# Technical Support Document The Industrial Sectors Integrated Solutions (ISIS) Model and the Analysis for the National Emission Standards for Hazardous Air Pollutants and New

Source Performance Standards for the Portland Cement Manufacturing Industry

U.S. Environmental Protection Agency Sectors Policies and Program Division and
Air Pollution Prevention and Control Division Research Triangle Park, NC 27711

August 2010ii 

## Table of Contents
List of Tables ................................................................................................................ ............................... iv List of Figures ............................................................................................................... ................................ v Conversion Table – English Units to SI Units .................................................................................. ........... ix 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 ................................................................................................. 1-10 1.2 Emissions from the U.S. Cement Industr y and Applicable Regulations ................................. 1-10 1.3 Overview of ISIS .............................................................................................................. ....... 1-12 References for Chapter 1 ...................................................................................................... ................... 1-14 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- 27 References for Chapter 2 ...................................................................................................... ................... 2-29 Chapter 3 Input Data for ISIS-Cement Model .................................................................................... ....... 3-1 3.1 Data Requirements ............................................................................................................. ........ 3-1 3.2 Cement-Specific Data .......................................................................................................... ...... 3-1 3.2.1 Industry, Fuel, and Emissions ............................................................................................ 3-1 3.2.2 Control Technologies and Emission Abatement Approaches .......................................... 3-13 3.2.3 Policy and Economic Parameters ..................................................................................... 3-13 References for Chapter 3 ...................................................................................................... ................... 3-24 Chapter 4 Model Calibration ................................................................................................... .................. 4-1iii 4.1 Calibration Methodology ....................................................................................................... .... 4-1 4.2 Data for Ca libration .......................................................................................................... ......... 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 Conclusions ................................................................................................................... ........... 4-10 References for Chapter 4 ...................................................................................................... ................... 4-11 Chapter 5 Analysis and Results of the Portland Cement NESHAP and NSPS .......................................... 5-1 5.1 Affected Sources .............................................................................................................. .......... 5-1 5.2 Emission Limits ............................................................................................................... .......... 5-2 5.3 Control Technologies .......................................................................................................... ....... 5-3 5.4 ISIS Runs and Results ......................................................................................................... ....... 5-3 5.4.1 ISIS Results .................................................................................................................. ...... 5-4 References for Chapter 5 ...................................................................................................... ................... 5-10 Disclaimer .................................................................................................................... ............................ 5-11iv 

### List of Tables
Table 1-1. Typical Average Heat Input by Cement Kiln Type ........................................................... 1-5 Table 1-2. Number of Kilns by Kiln Type in the U.S. in 2005 and 2009 ........................................... 1-6 Table 1-3. Largest Hydraulic Cement and Clinker Import Cu stom Districts in the U.S. in 2005 ....... 1-9 Table 3-1. Summary of Kilns Modeled in the ISIS Cement Industry Model ...................................... 3-1 Table 3-2. Portland Cement Demand in Milli ons 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 Milli on Metric Tons, by Customs District in 2005 .......................................................................................................................... ......... 3-6 Table 3-5. Specific Fuel Consumption and Total Exhaus t Gas Flow 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 ................................................................... 3-17 Table 3-11. CO 2 Control Technologies for Cement Kilns .................................................................. 3-19 Table 3-12. HCl, Hg, and THC Control Technologies for Cement Kilns ........................................... 3-20 Table 3-13. Multimedia Impacts of Process Capacity Replacement on Cement Kiln Operation 1 ...... 3-20 Table 3-14. Energy Efficiency Measures for Raw Materials Preparation ........................................... 3-21 Table 3-15. Energy Efficiency Clin ker Making Measures .................................................................. 3-22 Table 3-16. Energy Efficiency Measures for Finish Grinding ............................................................ 3-23 Table 3-17. Energy Efficiency Plan t-wide Measures .......................................................................... 3-23 Table 4-1. Cement Prices ($/short ton) for USGS Districts ................................................................ 4-4 Table 4-2. Cement Production (short tons) fo r Various USGS Districts ............................................ 4-5 Table 4-3. Cement Imports (except from Canada and Me xico) for Import Districts (short tons) ....... 4-6 Table 4-4. Reported and Calculated Cement Pric es in USGS Districts (2005) ................................... 4-7 Table 4-5. Reported and Calculated Cement Pric es in USGS Districts (2006) ................................... 4-8 Table 4-6. Reported and Calculated Cement Pric es in USGS Districts (2007) ................................... 4-9 Table 4-7. Aggregate production (reported and modeled) for 2005-2007 .......................................... 4-9 Table 5-1. Final NESHAP Emission Limits ...................................................................................... ....... 5-2 Table 5-2. Final NSPS Emission Limits ........................................................................................ ........... 5-2 Table 5-3. Control Technology and Control Efficiency Matrix by Pollutant ........................................... 5-3 Table 5-4 Policy Impact on U.S. Cement Demand in 2013 ....................................................................... 5 -5 Table 5-5 Policy Impact on U.S. Cement Production ............................................................................. ... 5-5 Table 5-6 Policy Impact on U.S. Cement Imports ................................................................................ ..... 5-5 Table 5-7 Policy Impact on U.S. Cement Prices ................................................................................. ....... 5-5v Table 5-8 Control Tec hnology Installation ..................................................................................... ........... 5-6 Table 5-9 Control Cost ........................................................................................................ ....................... 5-6 Table 5-10 Cement Capacity .................................................................................................... ................. 5-6 Table 5-11 Kiln Population .................................................................................................... .................... 5-7 Table 5-12 Number of Cement Facilities ........................................................................................ ........... 5-7 Table 5-13 Change in Employee Hours ........................................................................................... .......... 5-7 Table 5-14 PCA’s Labor Data: 2005 and 2008 .................................................................................... ...... 5-8 Table 5-15 Employment Impacts ................................................................................................. .............. 5-8 Table 5-16 Total Social Cost .................................................................................................. ................... 5-8 Table 5-17 Emissions .......................................................................................................... ....................... 5-9



### List of Figures Figure 1-1. Schematic of the Wet Cement Process ............................................................................... 1-3 Figure 1-2. Schematic of the Dry Cement Pro cess with Cyclone Preheater ......................................... 1-4 Figure 1-3. Trends in Cement Kiln Type and Capac ity in the U.S. (1995 to 2008) ............................. 1-6 Figure 1-4. Portland Cement Plant Locations ....................................................................................... 1-8 Figure 1-5. Imports of Clinker and Cement from 1998 to 2008 (PCA, 2009a) .................................. 1-10 Figure 1-6. Portland Cement Facilities and O 3 NAA, PM 2.5 NAA, and Class 1 Areas ...................... 1-12 Figure 1-7. An Integrated View of Pollution Generation Pathways, Emissions Abatement Approaches, and Multimedia Impacts for an Industrial Sector ....................................... 1-14 Figure 2-1. Consumer and Producer Surplus in a Market .................................................................... 2-2 Figure 2-2. Broad Modules and Associated Information Flow s Utilized in the ISIS Framework ........ 2-2 Figure 2-3. Stepwise Calculation of the Demand Curve ...................................................................... 2-5 Figure 3-1. Distribution of Cement Kilns in the United States as of 2009 ........................................... 3-3 Figure 3-2. Commercial Fuel Use Profile by U.S. Cement Industry in 2005. ...................................... 3-8 Figure 5-1 Portland Cement Facilities and Kiln s Modeled in ISIS-Cement ........................................ 5-1vi 

### Acronyms 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 BLD Bag Leak Detector BLS Bureau of Labor Statistics BTS Bureau of Transportation Statistics CA California CARB California Air Resources Board CC Carbon Capture CEMBUREAU European Cement Association based in Brussels CEMS Continuous Emission Monitoring System CGC Convert to Reciprocating Grate Cooler CIF Cost, Insurance & Freight CKD Cement Kiln Dust CO Colorado CO Carbon Monoxide CO 2 Carbon Dioxide 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 Floridavii FOM Fixed Operation and Maintenance GA Georgia GAMS General Algebraic Modeling System GDP Gross Domestic Product GHG Greenhouse Gas H 2 O Water HAP Hazardous Air Pollutant HCl Hydrochloric Acid HEC High-Energy Classifiers HEM High Efficiency Motors HERM High Efficiency Roller Mill Hg Mercury 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 KJ Kilojoules LI Lime Injection LNB Low NO X Burner LWS Limestone Wet Scrubber MBR Membrane Bags Retrofit MI Michigan MMBtu Million British Thermal Units MO Missouri MSA Mineralogical Society of America MYB Minerals Yearbook (USGS) NAA Nonattainment Area NACT North American Cement Transportation NAS National Academy of Science NATA National Air Toxics Assessment NC North Carolina NEI National Emission Inventory NESCAUM Northeast States for Coordinated Air Use Managementviii NESHAP National Emission Standards for Hazardous Air Pollutants NM New Mexico NO X Oxides of Nitrogen NSPS New Source Performance Standard NRC National Research Council NV Nevada O&M Operations and Maintenance OCAS Optimization of Compressed Air Systems OGR Optimize Grate Cooler OK Oklahoma OMB U.S. Office of Management and Budget OR Oregon PA Pennsylvania PC Pulverized Coal PCA Portland Cement Association PCVM Process Control Vertical Mill PM Particulate Matter PM Preventive Maintenance PRB Powder River Basin PSD Prevention of Significant Deterioration RMB Raw Materials Blending RMC Ready-Mix Concrete RMTHEC Raw Materials Transport High Energy Classifiers RTO Regenerative Thermal Oxidizer SBH Slurry Blending and Homogenization SC South Carolina SCR Selective Catalytic Reduction SFC Specific Fuel Consumption SG&A Sales, General and Administration SHLR Shell Heat Loss Reduction SLC Salt Lake City SNCR Selective Non-Catalytic Reduction SO X Oxides of Sulfur SPE Spatial Price Equilibrium SR Seal Replacement TDF Tire Derived Fuel THC Total Hydrocarbons TRAGIS Transportation Routing Anal ysis Geographical Information Systemix USGS United States Geological Survey UT Utah WA Washington WBCSD World Business Council for Sustainable Development WDOT Washington State Department of Transportation WMCCC Wash Mills with Closed Circuit Classifier



### 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, “A ir Quality Management in the United States,” the National Research Council (NRC) recommended to th e U.S. Environmental Protection Agency (EPA) that standard setting, planning, and control stra tegy development should be based on integrated assessments that consider multiple pollutants, and that these integrated assessments should be conducted in a comprehensive and coordinated manner (NAS , 2004). With these recommendations, EPA began to move toward establishing multipollutant and sect or-based approaches to manage air quality and environmental protection. The benefits of multi pollutant and sector-based analyses and approaches include the ability to identify optimum strategies (cons idering feasibility, costs, and benefits across all pollutant types such as criteria, toxics, and others ), while streamlining administrative and compliance complexities and reducing conflic ting and redundant requirements. The development of policy options for managing emi ssions and air quality can be made more effective and efficient through sophisticated analyses of releva nt technical and economic factors. Such analyses are greatly enhanced by the use of an appropriate mode ling framework. Accordingly, the Industrial Sectors Integrated Solutions (ISIS) model has been devel oped at EPA. Currently, the ISIS model is populated with US cement manufacturing data, and efforts are underway to build representations of the US pulp and paper sector and the US iron and steel sector. This document describes the framework of EPA’s ISIS model and its application to the US cement manufact uring industry and the analysis performed for the Portland Cement National Emission Standards for Hazardous Air Pollutants (NESHAPs) and the New Source Performance Standards (NSPS) for the Portland cement manufacturing industry

. 

### 1.1 The US Cement Industry 1.1.1 Cement Types and Categories Cement is a finely ground powder which, when mixe d with water, forms a hardening paste of calcium silicate hydrates and calcium aluminate hydrates. Cement is used in mortar (to bind together bricks or stones) and concrete (bulk rock-like building materi al made from cement, aggregate, sand, and water). Concrete production uses the majority of cement produced.
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 produ ction. By modifying the raw material mix and, to some degree, the temperatures utilized in manufacturi ng, slight compositional va riations can be achieved to produce Portland cements with slightly different properties. In the U.S., the different varieties of Portland cement are denoted per the American Soci ety for Testing and Materials (ASTM) Specification C-150. The ASTM standard C-150 recognizes eight types of Portland cement:

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

 Type IA is similar to Type I with th e addition of an air-entraining agent.

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

 Type IIA is similar to Type II with th e addition of an air-entraining agent.

 Type III is used when concrete mu st set and gain strength rapidly.1-2  Type IIIA is similar to Type III with th e addition of an air-entraining agent.

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

 Type V is used when concrete must resist high sulfate concentration in soil and groundwater. Portland cements are usually gray, but a more expensive white Portland cement (generally within the Type I or II designations) can be obtained by pro cessing only raw materials with very low iron and transition-elements content. In addition to the eight types of Portland cement listed above, small volumes of specialty cements are also manufactured. Other types of specialty cement include blended cement (Portland cement mixed together with blast furnace slag or other pozzolan), pozzolan-lime cement, masonry cement, and aluminous cemen t (PCA, 2008a). The 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 largel y of Portland cement and are used for similar applications (e.g., concrete) (U SGS, 2005). These cements include blended cement, block cement, expansive cement, oil well cement, regulated fast se tting cement, and waterproof cement. Plastic cements and Portland-lime cements are being grouped within the masonry cement designation. Because Portland cement accounts for approximately 95% of the ceme nt industry’s total production (van Oss, 2008), and because the costs and trends of this industry sector can be adequately captured by describing the market processes associated with the production, distribution, a nd use of Portland cement, in this work the focus is on Portland cement. In 2006, Portland cement’s market share in the U.S. was 94%, while masonry cement’s market share comprised the remaining 6% (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 mine rals may be added to adjust the raw material composition. The fine raw material is fed into a large ro tary kiln (cylindrical furn ace) where it is heated to extremely high temperatures [about 2640 °F (a bout 1450 °C)]. The high temperature causes the raw material to react and form a hard nodular material called “clinker”. Clinker is cooled and ground with approximately 5% gypsum and other minor additives to produce Portland cement. The main steps in the cement manufacturing process are illustrated in Figures 1-1 and 1-2 showing the wet process and the dry process with cyclone preheater. The schematic for a pr ecalciner kiln would be very similar to that shown in Figure 1-2, with the a ddition of a calciner vessel. The heart of the clinker production process is the kiln , which can be rotary or vertical shaft designs. Rotary kilns are commonly used in the U.S. and elsew here. These kilns are 6-8 m in diameter and 60 m to well over 100 m long. The kilns are set at a slight incline and rotate at 1 to 3 revolutions per minute. The kiln is fired at the lower end and the feed materi als move toward the flame as the kiln rotates. The materials reach temperatures between 1400-1500 °C in the kiln. Three steps occur with the raw material mixture during pyroprocessing. First, all moisture is driven off from the materials. Then the calcium carbonate in limestone dissociates into CO 2 and calcium oxide (free lime) during calcination. Finally, the lime and other minerals in the raw materials react to form calcium silicates and calcium aluminates, the main components of clinker.1-3Figure 1-1. Schematic of the Wet Cement ProcessSource: CEMBUREAU, 19991-4Figure 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 material slurry with moisture content ranging between 30 and 40%. A wet-process kiln needs additional length to ev aporate the water contained in the raw material feed. Nearly 33% additional kiln energy is cons umed in evaporating the water in the slurry. In dry-process kilns, raw material is fed as dry powder. There are three major variations of dry-process kilns in operation in the U.S.: long dry kilns, preheater kilns, and preheater/precalc iner kilns. In preheater kilns and preheater/precalciner kilns, the early stag es of pyroprocessing occur before the materials enter the rotary kiln. Preheater and preheater/precalcine r kilns have higher production capacities and greater fuel efficiency compared to other types of cement k ilns. Table 1-1 shows heat input in terms of millions of British Thermal Units (MMBtu)/ton 1 of clinker for various types of kilns. As the data clearly demonstrate, preheater/precalciner kilns provide great er fuel efficiency. The replacement of wet and (certain) dry process kiln capacity with modern k iln processes can yield, theoretically, substantial reductions in fuel use due to fuel efficiency gains. As the industry moves toward more efficient processes, replacement of wet and long dry process capacity w ith more efficient kiln process technologies is expected. Table 1-1. Typical Average Heat Input by Cement Kiln Type Kiln Type Heat Input, MMBtu/ton of clinker Wet 6.0 Long Dry 4.5 Preheater 3.8 Preheater/Precalciner 3.3 Source: EPA, 2007b (Table 3-3) As expected, a recent trend in the cement sector has shown the replacement of lower capacity, inefficient wet and long dry kilns with bigger and more efficient k ilns. This trend is expected to continue. In the U.S., the overall number of kilns decreased by 11 % from 1995 to 2004. During the same period, total clinker production capacity increased by 18.6%. The Po rtland Cement Association (PCA) data show that average kiln capacity also incr eased by 27%, from 405,000 to 556,000 t ons per year between 1995 and 2004. The number of kilns operating in the U.S. in 2005 compared with the number of kilns in operation in 2009 is shown in Table 1-2. The trend in kiln d esign and average kiln capacity is shown in Figure 1-3.

