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 2010

Table of Contents

  TOC \o "1-3" \h \z \u    HYPERLINK \l "_Toc268781919"  List of Tables	  PAGEREF _Toc268781919 \h  iv

  HYPERLINK \l "_Toc268781920"  List of Figures	  PAGEREF _Toc268781920 \h  v

  HYPERLINK \l "_Toc268781921"  Conversion Table – English Units to SI Units	  PAGEREF _Toc268781921 \h  ix

  HYPERLINK \l "_Toc268781922"  Chapter 1 Introduction	  PAGEREF _Toc268781922 \h  1-1

  HYPERLINK \l "_Toc268781923"  1.1	The US Cement Industry	  PAGEREF _Toc268781923 \h  1-1

  HYPERLINK \l "_Toc268781924"  1.1.1	Cement Types and Categories	  PAGEREF _Toc268781924 \h  1-1

  HYPERLINK \l "_Toc268781925"  1.1.2	Overview of the Cement Manufacturing Process	  PAGEREF _Toc268781925 \h  1-2

  HYPERLINK \l "_Toc268781926"  1.1.3	Kiln Types and Their Use	  PAGEREF _Toc268781926 \h  1-5

  HYPERLINK \l "_Toc268781927"  1.1.4	Portland Cement Production in the U.S.	  PAGEREF _Toc268781927 \h  1-7

  HYPERLINK \l "_Toc268781928"  1.1.5	Imports of Portland Cement in the U.S.	  PAGEREF _Toc268781928 \h  1-8

  HYPERLINK \l "_Toc268781929"  1.1.6	Cement Demand Centers	  PAGEREF _Toc268781929 \h  1-10

  HYPERLINK \l "_Toc268781930"  1.2	Emissions from the U.S. Cement Industry and Applicable Regulations	  PAGEREF _Toc268781930 \h  1-10

  HYPERLINK \l "_Toc268781931"  1.3	Overview of ISIS	  PAGEREF _Toc268781931 \h  1-12

  HYPERLINK \l "_Toc268781932"  References for Chapter 1	  PAGEREF _Toc268781932 \h  1-14

  HYPERLINK \l "_Toc268781933"  Chapter 2 ISIS Mathematical Framework	  PAGEREF _Toc268781933 \h  2-1

  HYPERLINK \l "_Toc268781934"  2.1	Indexes, Sets, and Mappings	  PAGEREF _Toc268781934 \h  2-3

  HYPERLINK \l "_Toc268781935"  2.2	Objective Function	  PAGEREF _Toc268781935 \h  2-4

  HYPERLINK \l "_Toc268781936"  2.3	Supply	  PAGEREF _Toc268781936 \h  2-7

  HYPERLINK \l "_Toc268781937"  2.4	Production Capacity and Supply Costs   PAGEREF _Toc268781937 \h  2-10

  HYPERLINK \l "_Toc268781938"  2.5	Emissions	  PAGEREF _Toc268781938 \h  2-14

  HYPERLINK \l "_Toc268781939"  2.6	Controls and Costs	  PAGEREF _Toc268781939 \h  2-16

  HYPERLINK \l "_Toc268781940"  2.7	Costs of Energy Efficiency Measures	  PAGEREF _Toc268781940 \h  2-23

  HYPERLINK \l "_Toc268781941"  2.8	Policy Options	  PAGEREF _Toc268781941 \h  2-26

  HYPERLINK \l "_Toc268781942"  2.9	Optimization and Post-Processing	  PAGEREF _Toc268781942 \h  2-27

  HYPERLINK \l "_Toc268781943"  References for Chapter 2	  PAGEREF _Toc268781943 \h  2-29

  HYPERLINK \l "_Toc268781944"  Chapter 3 Input Data for ISIS-Cement Model	  PAGEREF _Toc268781944 \h  3-1

  HYPERLINK \l "_Toc268781945"  3.1	Data Requirements	  PAGEREF _Toc268781945 \h  3-1

  HYPERLINK \l "_Toc268781946"  3.2	Cement-Specific Data	  PAGEREF _Toc268781946 \h  3-1

