industry Revenue (%)U.S. Industry Revenue (billion $)

CO 2 Reduction (%)
U.S. Industry Revenue (billion $) Change in U.S. Industry Revenue (%)
Figure 5-2. Projected Revenue for the U.S. Cement I ndustry over the Horizon 2013-20205-4 0 5 10 15 20 25

0 40 80 120

0.0 5.0 10.0 15.0 20.0 25.0Change in Cement Price (%)Cement Price ($/short ton)

CO 2 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 25.0Change in Demand (%)Demand (million short tons)

CO 2 Reduction (%)

Demand (million short tons) Change in Demand (%)
Figure 5-4. Projected Demand for Cement5-5

Figure 5-5 reflects that under example policy optio ns 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 indust ry resorts to increasing imports in a monotonic fashio n. Since imports come with zero emissions in the U. S. and result in reduced domestic fuel and raw materia ls processing, they can help comply with a policy requiring reductions in domestic emissions. While t he impact of increases in imports in the U.S. on emissions in exporting countries is beyond the scop e of this analysis, such increases in imports will generally result in increases in CO 2 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 25.0Change in imports (%)Imports (million short tons)

CO 2 Reduction (%)

Imports (million short tons) Change in Imports (%)
Figure 5-5. Projected Imports for the U.S. Cement I ndustry over the Horizon 2013-2020

A concern related to drop in revenue can be retirem ent/mothballing of kiln capacity in the U.S. cement industry. Figure 5-6 reflects the potential for ret irements/mothballing under the example reduction options. For this work, a unit is considered retire d 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 ea ch year of the horizon 2013-2020, the U.S. cement industry has excess kiln capacity relative to BAU d emand. 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 1 4 percentage points over the BAU projection. Significant collateral reductions in other pollutan t emissions may be possible under the range of practicable CO 2 reduction levels arrived at in this example analys is. Figure 5-7 shows such collateral reductions in NO X and SO 2 emissions. At the 15-perecnt CO 2 reduction level, each of NO X and SO 2 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 25.0Change in Retired/mothballed Capacity (%)Retired/mothballed Capacity (million short tons)

CO 2 Reduction (%) Retired/mothballed Capacity (million tons) Retired/mothballed Capacity (%) Figure 5-6. Projected Capacity Retirement/Mothballi ng

0 100 200 300 400 500 600 700

0 200 400 600 800 1000

0.0 5.0 10.0 15.0 20.0 25.0CO2Emissions (million short tons)NOx, SO2Emissions (thousand short tons)

CO 2 Reduction (%)
NOx SO 2 CO 2
Figure 5-7. Projected Emissions of NO X , SO 2 , and CO 2 from the U.S. Cement Industry over the

Horizon 2013-20205-7

For the cap at the 15-percent reduction level, whic h is in the range of practicable reductions for pur poses of this example analysis, additional detailed resul ts are presented in Figures 5-8 and 5-9. Figure 5-8 shows the projected CO 2 emissions under the BAU case, emission caps corres ponding to 15-percent reduction from CO 2 emissions in 2013 and banked allowances. The indus try engages in relatively modest levels of CO 2 allowance banking and related trading activity, pr actices consistent with the relatively modest level of reduction required. Figure 5-9 reflects that, in response to the CO 2 reduction requirement, the industry installs and o perates measures with multi-pollutant (NO X , SO 2 , and CO 2 ) reduction benefits. The majority of these measure s are installed in 2013 to help comply with the reduc tion 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-tire s are briefly described in the section on raw materia l and/or fuel substitution. Note that Figure 5-9 re flects that CemStar will need to be applied on about 40 pe rcent 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 Cem Star application.

0 10 20 30 40 50 60 70 80 90

2013 2014 2015 2016 2017 2018 2019 2020CO2(million tons)

BAU Case Emissions, CO2 (tons) Emission Cap, CO2 (tons)

Controlled Emissions, CO2 (tons) Banked Allowances, CO2 (tons)
Figure 5-8. Projected CO 2 Emissions and Reductions from the U.S. Cement Indu stry Under the BAU and the 10-percent CO 2 Reduction Cases.5-8 0 5 10 15 20 25 30 35 40 45

2013 2014 2015 2016 2017 2018 2019 2020Percent of Operating Capacity (%)

MKF-tires CEMstar PM EMCS SR CSI IF SHLR OGR

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

10-percent CO 2

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 CO 2 reductions options may be practicable in the near-term, and (2) for that rang e, what may be the market characteristics for the U .S. cement industry? These questions are relevant becau se in the absence of carbon capture and sequestrati on technology, the path forward for reducing CO 2 emissions in the near-term (decade ending 2020) wi ll need to depend on the available energy efficiency measur es and raw material and product substitution option s. The energy efficiency measures for clinker making a nd plan-wide application were considered in this example analysis. In addition to the energy efficie ncy 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 CO 2 allowance prices, a range of CO 2 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 t o 6.5 percent of the revenue in the BAU case and th e increase in cement price (in 2013, for example) ran ges from about 5 to 12 percent relative to the BAU. Significant collateral reductions in other pollutan t emissions may be possible under the range of 5- t o 15-percent CO 2 reductions. For example, at the 15-percent CO 2 reduction level, each of NO X and SO 2 emissions may be reduced by more than 250 thousand short tons. To meet a CO 2 reduction level of 15 percent from the projected emissions in 2013, th e 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 availab le in the near-term (i.e., the decade ending 2020). In pa rticular, the analysis did not take into account th e energy efficiency measures of Tables 3-14 and 3-16 and feasible raw material and product substitution approaches. Second, some assumptions associated wit h the abatement measures that were considered need additional evaluation. In particular, availabi lity 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. CO 2 emissions associated with imports need to be taken into account while setting reduction targets. These areas need t o be addressed to permit more comprehensive evaluation of the CO 2 reduction potential relative to the U.S. cement in dustry. Work is being initiated to address these areas and modify ISIS-cement as neede d. The focus of the example analysis presented is on m ore modest CO 2 reductions potentially possible with use of energy efficiency approaches in the near-ter m. However, in the longer term more significant CO 2 reductions (e.g., greater than 50%, perhaps) can on ly be achieved with use of CO 2 -specific mitigation technologies currently under development. 

### 5.3 Disclaimer Reference herein to any specific commercial product s, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily co nstitute 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 St ates Government, and shall not be used for advertis ing or product endorsement purposes.5-10



### References for Chapter 5 Andover Technology Partners (2008). Cost and Perfor mance 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 Ener gy 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/economic analyses.html

. Xu, T. (2010). Personal communication: Tengfang Xu, Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory with Ravi Sri vastava, U.S. EPA, May 2010.