1 PLEASE NOTE: for the purposes of this document, short tons will be referred to simply as “tons” and represents 2000 lbs. A unit conversion table is prov ided in the front matter of this document.1-6 Table 1-2. Number of Kilns by Kiln Type in the U.S. in 2005 and 2009 Kiln Type Number of Kilns (2005) Number of Kilns (2009) Wet 50 41 Dry 48 32 Preheater 36 29 Precalciner 47 66 Source: PCA, 2006 and 2009a

0 100 200 300 400 500 600

0 25 50 75 100 125 150 175 200 225

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 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 bien nial 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. Portla nd cement industry produced approximately 87 million metric tons of clinker, of which 13.5% was produced by plants with wet process kiln systems while 81% was produced by plants operating only dry kilns of all three types (long-dry, preheater and preheater/precalciner). The remaining 5.5% was produced by plants that operate both wet and dry kilns (USGS, 2007b: Table 7).1-7 

### 1.1.4 Portland Cement Production in the U.S. The cement industry remains a vital industry in the U.S. and throughout the world. In the U.S., Portland cement is a $12 billion industry (van Oss, 2008). In 2005, the U.S. consumed a record 128 million metric tons of Portland cement (USGS, 2007b: Table 1). In 2005, Portland cement was produced at 107 plants in the U.S., including 37 states and Puerto Rico. The locations of the clinker-producing Portland cement pl ant facilities in 2005 are shown in Figure 1-4. The 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 foreign companies (USGS, 2007b). Together, ten companies accounted for about 80% of the total cement production in U.S. for 2005 (USGS, 2007b). California, Texas, Pennsylvania, Florida, and Alab ama are the five leading cement-producing states, which accounted for about 43% of the total production in 2005 (USGS, 2007b: Table 3). In 2009, PCA projected a capacity expansion of 27 million tons between 2008 and 2013, an 18% increase in existing capacity from 2006. PCA’s projected capacity expansions are to come from 23 kilns; 5 came on-line in 2008 and 18 more are expected to come on-line between 2009 and 2013 (PCA, 2009b). The investment in these projected capacity expansions is pr ojected at $6.9 billion. Note that a typical project for a new facility (greenfield) from ground breaking to startup has a timeframe of about 2 to 3 years. Building a new kiln in an existing facility (brownfi eld) can take approximately 1 year (EPA, 2007b). If permitting for mining and (re)construction is accounted, a typical project can take as long as 4 to 6 years for a greenfield facility and up to 3 years for a brownfield facility (Staudt, 2009).1-8 Figure 1-4. Portland Cement Plant Locations



### 1.1.5 Imports of Portland Cement in the U.S. Portland cement is not only produced and consumed domes tically, 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 metr ic tons of hydraulic cement and clinker (USGS, 2007b: Tables 3 and 18). The level of imports to the U.S. is highly cyclical, with domestic producers importing primarily when domestic plants are at full capacity and cannot meet excess demand. Imports also occur when domestic producers seek to capture market share. Gene rally, imported cement and clinker make up 20 to 27% 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% of the total cement sales in the U.S. (USGS, 2007b). In 2005, the ten leading international cement and cli nker suppliers to the U.S. were, in descending order, Canada, China, Thailand, Greece, the Republic of Korea, Venezuela, Mexico, Colombia, Taiwan, and1-9 Sweden. The ten busiest ports of entry within cu stoms districts existing in 2005 were, in descending order, New Orleans, Tampa, Los Angeles, Houston- Galveston, San Francisco, Miami, Seattle, Detroit, New York, and Charleston (South Carolina) (USGS, 2007b). Table 1-3 shows the major customs districts for hydraulic cement and clinker imports in the U.S. Figure 1-5 shows the imports of clinker and cement from 1998 to 2008.

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

Custom District Import of Hydraulic Cement and Clinker Percentage of Total US

Imports thousands of metric to ns thousands of tons 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)1-10 Figure 1-5. Imports of Clinker and Ce ment from 1998 to 2008 (PCA, 2009a)



### 1.1.6 Cement Demand Centers Because of the relatively high transportation costs, th e U.S. cement industry is structured around statespecific cement demand centers. PCA reports that the v ast 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 tran sported over longer distances, especially when the less expensive rail and water transporta tion modes are available (APCA, 1997). 

### 1.2 Emissions from the U.S. Cement In dustry and Applicable Regulations Criteria pollutants, hazardous air pollutants, and carbon dioxide (CO 2 ) are released during cement manufacturing. Nitrogen oxide (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 air (thermal NO x ). EPA’s 2005 National Air Toxics Assessment (NATA) Inventory reports 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 sulfur-bearing compounds in coal, oil, and petroleum coke, as well as sulfur compounds in raw 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 flue gas p asses through the calcination zone. Compared to long dry and wet kilns, preheater and precalciner kilns tend to be mo re effective at capturing fuel-generated SO 2 . Accordingly,

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 addition to bulk CO 2 but CO emissions are treated as CO 2 for computational purposes.1-11 oxidation of sulfur in the feed materials is likely to be the major component of total SO 2 emissions. The 2005 NATA Inventory reflects that cement kilns rel eased 133,000 metric tons ( 147,000 short tons) of SO 2 emissions in 2005. Particulate matter (PM) emissions result from quarr ying operations (the crus hing and grinding of raw materials and clinker) as well as kiln line operations . The 2005 NATA Inventory estimates show that, in 2005, cement kilns released 10,000 tons of PM 10 emissions. The cement industry is also a source of HAPs (e.g., hydrochloric acid vapor and chlorine), as well as metals including but not limited to mercury, antimony, cadmium, and lead (EPA, 2008). The cement manufacturing process is also a source of carbon dioxide (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 CO 2 from fuel combustion can be calculated from heat input and fuel characteristics combined with clinker production. The CO 2 from calcination can be calculated taking into account the amount of limestone used as a source of clinker calcium. Limestone currently is the predominant source of calcium for the clinker. Pure limestone would produce 0.44 tons of 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 woul d be proportional to the amount of substitute materials added. Multiple regulatory requirements to reduce criteria ai r pollutants and HAPs emissions currently apply to the cement industry sector. The New Source Performance Standards (NSPS) and the National Emissions Standards for Hazardous Air Pollutants (NESHAPs) are two of the federal requirements that apply to cement facilities. Additionally, state and local regulat ory requirements might apply to individual cement facilities depending on their locations. In 2008, 44 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 30 miles of) Class 1 areas, as shown in Figure 1-6. Class 1 areas are areas of special natural, scenic, recreational, or historic (national or regional) value for which the Prevention of Significant Deterioration (PSD) regulations provide special protection.1-12 Figure 1-6. Portland Cement Facilities and O 3 NAA, PM 2.5 NAA, and Class 1 Areas



### 1.3 Overview of ISIS ISIS, a sector-based dynamic programming model, will facilitate the analyses of emission reduction strategies for multiple pollutants while taking into account plant-level economic and technical factors such as the type of emission units (for cement –kiln), associated capacity, location, cost of production, applicable controls, and their costs. For each of th e emission reduction strategi es under consideration, the model is able to provide information on the following:

 Optimal (least cost) industry operation.

 Cost-effective controls to meet the demand for cement.

 Emission reduction requirements over the time period of interest.1-13 ISIS design allows for incorporating multiple industries within a multi-market, multi-product, multipollutant, and multi-region emissions trading fra mework. The objective function in ISIS maximizes total surplus and uses an elastic formulation of the demand function to estimate area under the demand curve. The ISIS code is written in the General Algebr aic Modeling System (GAMS) language. Input data, organized in various spreadsheets of a Microsoft Excel workbook, are passed on to the GAMS files. These input data consist of an industry database, which provides unit-level production, capacity, production cost, and emissions information. The co ntrols database provides information regarding applicable air pollution control technologies and their cost and emission control characteristics. A policy module is used to specify various parameters of in terest to the policy analyst, such as emissions cap, emission reduction scenarios, and discount rate. The i nput data, control data, and policy parameters are then transmitted to the optimization part of the ISIS model, where they are used to solve the selected base and policy cases. After solving, the results are post- processed to calculate values of various outputs of interest. The output data are exported to Excel spreadsheets for further analyses and graphical representation of selected results. Within an industrial sector, generally emissions ari se from four pathways: (1) on-site emissions due to combustion of fossil fuels for energy at plants; (2 ) on-site emissions due to processing of certain raw materials (e.g., limestone calcination in cement pl ants, non-energy uses of fossil fuels in chemical processing and metal smelting); (3) off-site emissions due to combustion of fossil fuels at power plants to generate the electricity needed by the industria l sector; and (4) overseas emissions associated with imports. These pathways are depicted in Figure 1-7. Also shown in Figure 1-7 are the potential options for abating emissions from industrial sectors and multimedia impacts. The options shown in green ar e pollution prevention measures and the ones in red are mitigation measures. Clearly, the integrated pi cture presented in Figure 1-7 makes a compelling case for considering commodity production/supply activitie s 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.1-14 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 Vi ew of Pollution Generation Pathways, Emissions Abatement Approaches, and Multimedia Impa cts for an Industrial Sector


### References for Chapter 1 APCA (1997). Comments on EPA’s Draft Economic An alysis of Air Pollution Regulations for the Portland Cement Industry (May 1996), prepared for American Portland Cement Alliance by Environomics, January 29, 1997. 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 (1998). June 1998. Regulatory Impact Analysis of Cement Kiln Dust Rulemaking . Washington, DC: U.S. Environmental Protection Agency. http://www.epa.gov/osw/nonhaz/industria l/special/ckd/ckd/ckdcostt.pdf , accessed October 21, 2008. EPA (2007a). Personal communication from Alvaro Li nero, Florida Department of Environmental Protection to Keith Barnett, US EPA, on December 4, 2007. EPA (2007b). 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.pdf , accessed October 21, 2008. EPA (2008). AP 42, Fifth Edition, Compilation of Air Pollutant Emission Factors, Volume I, Chapter 11: Mineral Products Industry. http://www.epa.gov/ttn/chief/ap42/ch11/final/c11s06.pdf , accessed October 21, 2008. Complete document: http://www.epa.gov/ttn/chief/ap42/ , accessed October 21, 2008.1-15 EPA (2009). EPA 2005 National Air Toxics Assessment Inventory. U.S. Environmental Protection Agency. October 2009. NAS (2004). Air Quality Management in the United States. National Research Council (U.S.), Committee on Air Quality Management in the United States, National Academies Press, Washington, 2004. http://books.nap.edu/catal og.php?record_id=10728 , accessed October 21, 2008. PCA (2005). Letter from David S. Hubbard, Director, Le gislative Affairs, Portland Cement Association. RE: Hours of Service of Drivers; Proposed Ru le (Docket Number FMCSA-2004-19608), March 10, 2005. http://www.cement.org/exec/DH OS%20Comments%203.10.05.pdf , accessed October 21, 2008. PCA (2006). U.S. and Canadian Portland Cement I ndustry: Plant Information Summary. Portland Cement Association, Skokie, IL, 2006. PCA (2008a). History & Manufacture of Portland Cement. Portland Cement Association. http://www.cement.org/basics/concretebasics_history.asp , accessed October 21, 2008. PCA (2008b). Practical Application of PCA Economic Forecast and Market Assessments. Portland Cement Association, Education & Training, August 12-13, 2008, Skokie, IL. (Ed Sullivan PowerPoint presentation). PCA (2008c). Forecast Report: Long-Term Cement C onsumption Outlook. By Ed Sullivan January 31, 2008 . http://www.cement.org/econ/pdf/L ong-TermFlashwinter2007nonmember.pdf , accessed October 21, 2008. PCA (2009a). PCA Annual Yearbook 2009. North Am erican Cement Industry. Portland Cement Association. PCA (2009b). PCA Capacity Report. Flash Report. Portland Cement Association’s Economic Research Department. Updated October 19, 2009. Staudt, J. (2009). NO X , SO 2 and CO 2 Emissions from Cement Kilns. Andover Technology Partners, Memorandum to Ravi Srivastava, March 10, 2009. USGS (2005). Background Facts and Issues Concerni ng Cement and Cement Data. U.S. Geological Survey, Open-File Report 2005-1152. http://pubs.usgs.gov/of/2005/1152/2005-1152.pdf , accessed October 21, 2008. USGS (2007a). Mineral Commodity Summaries. pp. 40-41, January 2007, http://minerals.usgs.gov/minerals/ pubs/commodity/cement/cemenmcs07.pdf , accessed September 24, 2009. USGS (2007b). 2005 Minerals Yearbook: Cement. U.S. Geological Survey, p. 16.2, February 2007, http://minerals.usgs.gov/minerals/ pubs/commodity/cement/cemenmyb05.pdf , accessed September 24, 2009. van Oss, H.G. (2008). Personal communication from He ndrik G. van Oss, USGS, to Elineth Torres, US EPA, on July 7, 2008.1-16 This page intentionally left blank.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 reducti on 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 cons umption patterns are given full consideration, has been developed over the past 6 decades. In SPE network models, in terregional economies are simulated by finding the balance of demand, supply, and trade that will r esult in competitive market equilibrium among the regions. Enke (1951) first demonstrated how the co st 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 progra mming formulation. In this type of formulation, the equilibrium for each market of a sector is equi valent to the quantities and prices that result while maximizing the sum of consumer and producer surp luses 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 program ming formulation of the SPE problem was developed by Duloy and Norton (1975). Using Figure 2-1, the definition of the suppliers’ surp lus corresponding to a quantity Q of a commodity 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 horizontal line P1-P minus the area under the inverse supply curve up to point P. Similarly, the consumers’ surp lus corresponding to a quantity Q is given by the area under the inverse demand curve up to point C minus th e area under the horizontal line C1-C. This area is the cumulative opportunity gain of all consumers w ho purchase the commodity at a price lower than the price they would have been willing to pay. It is evident from Figure 2-1 that the total surplus is maximized exactly when Q is equal to the equilibrium quantity QE. This is a very useful result, as it provides a method for computing the equilibrium. The total surplus concept has long been a mainstay of social welf are economics because it takes in to account the interests of both consumers and of producers (Samuelson and Nordhaus, 1977).
The broad modules and associated information flows u tilized in the ISIS framework are shown in Figure 2-2. While ISIS structure permits accounting for multiple products, the descr iption below is provided relative to one product to bring out the important elem ents of the formulation and not burden the reader 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-supplied data) are shown in bold and the variables, whose values are determined in th e optimization process, are shown in italics . This scheme helps in organizing the numerous data elements and variables, and hopefully makes it easier for the reader to relate the data element to descriptions in the previ ous 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 Market