  HYPERLINK \l "_Toc268781947"  3.2.1	Industry, Fuel, and Emissions	  PAGEREF _Toc268781947 \h  3-1

  HYPERLINK \l "_Toc268781948"  3.2.2	Control Technologies and Emission Abatement Approaches	  PAGEREF _Toc268781948 \h  3-13

  HYPERLINK \l "_Toc268781949"  3.2.3	Policy and Economic Parameters	  PAGEREF _Toc268781949 \h  3-13

  HYPERLINK \l "_Toc268781950"  References for Chapter 3	  PAGEREF _Toc268781950 \h  3-24

  HYPERLINK \l "_Toc268781951"  Chapter 4 Model Calibration	  PAGEREF _Toc268781951 \h  4-1

  HYPERLINK \l "_Toc268781952"  4.1	Calibration Methodology	  PAGEREF _Toc268781952 \h  4-1

  HYPERLINK \l "_Toc268781953"  4.2	Data for Calibration	  PAGEREF _Toc268781953 \h  4-1

  HYPERLINK \l "_Toc268781954"  4.2.1	Cement Prices in USGS Districts	  PAGEREF _Toc268781954 \h  4-2

  HYPERLINK \l "_Toc268781955"  4.2.2	Cement Production in USGS Districts	  PAGEREF _Toc268781955 \h  4-2

  HYPERLINK \l "_Toc268781956"  4.2.3	Cement Imports by Import Districts   PAGEREF _Toc268781956 \h  4-2

  HYPERLINK \l "_Toc268781957"  4.3	Results of Calibration	  PAGEREF _Toc268781957 \h  4-2

  HYPERLINK \l "_Toc268781958"  4.4	Conclusions	  PAGEREF _Toc268781958 \h  4-10

  HYPERLINK \l "_Toc268781959"  References for Chapter 4	  PAGEREF _Toc268781959 \h  4-11

  HYPERLINK \l "_Toc268781960"  Chapter 5 Analysis and Results of the Portland Cement NESHAP and NSPS	  PAGEREF _Toc268781960 \h  5-1

  HYPERLINK \l "_Toc268781961"  5.1	Affected Sources	  PAGEREF _Toc268781961 \h  5-1

  HYPERLINK \l "_Toc268781962"  5.2	Emission Limits	  PAGEREF _Toc268781962 \h  5-2

  HYPERLINK \l "_Toc268781963"  5.3	Control Technologies	  PAGEREF _Toc268781963 \h  5-3

  HYPERLINK \l "_Toc268781964"  5.4	ISIS Runs and Results	  PAGEREF _Toc268781964 \h  5-3

  HYPERLINK \l "_Toc268781965"  5.4.1	ISIS Results	  PAGEREF _Toc268781965 \h  5-4

  HYPERLINK \l "_Toc268781966"  References for Chapter 5	  PAGEREF _Toc268781966 \h  5-10

  HYPERLINK \l "_Toc268781967"  Disclaimer	  PAGEREF _Toc268781967 \h  5-11

 List of Tables

  TOC \t "Table Caption" \c  Table 1-1.	Typical Average Heat Input by Cement Kiln Type	  PAGEREF _Toc268702374 \h  1-5

Table 1-2.	Number of Kilns by Kiln Type in the U.S. in 2005 and 2009	  PAGEREF _Toc268702375 \h  1-6

Table 1-3.	Largest Hydraulic Cement and Clinker Import Custom Districts in the U.S. in 2005	  PAGEREF _Toc268702376 \h  1-9

Table 3-1.	Summary of Kilns Modeled in the ISIS Cement Industry Model	  PAGEREF _Toc268702377 \h  3-1

Table 3-2.	Portland Cement Demand in Millions of Metric Tons	  PAGEREF _Toc268702378 \h  3-4

Table 3-3.	Bulk Shipment Costs (Cents per Metric Ton per Mile)	  PAGEREF _Toc268702379 \h  3-6

Table 3-4.	Portland Cement and Clinker Imports in Million Metric Tons, by Customs District in 2005	  PAGEREF _Toc268702380 \h  3-6

Table 3-5.	Specific Fuel Consumption and Total Exhaust Gas Flow Rate (wet) for Various Kiln Types	  PAGEREF _Toc268702381 \h  3-9

Table 3-6.	Estimated Uncontrolled NOX Emission Intensities for Cement Kilns	  PAGEREF _Toc268702382 \h  3-10

Table 3-7.	Average SO2 Emissions for Each Kiln Type in Each State	  PAGEREF _Toc268702383 \h  3-11

Table 3-8.	Approximate CO2 and H2O Produced from Combustion of Fuels	  PAGEREF _Toc268702384 \h  3-12

Table 3-9.	NOX Control Technologies for Cement Kilns	  PAGEREF _Toc268702385 \h  3-15

Table 3-10.	SO2 Control Technologies for Cement Kilns	  PAGEREF _Toc268702386 \h  3-17

Table 3-11.	CO2 Control Technologies for Cement Kilns	  PAGEREF _Toc268702387 \h  3-19

Table 3-12.	HCl, Hg, and THC Control Technologies for Cement Kilns	  PAGEREF _Toc268702388 \h  3-20

Table 3-13.	Multimedia Impacts of Process Capacity Replacement on Cement Kiln Operation1	  PAGEREF _Toc268702389 \h  3-20

Table 3-14.	Energy Efficiency Measures for Raw Materials Preparation	  PAGEREF _Toc268702390 \h  3-21

Table 3-15.	Energy Efficiency Clinker Making Measures	  PAGEREF _Toc268702391 \h  3-22

Table 3-16.	Energy Efficiency Measures for Finish Grinding	  PAGEREF _Toc268702392 \h  3-23

Table 3-17.	Energy Efficiency Plant-wide Measures	  PAGEREF _Toc268702393 \h  3-23

Table 4-1.	Cement Prices ($/short ton) for USGS Districts	  PAGEREF _Toc268702394 \h  4-4

Table 4-2.	Cement Production (short tons) for Various USGS Districts	  PAGEREF _Toc268702395 \h  4-5

Table 4-3.	Cement Imports (except from Canada and Mexico) for Import Districts (short tons)	  PAGEREF _Toc268702396 \h  4-6

Table 4-4.	Reported and Calculated Cement Prices in USGS Districts (2005)	  PAGEREF _Toc268702397 \h  4-7

Table 4-5.	Reported and Calculated Cement Prices in USGS Districts (2006)	  PAGEREF _Toc268702398 \h  4-8

Table 4-6.	Reported and Calculated Cement Prices in USGS Districts (2007)	  PAGEREF _Toc268702399 \h  4-9

Table 4-7.	Aggregate production (reported and modeled) for 2005-2007	  PAGEREF _Toc268702400 \h  4-9

Table 5-1.  Final NESHAP Emission Limits	  PAGEREF _Toc268702401 \h  5-2

Table 5-2.  Final NSPS Emission Limits	  PAGEREF _Toc268702402 \h  5-2

Table 5-3.  Control Technology and Control Efficiency Matrix by Pollutant	  PAGEREF _Toc268702403 \h  5-3

Table 5-4 Policy Impact on U.S. Cement Demand in 2013	  PAGEREF _Toc268702404 \h  5-5

Table 5-5 Policy Impact on U.S. Cement Production	  PAGEREF _Toc268702405 \h  5-5