ISIS Engine Industry Information • Products • Production capacities and

costs • Demand • Imports parameters • Transportation costs • Noted retirements and growth • Energy Intensity Production Technologies and Emission Abatement Approaches Availability, applicability,

cost, and performance Policy Parameters • Time horizon • Policy type

Model Outputs • Base case and controlled emissions • Production, Inter-market trades, imports • Commodity and allowance prices • Retirements and capacity growth • Controls installed • Costs • Other Economic Parameters • Discount rate • Escalation rates • Demand elasticity • Economic life

Fuels • Primary: coal, coke,

natural gas, coal+TDF • Substitute: tires, TDF Emissions • Sulfur dioxide • Nitrogen oxides • Carbon dioxide

Figure 2-2. Broad Modules and As sociated Information Flows Utilized in the ISIS Framework2-3 

### 2.1 Indexes, Sets, and Mappings Before we start the description of the mathematical st ructure of ISIS, it is helpful to define the sets of relevant entities and mappings describing th e relationships among various entities. Indexes (one-dimensional sets)

 ISIS data structures (sets and parameters), vari ables, 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, re placement, expansion, and new units. Subsets of i described below define more-specific populations of units;

o iE(i) is the set of existing units

o irepl(i) is the set of replacement units

o iExp(i) represents the set of expansion units

o iNew(i) represents the set of new units

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

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

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

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

 ee is the set of energy efficiency measures; and

 r is the set of geographic regions where units are located. Mappings The mappings used in ISIS are:

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

 poltikjee(t,i,k,j,ee) is the mapping relating availability, a pplicability, and the ability to change emissions of controls k and energy efficiency measures ee to unit i, pollutant j , and year t ;

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

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

 poltirk(t, ri(i,r),k) is the mapping relating availability and applicability of controls k to unit i located in geographic region r and year t ; and2-4  poltiree(t, ri(i,r),ee) is the mapping relating availability and applicability of energy efficiency measures ee to unit i located in geographic region r and year t

. 

### 2.2 Objective Function As mentioned above, the objective function solved in the ISIS model corresponds to maximizing the total surplus (or minimizing the negative of the total surplu s) 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 above are defined for year t as follows, dis(t) is the discount factor, tannualprodncost(t,i) is the annual production cost ($) for production unit i , ttranscost(t,dc) is the cost ($) of transporting the product to demand center dc , timportscost(t,id) is the cost ($) of importing foreign product in import district id , tcontrolcost(t,i k) is each unit’s total control cost ($) using control technology k , teemeasurescost(t, i, ee) is each unit’s total cost ($) usi ng the energy efficiency measures ee , alprice(t,j) is the allowance price input by the user, totalemissions(t,j) is emission of pollutant j from the sector, 

# 

 dc 

### 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 schem e. This approximation is explained below. Stepwise Approximation of Demand Curves in ISIS For clarity, the subscripts t and dc corresponding to time and demand center are dropped in the following explanation. The relationship between demand and price in a market dc is expressed as

σ 1 

### D0 P0      

 D D P )

( (2.2.2)

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

 is the elasticity of demand relative to price, and
D0 and P0 are the initially-specified demand quantity and price, respectively. Figure 2-3 shows a representation of the above 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 Curve2-6 First the range of the demand-price curve is defined 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 is 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 approximation. In ISIS a value of 0.5 is used for range
. Next, a series of steps is defined within the interval 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. Incr easing the value of this parameter will increase precision of the stepwise approximation, but will increase the model size. In ISIS, a value of 100 is used for Number of steps

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



### 2 1)

- dstep (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-demand 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 function 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 optimum solution. Equation (2.2.7) is used to generate the price-de mand curves for all time periods and demand centers. However, this equation needs specifications of on e point on each demand function in each time period (i.e., [P0{t, dc}, D0{t, dc}]) . To determine such points, a single run of the inelastic version of the ISIS model (with exogenous D0[t, dc]) is made and then the resulting shadow prices P0(t, dc) are used in Equation (2.2.7) to generate price-demand curves fo r all time periods and demand centers. These curves are used in the last term of the objective function an d in the supply constraint, Equation (2.3.1), presented in the next section. 

### 2.3 Supply The demand for a commodity in a mark et can be satisfied by domestic production and foreign imports as follows:

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

#      dstep id i dstep dc t demand dc id t antity importedqu dc i t ity prodnquant (2.3.1)

where prodnquantity(t,i,dc) is the quantity supplied from the domestic production unit i to demand center dc in year t , importedquantity(t,oi,id,dc) is the quantity of commodity received from the origin (country) of imports oi at the domestic import district id and supplied from that district to demand center dc in year t , and demand(t, dc,dstep) is the demand for the commodity at the dstep th level of the price-demand curve for the demand center dc in year t . The construction of the price-demand curves has been explained above.2-8 Note that transportation of quantity from producti on 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 manufacturing unit i in year t . Note that, in general, production can be from existing units, units added at a plant (i.e., expansion units), newer units replacing units at a plant (i.e., replacement units), an d new kilns. Treatment of these is explained in a subsequent section. Production of a unit is constrained by its capacity. So, 

### i). , capacity(t  ) , ( i

t prodn (2.3.3)
where capacity(t, i) is the capacity (tons product/year) of manufacturing unit i in year t

. The sum of all imported quantities supplied from an import district id to various demand centers dc in year t must equal the imports available at that import district in that year. Then, 

#    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 district id to demand center dc in year t, and Imports(t,oi,id,istep) is the imported quantity available at the import district id in year t at the istep th level of the imports curve for country oi . Construction of the country (or region) and import district specific imports-cost curves is explained below. Stepwise Approximation of Imports Curves in ISIS The treatment of imports is similar to the treatment for elastic demand curves. Again, for clarity, the subscripts t, oi, and id corresponding to time, origin of imports, and import district are dropped in the following explanation. The relationship between imports arriving from oi at id and their price is expressed as

 1 

### Imports0 Iprice0        

 Imports orts) Iprice(Imp (2.3.5)2-9 where Imports is the imports of the commodity arriving from oi at id with corresponding imports price (value) Iprice ,


###  is the elasticity of imports relative to imports price, and {Imports0, Iprice0} is a point on the applicable quantity-price curve. First the range of the imports-price curve is defined using 

### 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 approximation. In ISIS a value of 0.5 is used for range
. Next, a series of steps is defined within the interval I min - I max with the width of each step given by



### steps of Number I

- I Iwidth min max 

###  (2.3.8)

where Number of steps is another user-supplied pa rameter. Increasing the value of this parameter will increase precision of the stepwise approximation, but will increase the model size. In ISIS, a value of 100 is used for Number of steps

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



### 2 1)

- istep (2 Iwidth I Imports min istep 

###     (2.3.9)
Using the above information, the imports-price curve is determined

 1 istep istep 

### Imports0 Imports Iprice0 Imports Iprice        

 ) ( (2.3.10)

Equation (2.3.10) is used to genera te the imports-cost curves for all time periods, origins of imports, and import districts. However, this equation needs specifi cations 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 IS IS model (with exogenous Imports0[t, oi, id] corresponding to capacities of2-10 import districts) is made and then the resulting Iprice0(t, oi, id) are used in Equation (2.3.10) to generate the imports-cost curves for all time periods, origins of imports, and import districts. 

### 2.4 Production Capacity and Supply Costs The ISIS model includes constraints for ensuring th at endogenous production capacity changes occur in a realistic way. This section describes how the capacity changes take place in the ISIS framework and the treatment of related costs. Note that various cost elem ents (e.g., capital cost of a unit [$/ton clinker] in the cement sector) are escalated appropriately to reflect values in years of interest. Production Capacity Related Constraints Added capacity in year t is given by
When t = 1,

(2.4.1a)

For t > 1,



##  

. 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,
timeblock is the block of years used in simulation , and techlifeplants is the technical life of a unit. Only one vintage of a unit is possible for the period starting when the vintage comes on line and ending with its technical life.2-11 (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., repl acement units, expansion units, and new capacity) and projected units, (2) annual fixed operation and main tenance (FOM) costs, (3) annual variable costs associated with use of labor, raw material, fuel, electricity, and operation and maintenance, (4) annual transportation costs, and (5) annual cost of imports. These costs are described below. Capital Recovery If the existing units are paid for and do not have capital recovery costs, then 

##  

. 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 known, 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 the cement sector) of i th unit in year t, and CRFplant is the capital recovery factor calculated using an appropriate interest rate and time period, ecolifeplants , for capital recovery. Annual capital costs for all populations except existing and projected is 

### CRFplant i t Addcap st(t,i) plantcapco
   i) , capcostt(t ) , ( (2.4.5)

where capcostt(t,i) is the capital cost (e.g., $/ton of clinker for the cement sector) for the i th unit
. Variable Costs The annual variable cost at a unit is calculated using2-12 

##  

. ) , ( ) , ( 

#         

sw 

### (t,i,SW) SWdispcost ) tncost(t,i H2Oconsump i t fc i t 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 product) at unit i in year t , LBRt(t,i) is the cost of labor ($/ton product) at unit i in year t , ELCt(t,i) is the cost of electricity use ($/ton product) at unit i in year t , fc(t, i) is the cost of fuel ($/ton product) at unit i in year t , and varcost(t,i) is the annual variable cost ($/ton product) at unit i in year t.
The fuel cost for a unit is calculated as follows:



### . 1 ]] 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 unit i , fuelcostt(t,i, f) is the cost of fuel f ($/MMBtu) at unit i in year t , and varprodn(t,i,f,m,j,k,ee) is a production coefficient described in the next section. Annual water consumption re lated 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 by 

### ). , ( 2 i t 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 by2-13 

### sw) costt(t, SWdisposal
  sw) SWgen(t,i (t,i,sw) SWdispcost , (2.4.10) With annual solids discharges given by 

### ). , ( i

t 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 product at unit i

. Note that SWdisposalcostt(t,sw) value can be positive (disposal cost) or negative (sale price).
Total Annual Cost of Production Using the above information, the total annual cost of production at unit i in year t is



### . 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 origin 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 district. 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 id t quantity Imported dc i t quantity prodn (t,dc) ttranscost

id i 

#  

    dc) id, (t, sportcostt [imprttran dc) i, (t, sportcostt [prodntran

(2.4.14)2-14 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. Fu rther, the ISIS framework includes algorithms to account for tracking multiple pollutant streams asso ciated with uncontrolled emissions, controlled emissions, pollution prevention from process modifications 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 pollutant j resulting from processing (firing) fuel f and application of any control k and/or energy efficiency measure ee at unit i in year t, taking into account whether a unit is available for operation in that year; emintensityfuel(i,f,j) is the emission intensity (tons pollu tant per ton product) of pollutant j resulting from processing (firing) fuel f at unit i, taking in to account whether any controls (e.g., Best Available Control Technology [BACT]) are already installed on the unit; modeintensity(i,k,ee) is the modified energy intensity (MMBtu per ton product) needed to produce 1 ton of product at unit i . This energy intensity takes into acc ount any heat input changes accompanying a control k and/or an energy efficiency measure ee; eintensity(i) is the energy intensity (MMBtu per ton product) needed to produce 1 ton of product at unit i; primaryHIchange(i,k) is the change in primary heat input (MMBtu per ton product) due to application of control k at unit i; secondaryHIchange(i,k) is the change in heat input (MMBtu per ton product) due to any secondary fuel addition resulting from application of control k at unit i;2-15 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 pe r ton product) of pollutant j resulting from processing raw material m and application of any control k and/or energy efficiency measure ee at unit i in year t, taking into account whether a unit is available for operation in that year; emintensityrmt(i,m,j) is the emission intensity (tons pollu tant per ton product) of pollutant j resulting from processing raw material m at unit i, taking into account whether any controls (e.g., BACT) are already installed on the unit; primaryRMTchangepercent(i,k) is the percent change in primary raw material input (tons raw material per ton product) due to application of control k at unit i; and secondaryRMTchangepercent (i,k) is the percent change in raw material input (tons raw material per ton product) due to any secondary raw materi al 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)
where pol(t,i,f,m,j,k,ee) is emission (tons pollutant per ton product) of pollutant j resulting from processing fuel f and raw material m, and application of any control k and/or energy efficiency measure ee, at unit i in year t, taking into account whether a unit is available for operation in that year. Emissions of pollutant j at unit i in year t are given by



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

# 

  ee) j, k, i, t, poltikjee( m, f, 

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

where2-16 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 clinker) of pollutant j at unit i ; and poltikjee(t,i,k,j,ee) is the mapping described above.
Now total emissions from all units are



### . ) , , ( ) , ( 

#   i 

### j i t emissions j t ions totalemiss (2.5.6)

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



### ; ); , , , , ( ) , , , , ( jj j jj m f i t prodnpol j m f i t prodnpol   (2.5.7) with,



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

#  
ee j k i t poltikjee 

### ee k j m f i t varprodn j m f i t 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 th at endogenous applications of sector-based controls and energy efficiency options occur in a realistic ma nner. A description of these constraints and costs for controls is presented in this section. The treatment of energy efficiency measures is described in a subsequent section.
Controls Related Constraints Only one vintage of a control on a unit is possible for the period starting when the vintage comes on line and ending with its technical life.

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

   

# 

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

, ) (

_ (2.6.1)2-17 where

v is the set of vintages of a control for unit i , vyear(v) and tyear(t) are parameters with values corresponding to years in the selected time horizon, ts_c(t,i,k,v) is a binary variable that can bring v th vintage of control k for unit i online in year t, and techlifecontrols(k) is the technical life of control k. Control capacity is given by the following equations.
For t = 1,

. 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 coexist on a unit, 

### ) , ( ) , , ( ) , , ( i t capacity kk i t ControlCap k i t ControlCap   (2.6.2c)
where
k and kk are incompatible controls. Finally, the production coefficient asso ciated with a control is less than the capacity of the unit the control is installed on,



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

_ pollutants of Number ) , , , , (



###    

#  

v ee j k i t 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 comprise the following components: (1) capital and fixed operation and maintenance costs, (2) costs associated with any reagent and/or catalyst consumption, (3)2-18 costs associated with any reduction in fuel and/or raw material use, (4) cost associated with electricity consumption, (5) cost associated with byproduct(s) , and (6) costs associated with water use. The calculations of these costs are described below. Note that various cost elements (e.g., capital cost of a control [$/ton clinker] for the cement sector) are escala ted appropriately to use values in years of interest. 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 ($/ton of clinker) of application of control k on i th unit , cntrlfixedcostt(t,i,k) is the annual fixed operation and mainte nance (FOM) cost ($/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 us ing 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 i t poltikjee kf(k,f),m, j,ee k i t 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 f

uelcostch

(2.6.6)

where primaryHIchange(i,k) is the primary heat input change (MMBtu per ton clinker) with use of k th technology, secondaryHIchange(i,k) is the primary heat input change (MMBtu per ton clinker) with use of k th technology, fuelcostt(t,r,f) is the regional cost of fuel ($/MMBtu) in year t, ri(i,r) is a set with elements corresponding to mappi ng between kiln and their geographic locations, and kf(k,f) is a set with elements corresponding to mapping between fuels and technologies. poltirk(t, ri(i,r),k) is a mapping described above. Change in raw material cost associated with application of controls is given by

. ] 100 / 100 / [

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

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

#  

     

j,ee k i t ee ,m,poltikj f i fi j,ee k i t 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 R MTcostcha

(2.6.7)

where primaryRMTchangepercent is the percent change in primary raw material with use of k th technology, secondaryRMTchange(i,k) is the percent change raw material input corresponding to any secondary raw material addition with use of k th technology, and2-20 RMTt(t,i) is the unit-specific cost of raw material ($/ton clinker) in year t. Annual reagent consumption costs are



### rgnt). cet(t, reagentpri
  

# 
rgnt 

### rgnt k i t c pt rgntconsum k i t 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 i t ,poltikjee f i fi j m ee j k i t 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 reagent/tons clinker) associated with fuel -based emission intensity, reagentconsumptrmt(i,m,k,j,rgnt) is the reagent consumption due to control (tons reagent/tons clinker) associated with raw material-based emission intensity, reagentpricet(t,rgnt) is the price of reagent rgnt ($/ton of reagent) in year t , and rgntconsumpt_c(t,i,k,rgnt) is the annual reagent consumption due to control (tons reagent/year). Catalyst consumption cost is



### ; ) ) , , , (

_ ) , , ( (

_ 

# 

 

cat 

### cat k i t 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 i t 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 b yproduct generation disposal/sale is



### byprod); ricet(t, byproductp   

#  byprod 

### prod) c(t,i,k,by en byproductg ) k i t 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 i t ,poltikjee f i fi m,j ee j k i t 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 reagent/tons clinker) associated with fuel -based emission intensity, byproductprodnrmt(i,m,k,j,byprod) is the byproduct generation due to control (tons reagent/tons clinker) associated with raw material-based emission intensity,2-22 byproductpricet(t, byprod) is the price of disposing or selling the byproduct byprod ($/ton of reagent) in year t , and byproductgen_c(t,i,k,byprod) is the annual byproduct generation due to control (tons reagent/year).
Note that byproductpricet value can be positive (disposal cost) or negative (sale price). Annual electricity consumption (kWh/yr) due to control is

. ] [ ) , , ( ) , ( ) , , , , ( , pollutants of Number k) f, i, kWhperton( 

#  



 f i fi ee j k i t 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 i t poltik kwhcost_c( ) , , ( (2.6.15)
where KWhperton(i,f,k) is the electrical requirement (kWh per ton of clinker) for technology k , and electricitycostt(t,r) is the electricity price ($/kWh) in year t at unit i. Annual water consumption a ssociated 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 i t ,poltikjee f i fi m,j ee j k i t 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 r eagent/tons clinker) associated with fuelbased emission intensity,2-23 H2Ousermt(i,m,k) is the water use due to control (tons reagent/tons clinker) associated with raw material-based emission intensity, H2Ocostt(t) is the price of water ($/1000 gallons) in year t , and H2Oconsumpt_c(t,i,k) is the annual water use due to control (gallons/year).
Total Annual Cost Using the above costs, the total annual cost of controls is



### 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 efficiency 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 the 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.2-24 Energy efficiency measure capacity is given by the following equations.
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 coexist on a unit, 

#   1 ; ) , , (

_ ) , , (

_ 

#   

v 

### ,v) eee i t ee(poltiee ts ,v) ee i t ee(poltiee ts (2.7.2c)

where

ee and eee are incompatible measures. Finally, production coefficient associ ated with an energy efficiency me asure 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 are 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 ($/ton of clinker) of ee th energy efficiency measure application on i th unit , efixedcostt(t,i,ee) is the annual fixed operation and mainte nance (FOM) cost ($/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 i t 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 per 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 mappi ng between kiln and their geographic locations, and poltiree(t, ri(i,r),ee) is a mapping described above. Annual electricity consumption (kWh/yr) due to an energy efficiency measure is2-26 

### pollutants of Number

ee) i, kWhperton( 

# 



 ) , , , , ( , , 

### ) , , (

_ ee j k i t poltikjee m f 

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

### i) ycostt(t, electricit
  ,ee)

kwh_ee(t,i ) ee i t (poltiee kwhcost_ee ) , , ( (2.7.8)
where eKWhperton(i,f,ee) is the electrical requirement (kWh per ton of clinker) for ee, and electricitycostt(t,i) is the electricity price ($/kWh) in year t at unit i.
Total Annual Cost Using the above costs, the total annual co st of energy 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 em issions reduction policy options including cap-andtrade, emissions taxes, and emissi ons limits. Additionally, appropriate combinations of these options can also be evaluated. The policy options included in ISIS are described below. Cap-and-Trade Under this option, an emissions cap is set on the amount of a pollutant that can be emitted. Sources, companies, or other groups are issued emission 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 those banked in previous periods cannot exceed the cap in the current period. Sources or companies that need to increase their emissions must buy allowances from those who pollute less. The transfer of allowances is referred to as a trade. In effect, the buyer is paying a charge for polluting, while the selle r is being rewarded for having redu ced emissions by more than was needed. Thus, in theory, those that can easily re duce emissions most cheaply will do so, achieving the pollution reduction at the lowest possible cost to society. Generally, annual caps have been utilized in ongoing programs (e.g., Title IV SO 2 reduction program). However, ISIS does permit evaluation of potential pr ograms with caps over us er-selected time periods (e.g., 5-yearly caps). This evaluation is accomplished using, ) , ( ) , ( ) , ( j period t bnk j t bnk j t ions totalemiss 

   j) ec(t, (2.8.1)
where ec(t,j) is the emission cap for pollutant j in year t , and2-27 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, th is requirement 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 requirement for pollutant j in year t

. Emissions Limits ISIS permits evaluation of the costs and emissions reductions associated with more traditional emission reduction programs utilizing unit-specific rate-based emission limits. Such requirements are imposed using ) , ( ) , , ( i t prodn j i t 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 endoge nously determining the level of emission reduction corresponding to a certain allowance price. This info rmation may be useful, for example, in a situation where an allowance price is set for reducing emissions from many industrial sectors. In such a case the levels of emission reductions corres ponding to the same allowance price may be different for the sectors under consideration. The emissions response to a given a llowance 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

. Note that the allowance price inputs scheme above is equivalent to emission-tax-based programs in which affected units or companies pay a tax for every unit of pollution they produce. Thus this scheme can also be used to evaluate such programs. 

### 2.9 Optimization and Post-Processing In ISIS, the input data are pre-processed to arrive at suitable input parameters for use in the model equations explained in this chapter. Once the data have been pre-processed, ISIS solves for the2-28 appropriate levels of production, im ports and controls required to meet the constraints associated with commodity demand and emissions, while maximizing total surplus. Once the surplus maximization problem has been solved, the results are post-processe d to obtain parameters and level values of the variables of interest. The key variables of interest are: production level of each production unit to meet regional demand, level of imports in each region, installation of vari ous controls, emissions, and various costs. Output data are written in appropriate work sheets in an Excel workbook and further linked to various plots to enable visual presentation and analyses of the results.2-29 

### References for Chapter 2 Duloy, J. H.; Norton, R.D. (1975). Prices and incomes in linear programming models. American Journal of Agricultural Economics . 57(4): 591.600. Enke, S. (1951). Equilibrium among spatially separated markets: solution by electric analogue. Econometrica . 19: 40.47. FPL-PELPS, A price endogenous linear programming system for economic modeling, supplement to PELPS III, version 1.1, USDA Research paper FPL-RP-614, 2003. McCarl, B.A.; Spreen, T.H. (1980). Price endogenous mathematical programming as a tool for sector analysis. American Journal of Agricultural Economics . 62: 87.102. Samuelson, P.A. (1952). Spatial price equilibrium and linear programming. American Economic Review

. 42(3): 283.303. Samuelson, P.A.; and W. Nordhaus. (1977). Economics (17th edition) , John Wiley.3-1 

## Chapter 3 Input Data for ISIS-Cement Model
3.1 Data Requirements Data requirements for ISIS include sector-specific (in this case, cement-specific) data as well as policy and economic parameters. The cement-specifi c data requirements are discussed below. 

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

 Industry production, fuel, and emissions;

 Control technologies, and emission abatement approaches; and

 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 168 cement kilns that were in existence in 2009 and PCA’s projected capacity expansions from 2010 to 2012, as shown in Table 3-1 (PCA, 2009b). Additionally, the model also includes potential kiln representations that may come on line as a result of endogenous capacity addition [i.e., new production capac ity (by state) and replacements (by kilns)]. Table 3-1. Summary of Kilns Modeled in the ISIS Cement Industry Model Kiln Population Number of Kilns Existing (2009) 168 3 PCA’s Projected New Kilns (2010 – 2012) 9

Each kiln modeled in ISIS is characterized by its locat ion, design (i.e., wet, dry, preheater, or precalciner), daily and annual clinker capacity 4 , vintage, and retirement information when available (PCA, 2006). In addition, each kiln is characterized by its average variable costs components (AVC).

3 Does not include 2 white cement kilns ne ither 2 cement kilns in Puerto Rico. 4 Annual clinker capacity as reported by PC A refers to the kiln daily capacity mul tiplied by 365 less normal downtime days, where daily capacity is the normal clinker output a kiln can pr oduce per day given a realistic work pattern and normal downtime days are number of days of downtime required for mainte nance, repair or cleanup.3-2 3.2.1.2 Average Variable Costs In previous economic analyses, five input variab les in cement production have been identified to determine the kiln-level AVC functions: raw materials, repair and maintenance, labor, electricity, and fuel (Depro, 2007, Depro, 2010, Depro and Lentz, 2010). Raw materials serve as the kiln feed, and repair and maintenance are required for periodic upkeep of the kiln . Labor is used in the quarry, in the operation of the kiln, and for packing. Electricity is consumed ma inly by the auxiliary equipment and fuel is largely consumed in the kilns. The AVC for raw materials, labor, repair and maintenance, and electricity was determined following the methodology in the EPA regulatory impact analysis of the cement kiln dust rulemaking (EPA, 1998).
3.2.1.3 Cement Demand Centers The U.S. cement markets are organized in st ate-specific demand centers. Figure 3-1 shows the distribution of Portland cement kilns operating in 2009. Each state containing at least one kiln is shaded. The ISIS-cement model simulates each cement plant’s ability to compete in each of the demand centers as a function of the plant’s production cost and transpor tation cost associated with supply to each demand center.
3.2.1.4 Portland Cement Demand One of the key data inputs for the ISIS-cement model is the demand projection for each demand center. In general, this demand is a function of gross domes tic product (GDP) growth, interest rates, special construction projects (e.g., highways), and public s ector construction spending. Portland cement demand was 128 million metric tons in 2005. PCA expects cem ent demand will reach 192 million metric tons by 2035, which reflects an increase of nearly 64 million me tric tons with a compound annual growth rate of 1.4%. Cement demand through year 2035 is repor ted in the PCA Long-Term Cement Consumption Outlook (PCA, 2009c). PCA projections of cement de mand (in million metric tons) by state for 2005 through 2035, in 5-year increments, are shown in Table 3-2.3-3 Figure 3-1. Distribution of Cement Kiln s in the United States as of 20093-4 Table 3-2. Portland Cement Demand in Millions of Metric Tons State Demand Center 2005 2010 2015 2020 2025 2030 2035 Alabama 1.92 1.45 2.02 2.16 2.32 2.49 2.67 Arizona 4.77 2.21 3.85 5.16 6.55 7.95 9.59 Arkansas 1.30 0.93 1.29 1.41 1.54 1.67 1.81 California 16.01 8.33 13.88 15.43 17.11 18.95 20.96 Colorado 2.55 1.74 2.76 3.08 3.47 3.90 4.33 Connecticut 0.82 0.54 0.77 0.83 0.88 0.94 0.98

Delaware 0.22 0.18 0.26 0.28 0.29 0.31 0.32 District of Columbia 0.21 0.15 0.18 0.18 0.17 0.18 0.18 Florida 12.28 4.77 8.99 11.05 13.47 16.33 19.67 Georgia 4.75 2.52 3.95 4.96 5.67 6.17 6.65 Hawaii 0.44 0.35 0.47 0.49 0.52 0.55 0.57 Idaho 0.70 0.44 0.70 0.82 0.92 1.03 1.15 Illinois 4.64 3.10 4.29 4.62 4.99 5.44 5.98 Indiana 2.27 1.69 2.30 2.49 2.73 2.98 3.25

Iowa 1.94 1.59 1.98 2.04 2.10 2.15 2.20 Kansas 1.55 1.36 1.65 1.71 1.78 1.85 1.91 Kentucky 1.60 1.06 1.48 1.59 1.71 1.84 1.97 Louisiana 2.23 2.44 2.91 3.15 3.29 3.42 3.52

Maine 0.35 0.20 0.29 0.31 0.32 0.34 0.35 Maryland 1.66 1.12 1.63 1.78 1.94 2.12 2.31 Massachusetts 1.26 0.81 1.11 1.21 1.31 1.43 1.56 Michigan 3.06 1.54 2.05 2.19 2.44 2.68 2.75 Minnesota 2.06 1.29 1.86 2.09 2.34 2.57 2.80 Mississippi 1.14 1.07 1.50 1.58 1.66 1.76 1.85

Missouri 2.87 2.03 2.71 3.18 3.58 3.90 4.29 Montana 0.38 0.30 0.42 0.45 0.48 0.51 0.53 Nebraska 1.37 1.14 1.46 1.55 1.66 1.76 1.87 Nevada 2.63 1.20 2.33 3.26 4.27 5.14 6.16 New Hampshire 0.37 0.23 0.33 0.36 0.39 0.42 0.45 New Jersey 2.06 1.45 1.89 1.99 2.09 2.18 2.28 New Mexico 0.91 0.65 0.94 1.00 1.07 1.12 1.153-5 State Demand Center 2005 2010 2015 2020 2025 2030 2035 New York 3.29 2.84 3.60 3.73 3.87 3.98 4.08 North Carolina 3.25 2.16 3.43 3.92 4.50 5.16 5.88 North Dakota 0.36 0.39 0.47 0.47 0.46 0.44 0.42 Ohio 4.06 2.59 3.61 3.75 3.90 4.02 4.12 Oklahoma 1.67 1.57 1.68 1.76 1.87 1.98 2.11

Oregon 1.24 0.83 1.22 1.40 1.61 1.86 2.15 Pennsylvania 3.44 2.61 3.49 3.64 3.78 3.89 3.97 Rhode Island 0.19 0.12 0.17 0.18 0.19 0.19 0.20 South Carolina 1.94 1.07 1.81 1.99 2.19 2.38 2.56 South Dakota 0.48 0.44 0.55 0.57 0.61 0.64 0.67

Tennessee 2.52 1.54 2.38 2.61 2.89 3.19 3.53

Texas 15.09 12.11 17.51 21.03 24.10 27.48 30.58 Utah 1.53 1.22 1.82 2.08 2.40 2.78 3.23 Vermont 0.16 0.11 0.16 0.17 0.18 0.20 0.21 Virginia 2.87 1.87 2.84 3.13 3.45 3.80 4.17 Washington 2.24 1.68 2.50 2.75 3.04 3.36 3.73 West Virginia 0.54 0.47 0.59 0.60 0.60 0.59 0.58

Wisconsin 2.37 1.59 2.18 2.32 2.45 2.59 2.74 Wyoming 0.47 0.43 0.55 0.55 0.55 0.55 0.54