Table 5-6 Policy Impact on U.S. Cement Imports	  PAGEREF _Toc268702406 \h  5-5

Table 5-7 Policy Impact on U.S. Cement Prices	  PAGEREF _Toc268702407 \h  5-5

Table 5-8 Control Technology Installation	  PAGEREF _Toc268702408 \h  5-6

Table 5-9 Control Cost	  PAGEREF _Toc268702409 \h  5-6

Table 5-10 Cement Capacity	  PAGEREF _Toc268702410 \h  5-6

Table 5-11 Kiln Population	  PAGEREF _Toc268702411 \h  5-7

Table 5-12 Number of Cement Facilities	  PAGEREF _Toc268702412 \h  5-7

Table 5-13 Change in Employee Hours	  PAGEREF _Toc268702413 \h  5-7

Table 5-14 PCA’s Labor Data: 2005 and 2008	  PAGEREF _Toc268702414 \h  5-8

Table 5-15 Employment Impacts	  PAGEREF _Toc268702415 \h  5-8

Table 5-16 Total Social Cost	  PAGEREF _Toc268702416 \h  5-8

Table 5-17 Emissions	  PAGEREF _Toc268702417 \h  5-9

List of Figures

  TOC \t "Figure Caption" \c  Figure 1-1. 	Schematic of the Wet Cement Process	  PAGEREF _Toc268702288 \h  1-3

Figure 1-2. 	Schematic of the Dry Cement Process with Cyclone Preheater	  PAGEREF _Toc268702289 \h  1-4

Figure 1-3.	Trends in Cement Kiln Type and Capacity in the U.S. (1995 to 2008)	  PAGEREF _Toc268702290 \h  1-6

Figure 1-4.	Portland Cement Plant Locations	  PAGEREF _Toc268702291 \h  1-8

Figure 1-5.	Imports of Clinker and Cement from 1998 to 2008 (PCA, 2009a)   PAGEREF _Toc268702292 \h  1-10

Figure 1-6.	Portland Cement Facilities and O3 NAA, PM2.5 NAA, and Class 1 Areas	  PAGEREF _Toc268702294 \h  1-12

Figure 1-7.	An Integrated View of Pollution Generation Pathways, Emissions Abatement Approaches, and Multimedia Impacts for an Industrial Sector	  PAGEREF _Toc268702295 \h  1-14

Figure 2-1.	Consumer and Producer Surplus in a Market	  PAGEREF _Toc268702296 \h  2-2

Figure 2-2.	Broad Modules and Associated Information Flows Utilized in the ISIS Framework	  PAGEREF _Toc268702297 \h  2-2

Figure 2-3.	Stepwise Calculation of the Demand Curve	  PAGEREF _Toc268702298 \h  2-5

Figure 3-1.	Distribution of Cement Kilns in the United States as of 2009   PAGEREF _Toc268702299 \h  3-3

Figure 3-2.	Commercial Fuel Use Profile by U.S. Cement Industry in 2005.   PAGEREF _Toc268702300 \h  3-8

Figure 5-1 	Portland Cement Facilities and Kilns Modeled in ISIS-Cement	  PAGEREF _Toc268702301 \h  5-1

 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

CO2	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	Florida

FOM	Fixed Operation and Maintenance

GA	Georgia

GAMS	General Algebraic Modeling System

GDP	Gross Domestic Product

GHG	Greenhouse Gas

H2O	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 NOX 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 Management

NESHAP	National Emission Standards for Hazardous Air Pollutants

NM	New Mexico

NOX	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

SOX	Oxides of Sulfur

SPE	Spatial Price Equilibrium

SR	Seal Replacement

TDF	Tire Derived Fuel

THC	Total Hydrocarbons

TRAGIS	Transportation Routing Analysis Geographical Information System

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

m2	ft2	9.29 × 10-2

m3	ft3	2.83 × 10-2

°C	°F	5/9 × (°F – 32)

kg	lb	0.454

J/kg	Btu/lb	1.33 × 10-4

m3/s	cfm	4.72 × 10-4

m3/s	gpm	6.31 × 10-5

J/kWh	Btu/kWh	1055.056

mills	$	0.001

kg/m2	in. Hg	345.31

metric ton	short ton	0.907

Introduction

In the National Academy of Science’s 2004 report, “Air Quality Management in the United States,” the National Research Council (NRC) recommended to the U.S. Environmental Protection Agency (EPA) that standard setting, planning, and control strategy development should be based on integrated assessments that consider multiple pollutants, and that these integrated assessments should be conducted in a comprehensive and coordinated manner (NAS, 2004). With these recommendations, EPA began to move toward establishing multipollutant and sector-based approaches to manage air quality and environmental protection. The benefits of multipollutant and sector-based analyses and approaches include the ability to identify optimum strategies (considering feasibility, costs, and benefits across all pollutant types such as criteria, toxics, and others), while streamlining administrative and compliance complexities and reducing conflicting and redundant requirements.