Total 128.03 83.54 122.81 139.06 155.70 173.14 191.51

3.2.1.5 Transportation-Interregional Trade In ISIS-cement, a transportation matrix is used to describe the costs for transporting cement from kiln and import district locations to demand centers. To deve lop these costs, information on distances between supply and demand points and costs of transportation mode s (truck, rail, or water transport) was obtained. In particular, the Transportation Routing Analysis GIS (TRAGIS) model (TRAGIS, 2003) was used to develop the origin-destination distances. Also, in the matrix, the applicable lowest cost transportation option is used to connect a supply point with a destination. While the cement demand centers are interlinked through a transportation matrix, the comp etition is generally mainta ined on a regional level because the cost of transporting cement is relatively high. 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 (2007), nearly half of cement shipments reach customers via terminals rather than direct. Sh ipments to terminals are more than 80% by rail or water, rather than by truck. Table 3-3 provides information on cost per ton mile for bulk shipping via truck, rail, and barge. In ISIS-cement, transporta tion costs for each mode of transportation (truck, rail, and barge) are calculated from each kiln and import di strict to each demand center. For a given origin-3-6 destination pair, the dominant mode (lowest cost of sh ipment) is used to determine the transportation cost for that route. The TRAGIS model was used to arri ve at the origin-destination distances and feasible routes for rail and barge; Google Maps was used for truck transportation. The shipment costs shown in Table 3-3 were used to determine the transportation costs for the routes in ISIS. Table 3-3. Bulk Shipment Costs (Cents per Metric Ton per Mile)

Mode Journal of Transport Geography Report, 2005 N&S American Cement, 2004 American University, 1994 Averages Used in Model BTS 1 WDOT 2 NACT 3 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 i ndustry is operating at-cost or at a profit". 2 (Washington State Department of Transportation) Cost study to examine options for the transport of wheat (Jessup and Casavant, 1998). 3 (North American Cement Transportation) Report covers cem ent-specific transportation costs, assumes short tons-- converted to metric.
3.2.1.6 Imports U.S. cement markets receive imported quantities of ce ment and clinker from a number of countries, and these imports arrive at more than 30 import district s (USGS, 2009). In ISIS-cement, international supplies from exporting countries to U.S. import districts ar e modeled using supply elasticity and then these imports are transported to the demand centers. The five largest international suppliers of cement and clinker to the U.S. are China, Thailand, Venezuela, South Korea, and Greece. An econometric study was 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 study (Burtraw, 2010) reflected that the best estimate of th e international supply elastic ity of cement and clinker from China, Thailand, Venezuela, South Korea, Greece, and the rest of the world into the U.S. is 3.94. This value indicates that if the price of cement were to increase by 1% within any import district in the U.S., then, ceteris paribus , the quantity of cement imported from each of these five supply countries into that district would increase by 3.94%. Table 3-4 shows the import levels by USGS Customs District, then state, for 2005, estimated by using USGS data (USGS, 2007).

Table 3-4. Portland Cement and Clinker Imports in Million Metric Tons, by Customs District in 2005
USGS Customs District Quantity, million metric tons Alabama, Mobile 0.51 Alaska, Anchorage 0.14 Arizona, Nogales; Mexico 1.073-7 USGS Customs District Quantity, million metric tons 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 4.18 Maine, Portland 0.16 Maryland, Baltimore 0.13 Massachusetts, Boston 0.13 Michigan, Detroit 1.32 Minneapolis, MN, Canada 0.04 Minnesota, Duluth; Canada 0.16 Missouri, St. Louis 0.01 Montana, Great Falls 0.06 New York, Buffalo 0.82 New York, New York 1.26 New York, Ogdensburg 0.33 North Carolina, Wilmington 0.39 North Dakota, Pembina; Canada 0.18 Ohio, Cleveland 0.79 Oregon, Columbia-Snake 0.87 Pennsylvania, Philadelphia 0.49 Rhode Island, Providence 0.74 South Carolina, Charleston 1.1

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 0.133-8 USGS Customs District Quantity, million metric tons Virginia, Norfolk 0.7 Washington, Seattle 1.49 Wisconsin, Milwaukee; Canada 0.19 Total 33.33 Source: USGS, 2007. Data do not include Puerto Rico

3.2.1.7 Capacity Changes Cement plants have a relatively long lifespan, ty pically 50 years or more (FLSmidth, 2007). Various factors, including (but not limited to) raw material availability in the quarry, technology changes, productivity, efficiency, longevity, reliability, maintenan ce, and long-term costs can affect the lifespan of a cement kiln/plant. In ISIS-cement, retirements from 2002 to 2009 and projected retirements after 2009 of existing kilns were based on information from 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, 2004a ). Further, as mentioned earlier, ISIS-cement includes algorithms for endogenous capacity growth and retirement of kilns. To determine capital recovery factor for capital costs asso ciated with kiln capacity changes, an economic life of 25 years and an interest rate of 15% are used. Capital costs in 2005 $ per short ton of clinker for new, replacement of wet, and replacement of dry capacity are 208, 296, and 238, respectively (PCA, 2009a).
3.2.1.8 Fuel Intensity The Annual Energy Outlook (2008) energy use profile for 2005 is shown in Figure 3-2. In 2005, the cement sector consumed 451.2 trillion Btu (476.0 trillion kJ) of energy (EIA, 2008c). As shown in Figure 3-2, the primary fuel being burned in kilns is coal. Co al is projected to remain the dominant fuel used by the U.S. cement industry. However, there has been an increasing trend towards using other fuels, particularly alternative fuels, such as coke, 5 waste tires, and other wastes, especially oily wastes.

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. Ce ment Industry in 2005.

5 PCA does not specify if “coke” is me tallurgical coke or petr oleum coke. Authors beli eve it is the latter.3-9 Source: EIA, 2008a
In the ISIS-cement model, to determine the fuel intens ity of each kiln, correlations of kiln type to heat input and/or gas flow were deve loped (see Appendix A). Once determined, the kiln’s specific fuel intensity is used to calculate fuel cost for each kiln . PCA’s data on heat input to various kilns by type were used to develop kilns’ fuel intensities. The data , expressed in heat input per unit of clinker (specific fuel consumption [SFC]) and exhaus t gas flow rate (wet) (EGFW) per unit of clinker, are summarized in Table 3-5.

Table 3-5. Specific Fuel Consumption and Total Exhaust Gas Flow Rate (wet) for Various Kiln Types
Kiln Type SFC EGFW MMBtu/ton a Nm 3 /kg SCF/ton a 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 short t on Note: SFC=specific fuel consumption; Source: EPA, 2007a (Table 3-3) EGFW=exhaust gas flow rate (wet): Source: PCA, 2004 (original data in metric units)
For each individual kiln, the ISIS model determines th e optimal fuel type(s) based on the regional cost of the chosen fuel and the kiln’s specific fuel intensity.
3.2.1.9 Emissions The design of the ISIS Model can accommodate any num ber of pollutants of interest. In ISIS-cement, each kiln is characterized by its NO x , SO 2 , PM, hydrochloric acid (HCl), mercury (Hg), total hydrocarbon (THC), and CO 2 emission intensities. The characterization by kiln of the NOx, SO2, PM, HCl, mercury and THC emissions is presented in "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS" (August 6, 2010) (EPA, 2010).
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 air (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/t on of clinker and in lb/MMBtu (EPA, 2007a).3-10 Table 3-6. Estimated Uncontrolled NO X Emission Intensities for Cement Kilns

Kiln Type Heat Input, MMBtu/ton of clinker Uncontrolled NO x Emissions
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 sulf ur 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 ga s passes through the calcination zone. Compared to long dry and wet kilns, preheater and preheater/precalciner kilns tend to be more effective at capturing fuelgenerated SO 2 . Accordingly, oxidation of sulfur in the feed materials 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 (Andover Technology Partners, 2009a). State-sp ecific emission intensities of SO 2 were determined from emissions reported for the kilns in that state. For any state wh ere the emission intensity was not available for a kilntype, the national average emission intensity was assigned to kilns in that state.
CO 2 emissions from cement kilns result from limestone calcination and fuel combustion. 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; Andover Technology Partners, 2010). Table 3-8 shows approximate CO 2 and H 2 O produced from combustion for the most important fuels for cement 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 ? 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 11.983-12 SO 2 (lb/ton clinker) State Precalciner Preheater Dry Wet NC 1.15 2.32 9.02 7.81 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

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 Logan, WV BIT 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 0/MMBtu 49.71 49.73 40.26 58.21 74.64 49.92 PRB = Powder River Basin coal WV BI T = West Virginia bituminous coal3-13 

### 3.2.2 Control Technologies and Emi ssion Abatement Approaches ISIS-cement contains information on abatement approaches for NO x , SO 2 , PM, HCl, Hg, THC, and CO 2 emissions described above. The three categories of abatement approaches included are: process modifications and upgrades, raw material and/or fu el substitution, and mitigation technologies. For each emission abatement approach, where possible, inform ation on the following parameters was developed (Andover Technology Partners, 2008) and included in the model: capital cost, fixed operating cost, variable operating cost, emission reduction performance fo r all of the pollutants, impacts on fuel and/or raw material use, impact on electricity consumption, byproduct generation and cost, and impact on water use. To estimate capital recovery factors for capital cost s associated with control technologies, economic life values of 15 and an interest rate of 7% are generally used, but different values can be selected by the user. Payback periods and technical life for the energy effi ciency 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 the payback period. Agai n, an interest rate of 7% 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 re flect the impacts of these technologies on pollution reduction, electricity use, and water use. Multimedia impacts of changes to the capacity of cement kilns are listed in Table 3-13. Tables 3-14 through 3-17 s how the electricity consumption and heat input changes accomplished as a result of implementation of energy efficiency measures for Raw Materials Preparation, Clinker Making, Finish Grinding, and Plant-Wide Measures, respectively. 

### 3.2.3 Policy and Economic Parameters
3.2.3.1 Policy Parameters
The ISIS model framework allows the user to select a variety of potential policy options for evaluation. The user can select from cap-and-trade policy (with or without deminimis requirements), emissions charge policy, or rate-based policies. In a cap-and- trade policy scenario, separate caps on pollutants of interest can be specified. The user has the option to run a cap-and-trade policy scenario with or without banking of emissions. Further, a cap-and-trade policy scenario can include deminimis requirements, where the user defines a minimum level of emission reduction required for each emission unit. As mentioned before, it is also possible for the user to input an emission charge for pollutants of interest. Furthermore, traditional policy scenarios (rate-based policies) with unit specific emission reduction requirements specified by the user can be modeled in ISIS. The user can specify the policy horizon (time period) to be used for the model runs. The user can also choose to run ISIS with blocks of years (e.g., 5-year blocks). The simulation horizon and blocks of years can be chosen by the user subject to availability of data.
3.2.3.2 Additional Economic Parameters In the ISIS framework, the following additional econo mic parameters are used: discount rate, escalation rates, and demand elasticity. For the ISIS-cement model, the default discount rate has been chosen as 7%, as recommended by the U.S. Office of Manageme nt and Budget (OMB) for project evaluation (OMB, 1992). Escalation rates used can be found in the “ISI S_Inputs.xls” workbook. Escalation rates for labor are based on historical data from Bureau of Labor an d Statistics (BLS). Raw materials escalation factors are calculated from historical price data of crus hed sandstone and gypsum from USGS. The escalation3-14 factors for variable operating and maintenance costs are based on Chemical Engineering Index. Escalation factors for various fuels and electricity are esti mated 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 KilnsControl TypeImpact on Emissions,±% changeElectricity Consumption,kWh/ton of cementWaterConsumption,gal/ton ofcementNOXSO2PM Hg Other Grinding Kiln OperationLow 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 data8No data80 -1.24,9+1.259Low NOXBurner +Selective CatalyticReduction-90%2Oxidation10No data8Oxidation10No data80 -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 3-91.See EPA (2004), Table 5-1.3-162.Andover Technology Partners 2009b.3.These technologies do not use and do not affect the consumption of water in raw mix preparation or significantly affect electric power consumption incement manufacturing processes.4.Conversion from direct firing systems typical of wet and dry process kilns and older preheater kilns to indirect firing systems, required to implementlow NOXburner (LNB) technology, could result in reductions in primary air fan and kiln-induced draft fan power requirements and concomitant slightincreases in coal conveying power requirements. A reduction in fan/blower power on the order of 100 hp might be anticipated fora moderately sized(300,000 – 500,000 clinker tons per year) kiln converted from direct firing to indirect firing. No power savings on adding an LNB would be anticipatedif 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 content of TDF(typically, 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuels commonly used in cement kilns,such as coal or coke.Therefore it is not practical to relate SO2emissions to the use of these NOXcontrol methods. With respect to use of an SO2wet scrubber using groundlimestone, it is assumed that, for wet process kiln systems, uncontrolled SO2emissions of 8.2 lb per ton of clinker (8.9 lb/ton of cement) (EPA Pub. AP42, Table 11.6-8, 1995) are treated by use of a stoichiometric amount (with respect to uncontrolled SO2) of limestone of 90% purity in a 15% limestoneslurry. Limestone and water consumption are 15.5 lb and 87.7 lb, respectively, per ton of cement produced. For a precalciner kiln, it is assumed thatuncontrolled SO2emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement)(EPA, 1995: Table Table 11.6-8)are treated similarly (stoichiometricamount limestone of 90% purity in a 15% limestone slurry). Limestone and water consumption are 2.1 lb and 11.8 lb per ton of cement produced. For aprecalciner kiln, it is assumed that uncontrolled SO2emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement) (EPA, 1995: Table 11.6-8) are treatedsimilarly (stoichiometric amount limestone of 90% purity in a 15% limestone slurry). Limestone and water consumption are 2.1 lband 11.8 lb per ton ofcement produced.6.Kiln system and raw materials grinding electric power consumption would not be significantly affected by introduction of tiresor tire-derived fuel.7.Combined effect will vary. Low NOx20 to 40%, MKF 20 to 40%.8.While there may be theoretical or limited experimental bases toassume increases or decreases inemissions of various pollutants in connection withNOXemission controls, statistics on such effects are notavailable. See, generally, EPA (2007), Chapter 11.9.Assumes SNCR or SCR both stoichiometric additions of NH3in 20-percent solution. Typical precalciner NOXemissions of 4.2 lb/ton clinker beforetreatment (EPA 1995, Table 11.6-8),and 0.92 tons of clinker areused in each ton of cement. It should also be noted that SNCRis not applicable to wetprocess, long dry process, and many preheater kilns because the kiln gas exit temperatures aretoo low from those units for thenecessary reactions totake place. See EPA (2007a). SNCR is assumed to achieve 63% reduction in NOXemissions from applicable kiln systems. See EPA (2007a), Chapter 8.There may be an attendant small increase in kiln system electric power in connection with injection of water into the kiln system due to an increase ingas volume handled by the kiln’s fan system. The increase is not considered in the calculation.10.Typically, up to about 1% of SO2can be oxidized to SO3, Elemental form of Hg oxidized across SCR provided sufficient concentration of halogens influe gas.11.On a lb/ton of clinker basis.12.Use of theCemStarprocess may have multiple effects on electric power consumption, including reduced raw mix preparation costs in the event thatungroundCemStarmaterial is fed to the rotary kiln, and reduced fan power requirements resulting from reduced kiln gas volumes in connection withboth combustion gases and raw mix calcination. Assuming a 5% replacement of rawmix with carbonate-freeCemStarmaterial, a concomitant 5%reduction in raw mix preparation energy is also assumed. A very approximate resulting 5% reduction in kiln electrical energy consumption is assumedbased on a roughly 5% reduction in kiln exit gases from both calcination and combustion.13.CemStarand fly ash injection are expected to have similar effects onraw mix preparation and kiln process electrical energy requirements.3-1714.See NESCAUM (2000) for effect ofCemStaron NOXreductions from cement kilns. Investigation of the combined effects of multiple technologies onpollutant emissions was not carried out 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 disposal ratesrange from 0.042 to 0.115 tons CKD/ton clinker (0.046 to 0.125 tons CKD/ton cement assuming 0.92 tons 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, disposalcosts are expected to be of comparable cost to transportation costs in connection withoff-site disposal. For example, prices at three northern Californialandfills range from $26/short ton to $69/short ton of CKD plus transportation, whichmay range from approximately $500 to $1,200 per truck load. Pers.Comm., E. Leamer (ARCADIS) Feb. 26, 2009. CKD disposal prices would be expected to be generallyunaffected by the type of cement kiln pollutioncontrols employed.References for Table 3-9(references called out in the above notes can be found at the end of the chapter)Air Pollution Controls and Efficiency Improvement Measures for Cement Kilns, Prepared by ARCADIS Under Contract No. EP-C-04-023, March 31, 2008.Srivastava et al., ES&T, March 2006, pp.1385-1393.Theoretical Approach for Enhanced Mass Transfer Effects in-Duct Flue Gas Desulfurization Processes. Final Report. 1992.Table 3-10. SO2Control Technologies for Cement KilnsControl TypeImpact on Emissions,±% changeElectricity Consumption,kWh/ton of cementWaterConsumption,gal/ton ofcementNOXSO2PM Hg Other Grinding Kiln OperationWet Scrubber-90% to -95%1-80% -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.Andover Technology Partners 2009b.2.Electric power consumption in connection with wet scrubbing is assumed to be primarily a result of: 1) grinding of limestone tobelow 45 to 74micrometers in particle size and pumping limestone slurry; and 2)increased kiln exhaust gas fan power requirements. The latterenergy increase iscaused by increased pressure drop demand on kiln ID fans due to conveying kiln exit gases across the scrubber spray tower plusincreased gas volume3-18demand on kiln ID fans due to the addition of water in the limestone slurry. Slurry preparation and pumping is assumed to require 20 kWh per ton oflimestone. The spray tower pressure drop is assumed to add 500 hp to ID fan requirements on a 300,000 to 500,000 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 content of TDF(typically 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuels commonly used in cement kilns, such as coal or coke.Therefore it is not practical to relate SO2emissions to the use of these NOXcontrol methods. With respect to use of an SO2wet scrubber using groundlimestone, it is assumed that, 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 limestone of 90% purity in a 15% limestone slurry.Limestone and water consumption are 15.5lb and 87.7 lb, respectively, per ton ofcement produced. For aprecalciner kiln, it is assumed thatuncontrolled SO2emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement)(EPA, 1995 Table 11.6-8) are treated similarly (stoichiometric amountlimestone of 90% purity in a 15% limestone slurry). Limestone and water consumption 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. Dry Sorbent Injection report.(ARCADIS, 1992)5.Dry lime injection technology is not well developed for the cement industry and no statistics are available.General Notes: See Table 3-9References for Table 3-10(references called out in the above notes can be found at the end of the chapter)Andover Technology Partners, Cost and Performance of Controls, March 10, 2009.Srivastava et al., ES&T, March 2006, pp.1385-1393.Theoretical Approach for Enhanced Mass Transfer Effects in-Duct Flue Gas Desulfurization Processes. Final Report. ARCADIS, 1992.EPA (1995). Compilation of Air Pollution Emission Factors - Volume 1: Stationary Point and Area Sources. Fifth Edition, Supplements A-F, AP-42. U.S.Environmental Protection Agency, Research Triangle Park, North Carolina.3-19Table 3-11. CO2Control Technologies for Cement KilnsControl TypeImpact on Emissions,+/- % changeElectricityConsumption,MWh/ton ofclinkerProcess WaterConsumption,ton/ton ofclinkerCooling WaterConsumption,ton/ton of clinkerNOXSO2PM Hg CO2OtherSolvent CC1New -99% -99% -85% -0.022 0.429 4.82Solvent CC1Retrofit -99% -99% -85% -0.103 0.160 4.82Oxy-combustion -99% -99% -85% 0.174 14.3Notes to Table 3-111.CC = carbon captureReferences for Table 3-11Andover Technology Partners, Cost and Performance of Controls, March 10, 2009.EPA, Report to Congress on Cement Kiln Dust, Chapter 3: CKD Generation and Characteristics; US Environmental Protection Agency:1993.3-20 Table 3-12. HCl, Hg, and THC Contro l Technologies for Cement Kilns