The development of policy options for managing emissions and air quality can be made more effective and efficient through sophisticated analyses of relevant technical and economic factors. Such analyses are greatly enhanced by the use of an appropriate modeling framework. Accordingly, the Industrial Sectors Integrated Solutions (ISIS) model has been developed at EPA. Currently, the ISIS model is populated with US cement manufacturing data, and efforts are underway to build representations of the US pulp and paper sector and the US iron and steel sector. This document describes the framework of EPA’s ISIS model and its application to the US cement manufacturing industry 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.

The US Cement Industry

Cement Types and Categories

Cement is a finely ground powder which, when mixed with water, forms a hardening paste of calcium silicate hydrates and calcium aluminate hydrates. Cement is used in mortar (to bind together bricks or stones) and concrete (bulk rock-like building material made from cement, aggregate, sand, and water). Concrete production uses the majority of cement produced.

Portland cement and blended cement are used in concrete production, but Portland cement is by far the most common type of cement used for concrete production. By modifying the raw material mix and, to some degree, the temperatures utilized in manufacturing, slight compositional variations can be achieved to produce Portland cements with slightly different properties. In the U.S., the different varieties of Portland cement are denoted per the American Society for Testing and Materials (ASTM) Specification C-150. The ASTM standard C-150 recognizes eight types of Portland cement:

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

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

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

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

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

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

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

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

Portland cements are usually gray, but a more expensive white Portland cement (generally within the Type I or II designations) can be obtained by processing only raw materials with very low iron and transition-elements content. In addition to the eight types of Portland cement listed above, small volumes of specialty cements are also manufactured. Other types of specialty cement include blended cement (Portland cement mixed together with blast furnace slag or other pozzolan), pozzolan-lime cement, masonry cement, and aluminous cement (PCA, 2008a). The common industry practice, and that of the US Geological Survey (USGS), includes, within the Portland cement designation, a number of other cements not within ASTM C-150, which are composed largely of Portland cement and are used for similar applications (e.g., concrete) (USGS, 2005). These cements include blended cement, block cement, expansive cement, oil well cement, regulated fast setting cement, and waterproof cement. Plastic cements and Portland-lime cements are being grouped within the masonry cement designation. Because Portland cement accounts for approximately 95% of the cement industry’s total production (van Oss, 2008), and because the costs and trends of this industry sector can be adequately captured by describing the market processes associated with the production, distribution, and use of Portland cement, in this work the focus is on Portland cement. In 2006, Portland cement’s market share in the U.S. was 94%, while masonry cement’s market share comprised the remaining 6% (USGS, 2007a).

Overview of the Cement Manufacturing Process

Portland cement is produced from raw materials such as limestone, chalk, shale, clay, and sand. These raw materials are quarried, crushed, finely ground, and blended to the correct chemical composition. Small quantities of iron ore, alumina, and other minerals may be added to adjust the raw material composition. The fine raw material is fed into a large rotary kiln (cylindrical furnace) where it is heated to extremely high temperatures [about 2640 °F (about 1450 °C)]. The high temperature causes the raw material to react and form a hard nodular material called “clinker”. Clinker is cooled and ground with approximately 5% gypsum and other minor additives to produce Portland cement. The main steps in the cement manufacturing process are illustrated in Figures 1-1 and 1-2 showing the wet process and the dry process with cyclone preheater. The schematic for a precalciner kiln would be very similar to that shown in Figure 1-2, with the addition of a calciner vessel.