Control Type 1 Impact on Emissions, ± % change 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 technologies may be utilized as necessary. 2. ACI = activated carbon injection. 3. RTO = regenerative thermal oxidizer

Table 3-13. Multimedia Impacts of Process Capa city Replacement on Cement Kiln Operation 1

Kiln Type Water Consumption, gal/ton of cement 2 Electricity Consumption, kWh/ton of cement Waste

Grinding 3 Kiln Operation 4 Generatio n 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 pres ented on a per-short-ton-of-cement basis and generallymake use of published data reported on a per-short-ton-of-clinker basis. These values are converted to a per-tonof-cement basis by assuming that cement consists of 92-per cent clinker. All weight units in this table are short tons. 2. Water is not normally consumed in dry process cement kiln processes, except on an emergency basis to prevent damage to process equipment by hot gas or solid process streams. Water is used in non-contact cooling processes that are common in both wet process and dry process cement plants, some of which may or may not use closed circuit cooling systems w ith no evaporation losses. Water is consumed in direct contact cooling processes such as cement grinding in both wet process and dry process plants. However, the nature and amount of such water consumption is not intrinsically different between wet process and dry process plants. 3. Power data reported in Worrell and Galitsky (2004) are used for raw mix preparation and kiln process electrical energy consumption for wet and all dry processes.3-21 4. Air pollution control device contributions to electric power consumption data on cement kiln process systems are assumed to include only existing particulate matter control devices and not scrubbers for 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. Clin ker is assumed to require 1.42 tons of raw kiln feed (dry) per ton of clinker. Kiln feed loss on ignition is assu med to be 35 percent. Kiln feed slurry is assumed to contain 36-percent water. 7. Industry-wide statistics on electrical energy use do not distinguish among the various cement processing stages between the various dry processes – all dry process plants are averaged together. Published data that distinguish between various process types combine all process phases without indicating energy use for individual process phases. Therefore, electrical energy use is reported here to be the same for all dry process plants. See Worrell and Galitsky (2004). 8. As stated in note 7, industry data on electric power use in raw materials preparation and kiln processing are not available for every dry process. Data are for all dry pro cesses are combined. 9. Data reported in EPA (1993) on CKG generation by cement plants doe not distinguish between preheater and precalciner kilns with respect to net CKD generation rates. 10. CKD disposal prices vary widely by location. Transportation of CKD is a significant component of the cost of disposal if disposal is off-site. For example, an estimated off-site disposal cost ranging from $26 to $69 per ton of CKD for disposal, plus transportation costs ranging from $500 - $1,500 per truck load for one northern California location, depending on the landfill chosen. (ARCADIS, 2009) On-site disposal costs would include costs of transportation, dust control, and landfill operation. These costs have not been determined and would most certainly vary widely by location based on terrain, site geology, landfill operating requirements, and permit and future closure costs. 11. Modern preheater cement kilns and pr ecalciner cement kilns generate simila r volumes of CKD. Older preheater cement kilns are similar in CKD generation to long dry process cement kilns.
Table 3-14. Energy Efficiency Measures for Raw Materials Preparation

Energy Efficiency Improvement Method Electricity Consumption Change, kWh/ton clinker 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 Ho mogenization) -0.35 WMCCC (Wash Mills with Closed Circuit Classifier) -12.00 RMTHEC (High-Efficiency Classifiers) -5.05 -5.05 -5.05 -5.053-22 Table 3-15. Energy Efficiency Clinker Making Measures
Energy Efficiency

Improvement Method Electricity Consumption Change, kWh/ton clinker Heat Input Change, MMBtu/ton of clinker 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 Grinding

Energy Efficiency Improvement Method Electricity Consumption Change, kWh/ton clinker Dry Wet Preheater Precalciner EMPC (Energy Management and Process Control) -1.60 -1.60 -1.60 -1.60 IGMBM (Improved Grinding Media [Ball Mills]) -1.80 -1.80 -1.80 -1.80 HPRP (High-Pressure Roller Press) - 16.00 -16.00 -16.00 -16.00 HEC (High-Efficiency Classifiers) -3.85 -3.55 -3.85 -3.85

Table 3-17. Energy Efficiency Plant-wide Measures

Energy Efficiency Improvement Method Electricity Consumption Change, kWh/ton clinker Dry Wet Preheater Precalciner PM *(Preventive Maintenance) -2.50 -2.50 -2.50 -2.50 HEM (High Efficiency Motors) -2.50 -2.50 -2.50 -2.50 ASD (Adjustable Speed Drives) -6.25 -6.00 -6.25 -6.25 OCAS (Optimization of Compressed Air Systems) -1.00 -2.50 -1.00 -1.00 *This is the only occurrence wherein “PM” does not stand for “particulate matter”3-24 

### References for Chapter 3 American University (2008). TED Case Studies: Cemex Case . http://www.american.edu/TED/cemex.htm , accessed October 21, 2008. Andover Technology Partners (2008). Cost and Perfo rmance of Controls, memorandum to U.S. EPA, September 25, 2008. Andover Technologies Partne rs (2009a). Memorandum: NO X , SO 2 and CO 2 Emissions from Cement Kilns (Emissions Memo) , from Jim Staudt to Ravi Srivastava, Samudr a 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 Torre s. Dated March 10, 2009 (see Appendix A of this document). Andover Technologies Partners (2010). 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). ARCADIS (1992). Theoretical Approach for Enhan ced Mass Transfer Effects in-Duct Flue Gas Desulfurization Processes. Final Report. ARCADIS, 1992. ARCADIS (2009). Personal Comm. with E. Leamer of ARCADIS, 26 February, 2009. Burtraw, D. (2010). Supply Elasticity Estimation, me morandum to Ravi Srivastava, U.S. EPA, March 10, 2010. Depro, B.M. (2007). RTI International. “ Documentation for Portland Cement Kiln Cost Functions (2005 )”, Memorandum to Keith Barnett, US EPA, Augus t 31, 2007. (See Appendix A of this document). Depro, Brooks. (2010). RTI International. “ISIS Cement Production Costs”, Memorandum to Elineth Torres, US EPA, March 31, 2010. (See Appendix A of this document). Depro, Brooks and Lentz, Anthony. (2010). RTI International. “ISIS Cement Production Costs”, Excel Spreadsheet Attachment to Memo of Same Name from Brooks Depro (2010), February 16, 2010. BLS (2008). Bureau of Labor Statistics, Databases, Tables & Calculators by Subject, Major Sector Productivity and Costs Index. Series ID : PRS30006112, Sector: Manufacturing. http://data.bls.gov/PDQ/servlet/SurveyOutputServle t?data_tool=latest_numbe rs&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. EPA (1993). Report to Congress on Cement Kiln Dust , Chapter 3: CKD Generation and Characteristics; US Environmental Protection Agency: 1993. EPA (1995). Compilation of Air Pollution Emission Factors - Volume 1: Stationary Point and Area Sources. Fifth Edition, Supplements A-F, AP-42. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.3-25 EPA (1998). June 1998. Regulatory Impact Analysis of Cement Kiln Dust Rulemaking . Washington, DC: U.S. Environmental Protection Agency. http://www.epa.gov/osw/nonhaz/industria l/special/ckd/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 Version 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 Protection Agency. EPA (2010). August 6, 2010. "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS" Memorandum prepared for Keith Barnett by RTI. US Environmental Protection Agency. FLSmidth & Co. A/S (2007). Q2 Report 2007. August 2007 http://hugin.info/2106/ R/1148414/219358.pdf , accessed October 21, 2008. NESCAUM (2000). Status Report on NO X Controls for Gas Turbines, Cement Kilns, Industrial Boilers, Internal Combustion Engines: Technologies & Cost E ffectiveness. Northeast States for Coordinated Air Use Management, Decem ber 2000. Accessed at http://www.nescaum.org/documents/nox-2000.pdf/ OMB (1992). Guidelines and Discount Rates for Benefit-Cost Analysis of Federal Programs. OMB Circular No.A-94 (Revised). U.S. Office of Management and Budget, October 29, 1992. http://www.whitehouse.gov/omb/circulars/a094/a094.html#8 , accessed October 21, 2008. PCA (2004). Innovations in Portland Cement Manufacturi ng. Portland Cement Association. Edited by J. I. Bhatty, F. M. Miller, and S. H. Kosmatka. 2004. PCA (2004a). U.S. and Canadian Portland Cement I ndustry: Plant Information Summary of December 31, 2004. PCA (2006). U.S. and Canadian Portland Cement I ndustry: Plant Information Summary. Portland Cement Association, Skokie, IL, 2006. PCA (2008). Forecast Report: Long-Term Cement Cons umption Outlook. By Ed Sullivan January 31, 2008 . http://www.cement.org/econ/pdf/L ong-TermFlashwinter2007nonmember.pdf , accessed October 21, 2008. PCA (2009a). PCA Capacity Report. Flash Report. Portland Cement Association’s Economic Research Department. Updated October 19, 2009. PCA (2009b). Forecast Report: Long-Term Cement Cons umption Outlook. By Ed Sullivan. October 28, 2009. Portland Cement Association.3-26 Ryan, S. (2006). The Costs of Environmental Regula tion 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, Excel Spreadsheet. TRAGIS (2003). Transportation Routing Analysis Ge ographic Information System (TRAGIS) User’s Manual, Revision 0, Oak Ridge Na tional Laboratory, June 2003. https://tragis.ornl.gov/TRAGISmanual.pdf

. USGS (2005). Background Facts and Issues Concer ning Cement and Cement Data. U.S. Geological Survey, Open-File Report 2005-1152 . http://pubs.usgs.gov/of/2005/1152/2005-1152.pdf , accessed October 21, 2008. USGS (2007a). 2005 Minerals Yearbook: Cement. U.S. Geological Survey, p. 16.2, February 2007, http://minerals.usgs.gov/minerals/ pubs/commodity/cement/cemenmyb05.pdf , accessed October 21, 2008 USGS (2007b). Mineral Commodity Summaries: Cement, U.S. Geological Survey, pp. 40-41, January 2007. http://minerals.er.usgs.gov/minerals /pubs/commodity/cement/cemenmcs07.pdf , accessed October 21, 2008. USGS (2009). 2005 Minerals Yearbook: Cement. U.S. Geological Survey (Table 17), pp. 16.21. http://minerals.usgs.gov/minerals/pubs /commodity/cement/myb1-2007-cemen.pdf

. Worrell, E.; Galitsky, C. (2004). Energy Efficiency Improvement Opportunities for Cement Making; Lawrence Berkeley National Laboratory, LBNL-54036, Jan 2004. An ENERGY STAR ® Guide for Energy and Plant Managers, http://www.osti.gov/e nergycitations/servlets/ purl/821915-Re2kcK/native/. Yates, J.R. et al. (2003). CemStar Process and Technology for Lowering Greenhouse Gases and Other Emissions while Increasing Cement Production. http://www.hatch.ca/Environment_Community/Susta inable_Development/Projects/Copy%20of%20Cem Star-Process-final4-30-03.pdf accessed February 27, 2009.4-1 

## Chapter 4 Model Calibration
Large techno-economic models of ISIS framework size require model calibration as they utilize extensive amount of data which comes from different sources. This chapter outlines calibration methodology that was used, discusses data used for calibration, presents calibration results, and gives further recommendations. 

### 4.1 Calibration Methodology Calibration methodology utilizes the concept of calibra tion constant. The calibration constant has been developed to account for possible errors in costs. The value of calibration constant, calconst(i) , is set by trial and error during calibration. The objective of the trial and error approach is to minimize the absolute difference in the reported and model-pr edicted prices (which are marginal values of the supply equation) for each USGS district. In the first step of calibration, the model is set to run for 2005-2007 by making appropriate changes in the input “Policy” worksheet, and GAMS input files. The import quantities are then adjusted equal to reported import quantity for each of the import dist rict, except for those of Mexico and Canada. In the next step, impact of changing the calibration c onstant is monitored. This impact of the calibration constant is assessed on estimated production quantities to keep the difference between reported and model predicted production values within reasonable limits 6 . The calibration constant modifies each kiln’s variable cost of production. “Calibration” worksheet within the Inputs workbook has values of calibration constant assigned for each USGS district. Finally, in the input GAMS file, the values are assigned for a given USGS district to each of the kilns located in that USGS district. The model is first calibrated for year 2005, to obtain values of the calibration parameter calconst(i) for the year. Next, calconst(i) values for 2005 are used as starting point and obtain values for the same parameter for 2006. Similarly, the process is repeated to obtain the values for 2007. Then, an average of the calconst(i) values over the three years is taken and used fo r the future model runs. Current values of the parameter Calconst(i) being used in the model runs can be found in the worksheet “ Calibration ” of the “ISIS_Inputs.xls” workbook. Calibration is a dynamic process, and it is recommend ed that model calibration be performed periodically. In this fashion, any available new production, imports, or price data could be utilized in t he model.. 

### 4.2 Data for Calibration Production quantities in various USGS production distri cts, import levels in the import districts and reported cement prices for USGS districts are th e key quantities used for calibration of the model. Reported data for years 2005, 2006 and 2007 were us ed to calibrate the model and obtain values of appropriate calibration parameters.

6 There is no standard method to guide user in determining acceptable level of “error” in the reported and predicted values for the purpose of calibration. In this works, we have set acceptable level for the absolu te gap between the individual reported and predicted values to ± 15%, although effort ha s been made to keep this level below 10% for most of the quantities. However, due to discontinuities in the transportation matrix, error in the re ported data, or other unknowns, th e gap in the estimated and re ported values may be higher in certain markets.4-2 

### 4.2.1 Cement Prices in USGS Districts Reported cement prices in various USGS district s are shown in Table 4-1 for years 2005 through 2007. For a given year, the reported cement price shows si gnificant variation across USGS districts. For most districts the cement price has increased over the thr ee 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 distri cts is shown in Table 4-2 for years 2005 through 2007. For a given year, the reported cement production shows significant variation across USGS districts. Generally production dropped fro m 2005 to 2007, due to a decrease in demand resulting from the economic downturn. 

### 4.2.3 Cement Imports by Import Districts Reported cement import quantity in various import districts is shown in Table 4-3 for years 2005 through 2007. For a given year, the reported cement imports show a significant variation across import districts. Generally imports dropped from 2005 to 2007, due to a decrease in demand resulting from the economic downturn. New Orleans (LA), San Francisco (CA) an d 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, respectivel y. As can be seen from these tables, in most of the districts reported and calculated cement prices are with in 10% range, whereas in a few they are higher. It should be noted that in general the price differentials are smaller in the year 2005 and have increased in 2007. In some USGS districts the reported and calcula ted prices are different than the model predicted prices due to several factors, including error in reported prices, discontinuities in the transportation matrix, and/or unique factors for some specific markets. Specifically, the Arizona and New Mexico USGS di strict has a price differential due to capacity constraints in New Mexico. New Mexico has only two sm all, very old kilns with a capacity half than the demand in New Mexico. The balance demand is met by kilns in Texas, and imports, which incur a significant transportation cost, resulting in a high pric e for cement in this market. Similarly, Oregon and Washington USGS District is dependent on supplies from other states. While demand in Washington is met by domestic production, and imports from Canada , Oregon, relies on imports from Canada, and other nearby states, resulting in higher cost. Generally, individual market prices are within the cr iteria specified in the QA document, the aberrations 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.

Further while calibrating for price, aggregate production leve l is also tracked to make sure that it is within reasonable limits. The reported and predicted aggreg ate production levels for 2005-2007 are shown in Table 4-7.4-3 The current set of calibration constant values are aver aged over the years of calibration, and are available in the “Calibration” worksheet in the “ISIS_Inputs.xls” workbook.4-4 Table 4-1. Cement Prices ($/short ton) for USGS Districts USGS District 2005 2006 2007 Alabama 74.61 81.74 87.09

Illinois 80.29 89.81 91.17 Indiana 72.03 80.04 79.99 Kansas 76.58 86.50 92.87 Maryland 74.76 81.19 80.18 Missouri 78.92 86.61 89.36

Ohio 82.08 90.24 91.47 South Carolina 68.57 80.46 87.54 Maine and New York 80.74 92.53 96.62 Pennsylvania 79.73 89.99 90.85 Michigan and Wisconsin 79.83 84.82 90.12 Iowa, Nebraska and South Dakota 78.72 89.83 93.08 Florida 77.56 90.44 95.55 Georgia, Virginia and West Virginia 84.49 95.93 93.60 Kentucky, Mississippi and Tennessee 84.37 89.81 88.90 Arkansas and Oklahoma 89.00 96.99 97.74 Texas 74.72 85.10 88.33 Arizona and New Mexico 75.76 84.16 86.64 Colorado and Wyoming 82.24 92.90 92.73 Idaho, Montana, Nevada and Utah 83.61 90.26 95.25 California 88.20 99.09 100.24 Oregon and Washington 79.38 90.72 90.724-5 Table 4-2. Cement Production (short tons) for Various USGS Districts USGS District 2005 2006 2007 Alabama 5,647,141 5,733,121 5,578,650

Illinois 3,568,182 3,425,984 3,434,254 Indiana 3,370,868 3,334,492 3,285,999 Kansas 3,182,373 3,310,241 3,039,164 Maryland 2,813,098 2,922,227 3,305,160 Missouri 5,877,524 5,776,111 5,763,479

Ohio 1,086,879 1,064,833 1,010,077 South Carolina 3,601,251 3,654,162 4,057,414 Maine and New York 3,572,591 3,699,357 3,471,388 Pennsylvania 6,931,334 6,631,505 6,239,551 Michigan and Wisconsin 6,171,841 5,993,267 6,047,588 Iowa, Nebraska and South Dakota 4,962,606 5,024,335 4,889,607 Florida 6,311,835 6,477,181 6,076,137 Georgia, Virginia and West Vi rginia 2,612,478 2,696,253 2,528,648 Kentucky, Mississippi and Tennessee 3,649,753 3,849,271 3,770,250 Arkansas and Oklahoma 3,097,495 2,979,547 2,879,982 Texas 12,737,207 12,510,131 12,038,919 Arizona and New Mexico 3,073,244 2,809,792 2,902,131 Colorado and Wyoming 2,918,920 2,842,861 2,797,240 Idaho, Montana, Nevada and Utah 3,400,630 3,354,333 3,308,645 California 12,747,128 12,069,207 11,941,198 Oregon and Washington 2,175,963 2,101,005 2,103,4814-6 Table 4-3. Cement Imports (except from Canada and Mexico) for Import Districts (short tons) Import District 2005 2006 2007 Baltimore, MD 146,300 203,500 18,700

Boston, MA 145,200 50,600 3,300 Buffalo, NY 6,600 4,400 0 Charleston, SC 1,212,200 1,100,000 363,000

Chicago, IL 1,100 1,100 0 Cleveland, OH 0 1,100 0 Columbia-Snake, ID-OR-WA 831,600 1,115,400 1,180,300 Detroit, MI 59,400 0 0 Duluth, MN 0 0 0 El Paso, TX 0 0 0 Great Falls, MT 0 0 0 Houston-Galveston, TX 2,880,900 3,705,900 3,641,000 Laredo, TX 0 0 0 Los Angeles, CA 3,357,200 3,759,800 2,029,500 Miami, FL 2,395,800 2,311,100 980,100 Minneapolis, MN 0 0 0 Mobile, AL 565,400 573,100 0 New Orleans, LA 4,503,400 5,090,800 1,171,500

New York, NY 1,390,400 1,327,700 810,700

Nogales, AZ 0 0 0 Norfolk, VA 767,800 793,100 447,700 Ogdensburg, NY 0 0 0

Pembina, ND 0 0 0 Philadelphia, PA 544,500 665,500 342,100

Portland, ME 0 0 0 Providence, RI 814,000 680,900 509,300 San Diego, CA 620,400 708,400 427,900 San Francisco, CA 2,599,300 3,081,100 1,524,600

Savannah, GA 88,000 204,600 376,200

Seattle, WA 368,500 733,700 644,600 St. Albans, VT 0 0 0 St. Louis, MO 0 0 3,300

Tampa, FL 3,825,800 3,791,700 1,523,500 Wilmington, NC 427,900 416,900 284,9004-7 Table 4-4. Reported and Calculated Ce ment Prices in USGS Districts (2005) USGS District Reported Calculated % Δ 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 Ce ment Prices in USGS Districts (2006) USGS District Reported Calculated % Δ 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 Ce ment Prices in USGS Districts (2007) USGS District Reported Calculated % Δ Alabama 87.09 73 -17

Illinois 91.17 72 -21 Indiana 79.99 75 -7 Kansas 92.87 71 -24 Maryland 80.18 74 -8 Missouri 89.36 71 -21

Ohio 91.47 76 -17 South Carolina 87.54 69 -21 Maine and New York 96.62 74 -24 Pennsylvania 90.85 75 -17 Michigan and Wisconsin 90.12 76 -15 Iowa, Nebraska and South Dakota 93.08 71 -23 Florida 95.55 66 -31 Georgia, Virginia and West Virginia 93.60 74 -20 Kentucky, Mississippi and Tennessee 88.90 71 -20 Arkansas and Oklahoma 97.74 88 -10 Texas 88.33 80 -10 Arizona and New Mexico 86.64 101 17 Colorado and Wyoming 92.73 89 -4 Idaho, Montana, Nevada and Utah 95.25 83 -13 California 100.24 81 -20 Oregon and Washington 90.72 75 -18

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

Year Reported Production (million short tons) Calculated Production (million short tons) % Δ

2005 111.84 112.54 0.63 2006 110.30 111.23 0.84 2007 108.17 101.95 -5.754-10 

### 4.4 Conclusions If any of the key input parameters, specifically those relating to production quantities and costs, are refined or otherwise modified or additional observed data becomes available, the calibration of the model should be repeated. Transportation matrix, modes, and cost of transportation also have significant impact on the behavior of production distribution across the districts and production prices. Therefore, if any further modifications or refinements are made to th e transportation matrix, the model needs to be recalibrated.
At the time of calibration of the current version of th e model, production, import and price values for only 2005 to 2007 were available. As discussed above, when the new valu es become available, the model needs to be calibrated again. Calibration of the mode l should be repeated as soon as new information or new observed data become available. Due to practical limitations it is recommended that the calibration of the model be repeated every two years. Further, since the calibration data is available only for three years, equal weight was given to the parameters obtained fo r each year. Once larger data-set is available, a modified weighing system can be adopted to give highest weight to the most-recent year data.4-11 

### References for Chapter 4 United States Geological Service. Mineral Yearbook 2008. Table 5. http://minerals.usgs.gov/mine rals/pubs/commodity/cement/ United States Geological Service. Mineral Yearbook 2008. Table 3. http://minerals.usgs.gov/mine rals/pubs/commodity/cement/ United States Geological Service. Mineral Yearbook 2008. Table 11. http://minerals.usgs.gov/mine rals/pubs/commodity/cement/5-1 

## Chapter 5 Analysis and Results of the Po rtland Cement NESHAP and NSPS
5.1 Affected Sources EPA is finalizing amendments to the NESHAP for the Portland cement manufacturing industry and the NSPS for Portland cement plants. The final amendmen ts to the NESHAP add or revise, as applicable, emission limits for mercury (Hg), total hydrocarbo ns (THC), and particulate matter (PM) from nonhazardous waste (nhw) kilns located at major or area sources, and hydrochloric acid (HCl) from nhw kilns located at major sources. The amendments to the N SPS add or revise, as applicable, emission limits for PM, NOx and SO2 for facilities that commence construc tion, modification, or reconstruction after June 16, 2008. ISIS-cement was used to conduct an integrated engi neering and economic analysis for the final Portland Cement NESHAP and final NSPS. Figure 5-1 shows the cement facilities and the kilns modeled in ISIS for the final NESHAP and final NSPS analysis. Ev en though the Portland cement NESHAP affects only nhw kilns, in ISIS-cement all cement kilns projected to be in the U.S. in 2013 are modeled to meet the cement demand.

Figure 5-1 Portland Cement Faciliti es and Kilns Modeled in ISIS-Cement ISIS Inputs: 2013
Total Cement Facilities = 108

Total Cement Kilns = 177 Total Cement Capacity = 127.6 Million metric tons

Non-hazardous Waste Facilities = 97
Total Non-hazardous Waste Kilns = 157 Total Cement Capacity = 115.2 Million metric tons Hazardous Waste Facilities = 11
Total Kilns = 20 Total Cement Capacity = 12.4 Million metric tons

Existing Facilities in 2009 = 93 Existing Kilns = 148 Total Cement Capacity = 103.8 Million metric tons Projected New Facilities in 2010-2012 = 4 Projected New Kilns = 9 Total Cement Capacity = 11.4 Million metric tons5-2 From the 108 Portland cement facilities modeled in IS IS-cement for the final NESHAP and final NSPS analysis, 97 facilities or 157 nhw kilns are projected to be subject to the final NESHAP. From these 157 nhw kilns projected to be subject to the NESHAP, 16 k ilns are projected to be subject to the limits for new sources while 137 kilns are projected to be subj ect to the limits for existing sources. Seven facilities or 7 kilns are projected to be subject to the final NSPS. 7 

### 5.2 Emission Limits
Table 5-1 shows the emission limits for the final NES HAP analyzed in ISIS, while Table 5-2 shows the emission limits for the final NSPS. Emission limits in tables 5-1 and 5-2 are average annual emissions. EPA’s analysis using ISIS-cement focused on the indivi dual rules and also on the combination of the final NESHAP and the final NSPS. The limits used in th e analysis were input as lbs/ton cement produced annually.
Table 5-1. Final NESHAP Emission Limits NESHAP Limits for ISIS' Runs Pollutant Existing New Units Average Average Hg 31.7 14 lb/MM ton clinker 0.0000317 0.000014 lb/ton clinker 0.000029164 0.00001288 lb/ton cement
THC 5 5 ppmv 0.050856164 0.050856164 lb THC/ton clinker 0.046787671 0.046787671 lb/ton cement
HCl 0.4 0.4 ppmv 0.003378699 0.003378699 lb HCl/ton clinker 0.003108403 0.003108403 lb/ton cement PM 0.012 0.005 lb/ton clinker 0.01104 0.0046 lb/ton cement

Table 5-2. Final NSPS Emission Limits NSPS Limits for ISIS' Runs
Pollutant New Units Limit Average
NOx 1.5 1.5 lb/ton clinker 1.38 1.38 lb/ton cement
SO2 0.4 0.4 lb/ton clinker 0.368 0.368 lb/ton cement

7 Under the NESHAP any source that commenced constr uction after May 6, 2009 is a new source. Under the NSPS, any source constructed, reconstructed or modified after June 16, 2008 is a new source.5-3 

### 5.3 Control Technologies Kilns subject to the final NESHAP and/or final NSPS ar e expected to add one or more control devices to comply with the final emission limits. Table 5-3 shows control technologies and control efficiencies used in the final NESHAP and final NSPS analysis. Th e cost of these control technologies can be found in the "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS". August 6, 2010. The final NESHAP and final NSPS ru les also include additional testing and monitoring requirements for affected sources. In the final NESH AP, in addition to meeting the emission limits, each affected kiln will be required to install Continuous Emission Monitoring Systems (CEMS) to monitor Hg, THC and HCl, while bag leak detector (BLD) will be required to monitor pe rformance of all baghouses (fabric filters). In the final NSPS, in addition to meeting the emission limits, each kiln affected will be required to install CEMS for NOx and SO2. The cost of these devices can also be found in "Summary of Environmental and Cost Impacts for Final Por tland Cement NESHAP and NSPS", August 6, 2010
. Table 5-3. Control Technology and Co ntrol Efficiency Matrix by Pollutant Control Technologies Pollutants Hg THC HCl PM SO2 NOx Limestone Wet Scrubbers (LWS) 80% 99% 90% Activated Carbon Injection (ACI) with polishing baghouse 90% 80% 99.9% Regenerative Thermal Oxidizer (RTO) 98% Dry Lime Injection (DLI) 75% 70% Low NOx Burner (LNB) + Selective Non-Catalytic Reduction (SNCR) 65% Baghouse with Membrane Bags (BHMB) 99.9% Membrane Bags Retrofit (MBR) 99.9% ACI + LWS 90% 80% 99% 99.9% 90% LWS + RTO 80% 98% 99% 90%



### 5.4 ISIS Runs and Results EPA’s analysis focuses on the results of the final NESHAP and final NSPS. We also present additional information on different combinations of the regulat ory programs to help stak eholders better understand the size and scope of each. The three policies analyzed are:

o Final NSPS only,

o Final NESHAP only, and

o Final NSPS and NESHAP. Because all existing sources under the NESHAP will be required to come into compliance in 2013, the analysis presented here reflects the impact of the final NESHAP and final NSPS in 2013.5-4 As explained in Chapter 3, one of the key data i nputs for the ISIS-cement model is the demand forecast for each demand center. For cement, the demand is a function of gross domestic product (GDP) growth, interest rates, special construction projects (e.g., hi ghways), and public sector construction spending. In 2005, Portland cement demand reached a record high of 128 million metric tons. The economic distress in the US during 2008 and 2009 resulted in a decline in cement demand. The demand for cement compared with 2005 levels declined from nearly 89% in 2007 to less than 60% in 2009 (PCA, 2009). Per PCA, cement demand is not expected to reach past cycli cal peak levels (2005) until 2015 (PCA, 2010). Since multiple years were not modeled, the average de mand for the years 2013 th rough 2018 of PCA’s 2009 Long-Term Cement Consumption Outlook (PCA, 2009 ) was used as the exogenous demand in 2013. This average demand is expected to be more repres entative of what will be typical after implementation of the regulation. On the domestic supply side, ISIS uses the industry data for all the kilns operating in 2009 (see Figure 5- 1) and optimizes the industry in 2013 taking into account retirements, replacements, and new kilns projected by PCA from 2010 to 2013. For the policy scenarios analyzed here, ISIS simultaneously estimated 1) optimal industry operation to meet dema nd and emission reduction requirements; 2) the suite of control technologies needed to meet the emission lim its; 3) the engineering cost of controls; and 4) the economic impacts of the policies. In the analysis shown below, EPA assumed that all the projected new kilns shown in Figure 5-1 will be active in 2013. To accommodate for this assumption, in the NSPS policy run, ISIS required the 7 new kilns projected to be subject to the final NSPS to r un in both the reference and policy case. In the NSPS policy case these 7 kilns were required to meet the NSPS limits by installing controls. In the NESHAP and the combination of NSPS and NESHAP, ISIS required the 16 new kilns 8 projected to be subject to the NESHAP limits for new sources to run in both the re ference and policy case. In the NESHAP policy case these 16 kilns were required to meet the NESHAP lim its for new sources by installing controls. On the other hand, in the NSPS and NESHAP combination the 16 kilns projected to be subject to the NESHAP limits for new sources as well as the 7 kilns projected to be subject to the NSPS were required to meet both the NSPS and NESHAP limits for new sources by installing controls. 

### 5.4.1 ISIS Results
5.4.1.1 Cement Demand As mentioned in Chapter 3, in ISIS-cement the U.S. cement industry is organized in state-specific demand centers. The model simulates each cement kiln’s ab ility to compete in each of the demand centers as a function of the kiln’s production cost and transporta tion cost associated with supplying to each demand center. At the same time the cement demand at each demand center can be met by local production, foreign imports, or by inter-market tradi ng (i.e., shipping from other demand center). The exogenous cement demand used in ISIS for th is analysis was the average demand for 2013 through 2018 or 123.6 million metric tons of cement. Table 5-4 shows the impact of the two policies analyzed on the cement demand in 2013. As shown in Table 5-4, in 2013 the US cement demand could drop 0.65% under the NSPS, 5.70% under the NESHAP, and 6.27 un der the combination of the NSPS and NESHAP.

8 Seven of the 16 new kilns projected to be subject to the NES HAP limits for new sources are also kilns projected to be subject to the NSPS.5-5 Table 5-4 Policy Impact on U.S. Cement Demand in 2013 Cement Demand (metric tons of cement) Policy Run Reference Case Policy Ca se Change in Demand % Change NSPS 123,653,270 122,844,362 -808,907 -0.65 NESHAP 123,653,270 116,608,341 -7,044,929 -5.70 NSPS and NESHAP 123,653,270 115,894,658 7,758,612 -6.27

5.4.1.2 Cement Production Table 5-5 shows the impact of the policies analyzed on the US cement production in 2013. As shown in Table 5-5, U.S. cement production could drop 1.4% under the NSPS, 9.60% under the NESHAP, and 10.62% under the combination of NSPS and NESHAP. Table 5-5 Policy Impact on U.S. Cement Production Cement Production (metric tons of cement) Policy Run Reference Case Policy Ca se Change in Production % Change NSPS 94,036,939 92,680,715 -1,356,224 -1.4 NESHAP 94,141,619 85,101,425 -9,044,929 -9.60 NSPS and NESHAP 94,141,619 84,145,488 -9,996,131 -10.62

5.4.1.3 Cement Imports Table 5-6 shows the impact of the policies analyzed on US cement imports in 2013. As shown in Table 5-6, U.S. cement imports could rise 1.84% under the NSPS, 6.76% under the NESHAP, and 7.58 under the combination of NSPS and NESHAP. Table 5-6 Policy Impact on U.S. Cement Imports Cement Imports (metric tons of cement) Policy Run Reference Case Policy Ca se Change in Imports % Change NSPS 29,616,331 30,163,647 547,316 1.84 NESHAP 29,511,650 31,506,916 1,995,266 6.76 NSPS and NESHAP 29,511,650 31,749,170 2,237,520 7.58

5.4.1.4 Cement Prices Table 5-7 shows the estimated average national pri ce in 2013 for Portland cement under the policies analyzed. As shown in Table 5-7, cement prices may increase 0.74% under the NSPS, 6.83% under the NESHAP, and 7.58% under the combination of NSPS and NESHAP. Table 5-7 Policy Impact on U.S. Cement Prices Cement Price ($/metric ton of cement) Policy Run Reference Case Policy Ca se Change in Price % Change NSPS 87.77 88.42 0.65 0.745-6 NESHAP 84.81 90.60 5.79 6.83 NSPS and NESHAP 84.81 91.24 6.42 7.58

5.4.1.5 Control Technology Installation Table 5-8 shows the projected control technology installation in 2013 for the policies analyzed. Table 5-8 Control Technology Installation Controls Policy Run LNB + SNCR LWS ACI LWS + ACI MBR RTO LWS + RTO DLI BHMB NSPS 7 7 - - - - - - - NESHAP 0 30 42 29 6 15 6 1 2 NSPS and NESHAP 7 27 41 22 8 11 12 1 1

Table 5-9 shows control cost associated with the cont rol installation shown in Table 5-8. Table 5-9 also shows the monitoring cost associated wi th the three policy scenarios analyzed.
Table 5-9 Control Cost Control Cost ($) Policy Run Control Cost Monitoring Total Cost NSPS 32,135,140 1,386,821 33,521,691 NESHAP 333,536,975 17,236,198 350,773,173 NSPS and NESHAP 325,654,798 16,839,964 342,494,762

5.4.1.6 Cement Capacity One important feature of the ISIS-cement model is that the cement capacity is dynamically optimized by endogenously installing new capacity, replacing exis ting capacity and retiring non-competitive existing capacity. Table 5-10 shows the cement capac ity in 2013 for the policies analyzed. Table 5-10 Cement Capacity Capacity (metric tons cement) Policy Run Reference Case Policy Ca se Capacity Change % Change NSPS 94,036,939 92,680,715 -1,356,224 -1.4 NESHAP 94,141,619 85,101,425 -9,044,929 -9.60 NSPS and NESHAP 94,141,619 84,145,488 -9,996,131 -10.62

With respect to the reference case in 2013 ISIS identif ied that 1.3 million metric tons of existing capacity may idle under the NSPS, 9.0 million metric tons of existing capacity may idle under NESHAP, while 9.9 million metric tons of existing capacity may idle under the combination of NSPS and NESHAP.5-7 Table 5-11 shows the number of kilns active in each policy analyzed in ISIS. A kiln that is active in ISIScement will run at 100% of its capacity after ta king into account normal downtime days. A kiln is considered to be idling if it was producing cement in the reference case but is not in the policy case. As shown in Table 5-11, 1 kiln may idle under the N SPS, 12 kilns may idle under the NESHAP, and 14 kilns may idle under the combination of NSPS and NESHAP. If kilns owners decide to run their kilns at a lower utilization rate a lower number of kilns will be expected to idle under each policy scenario. Table 5-11 Kiln Population Kilns Policy Run Reference Case Polic y Case Kilns Idling % Change NSPS 127 126 1 -0.78 NESHAP 113 101 12 -11.88 NSPS and NESHAP 113 99 14 -12.38
Table 5-12 shows the number of cement facilities that may idle as result of the policies analyzed in ISIS. The changes in cement capacity and kiln population may cause 4 facilities to idle under the NESHAP, and may cause 8 facilities to idle under the combination of NSPS and NESHAP. A facility is considered idling if one or more kilns at the facility were pr oducing cement in the reference case but they are not producing in the policy case.
Table 5-12 Number of Cement Facilities Facilities Policy Run Reference Case Policy Case Facilities Idling % Change NSPS 86 86 0 0.00 NESHAP 81 77 4 -4.93 NSPS and NESHAP 81 73 8 -9.87

5.4.1.7 Employment Impacts Table 5-13 shows the total number of employee hours for each policy analyzed. The number of employee hours for each policy is a function of cement producti on. In each policy analyzed, the number of employee hours is proportional to the cement production. The difference between the number of hours in the reference and policy case in each scenario gives an estimate of the change in the number of employee hours due to the policy analyzed. Using PCA’s labor us e data, we can calculate the number of jobs that could potentially be affected by each policy under an alysis. Table 5-14 shows PCA’s labor use data for 2005 and 2008 (PCA 2005, 2008). Table 5-15 shows the estimated impact on employment for each policy under analysis. Table 5-13 Change in Employee Hours Employee Hours Policy Run Reference Case Polic y Case Difference % Change NSPS 24,586,999 24,385,731 -201,269 -0.82 NESHAP 24,086,384 21,735,357 -2,351,111 -9.76 NSPS and NESHAP 24,086,384 21,484,727 -2,601,741 -10.805-8 Table 5-14 PCA’s Labor Data: 2005 and 2008

Year Process Labor hours: Direct Number of Employees Number of Direct Hours per Employee

2005 Wet Process 3,970,188 1,962 2,024 Dry Process 35,448,534 16,562 2,140 Total 39,418,722 18,524 2,128 Average 19,709,361 9,262 2,082

2008 Wet Process 3,607,766 1,769 2,039 Dry Process 34,767,447 16,504 2,107 Total 38,375,213 18,273 2,100 Average 19,187,607 9,137 2,073

Table 5-15 Employment Impacts Employment Impacts Policy Run Change in Employee Hours Change in Employment NSPS -201,269 94-97 NESHAP -2,351,111 1,105-1,134 NSPS and NESHAP -2,601,741 1,223-1,255

5.4.1.8 Total Social Cost In ISIS, total social cost is the cost to comply with the policy under analysis. This cost includes installation of controls and monitoring requirements as well as the cost to society due to the changes in supply, demand, employment and price effects. Ta ble 5-16 shows the change in surplus for each policy analyzed in ISIS. As shown in Table 5-16 the su rplus for the cement industry may fall by $95 million under the NSPS, $520 million under the NESHAP, while it may fall $385 million under the combination of NSPS and NESHAP.
Table 5-16 Total Social Cost Surplus Loss (Million Dollars) Policy Run Reference Case Policy Ca se Change in Surplus % Change NSPS 10,050 9,955 -95 -0.95 NESHAP 9,229 8,808 -421 -4.56 NSPS and NESHAP 9,229 8,844 -385 -4.17

5.4.1.9 Emissions Table 5-17 shows the estimated emission changes in 2013 for Hg, THC, PM, HCl, SO 2 and NO x for each policy analyzed.5-9 Table 5-17 Emissions

Emissions (tpy) Policy Run Pollutant Reference Case Policy Case Change in Emissions Percent Change

NSPS PM 7,645 7,561 83 1.09 Hg 5.78 5.32 0.46 7.94 THC 12,207 12,185 22 0.18 HCl 4,611 3,925 686 14.88 NO x 126,751 116,043 10,708 8.45 SO 2 122,825 114,481 8,344 6.79

NESHAP PM 6,349 620 5,729 90.23 Hg 7.25 1.24 6.01 82.91 THC 11,915 1,106 10,809 90.72 HCl 4,434 128 4,307 97.12 NO x 123,372 109,214 14,159 11.48 SO 2 115,163 34,919 80,245 69.68

NSPS and NESHAP PM 6,349 622 5,727 90.20 Hg 7.25 1.25 6.00 82.76 THC 11,915 1,060 10,8545 91.10 HCl 4,434 137 4,297 96.91 NO x 123,372 96,154 27,218 22.06 SO 2 115,164 35,775 79,389 68.945-10 

### References for Chapter 5 EPA (2010). August 6, 2010. "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS" Memorandum prepared for Keith Barnett by RTI. US Environmental Protection Agency.
PCA. December 2005. U.S. and Canadian Labor -Energy Input Survey 2005 . Skokie, IL: PCA’s Economic Research Department. Portland Cement Association
PCA. December 2008. U.S. and Canadian Labor -Energy Input Survey 2008 . Skokie, IL: PCA’s Economic Research Department. Portland Cement Association
PCA. 2009. Forecast Report: Long-Term Cement Consumption Outlook . By Ed Sullivan. October 28, 2009. Portland Cement Association
PCA. 2010. Forecast Report: Capacity Update. By Ed Sullivan. April 28, 2010. Portland Cement Association5-11 

## Disclaimer
Reference herein to any specific commercial products , process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily cons titute or imply its endorsement, recommendation, or favoring by the United States Government. The view s and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government, and shall not be used for advertising or product endorsement purposes.