The heart of the clinker production process is the kiln, which can be rotary or vertical shaft designs. Rotary kilns are commonly used in the U.S. and elsewhere. These kilns are 6-8 m in diameter and 60 m to well over 100 m long. The kilns are set at a slight incline and rotate at 1 to 3 revolutions per minute. The kiln is fired at the lower end and the feed materials move toward the flame as the kiln rotates. The materials reach temperatures between 1400-1500 °C in the kiln. Three steps occur with the raw material mixture during pyroprocessing. First, all moisture is driven off from the materials. Then the calcium carbonate in limestone dissociates into CO2 and calcium oxide (free lime) during calcination. Finally, the lime and other minerals in the raw materials react to form calcium silicates and calcium aluminates, the main components of clinker.

Figure 1-1. 	Schematic of the Wet Cement Process

Source: CEMBUREAU, 1999

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

Source: CEMBUREAU, 1999

Kiln Types and Their Use

Rotary kilns are broadly categorized as dry- and wet-process kilns, depending on how the raw materials are prepared. Wet-process kilns are fed raw material slurry with moisture content ranging between 30 and 40%. A wet-process kiln needs additional length to evaporate the water contained in the raw material feed. Nearly 33% additional kiln energy is consumed in evaporating the water in the slurry.

In dry-process kilns, raw material is fed as dry powder. There are three major variations of dry-process kilns in operation in the U.S.: long dry kilns, preheater kilns, and preheater/precalciner kilns. In preheater kilns and preheater/precalciner kilns, the early stages of pyroprocessing occur before the materials enter the rotary kiln. Preheater and preheater/precalciner kilns have higher production capacities and greater fuel efficiency compared to other types of cement kilns. Table 1-1 shows heat input in terms of millions of British Thermal Units (MMBtu)/ton of clinker for various types of kilns. As the data clearly demonstrate, preheater/precalciner kilns provide greater fuel efficiency. The replacement of wet and (certain) dry process kiln capacity with modern kiln processes can yield, theoretically, substantial reductions in fuel use due to fuel efficiency gains. As the industry moves toward more efficient processes, replacement of wet and long dry process capacity with more efficient kiln process technologies is expected.

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

Kiln Type	Heat Input,

MMBtu/ton of clinker

Wet	6.0

Long Dry	4.5

Preheater	3.8

Preheater/Precalciner	3.3

Source: EPA, 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 kilns. 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 Portland Cement Association (PCA) data show that average kiln capacity also increased by 27%, from 405,000 to 556,000 tons per year between 1995 and 2004. The number of kilns operating in the U.S. in 2005 compared with the number of kilns in operation in 2009 is shown in Table 1-2. The trend in kiln design and average kiln capacity is shown in Figure 1-3.

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

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

Note:  The PCA Plant Information Summary became biennial in 2004.  The 2005 data were obtained by personal communications between EPA and the PCA.

As reported by the USGS, in 2005, the U.S. Portland cement industry produced approximately 87 million metric tons of clinker, of which 13.5% 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).

Portland Cement Production in the U.S.

The cement industry remains a vital industry in the U.S. and throughout the world. In the U.S., Portland cement is a $12 billion industry (van Oss, 2008). In 2005, the U.S. consumed a record 128 million metric tons of Portland cement (USGS, 2007b: Table 1).

In 2005, Portland cement was produced at 107 plants in the U.S., including 37 states and Puerto Rico. The locations of the clinker-producing Portland cement plant facilities in 2005 are shown in Figure 1-4. The cement manufacturing sector in the U.S. is concentrated among a relatively small number of companies, many of which are owned by or are subsidiaries of foreign companies (USGS, 2007b). Together, ten companies accounted for about 80% of the total cement production in U.S. for 2005 (USGS, 2007b). California, Texas, Pennsylvania, Florida, and Alabama are the five leading cement-producing states, which accounted for about 43% 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 projected at $6.9 billion. Note that a typical project for a new facility (greenfield) from ground breaking to startup has a timeframe of about 2 to 3 years. Building a new kiln in an existing facility (brownfield) can take approximately 1 year (EPA, 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).

Figure 1-4.	Portland Cement Plant Locations

Imports of Portland Cement in the U.S.

Portland cement is not only produced and consumed domestically, but it is also traded internationally. In 2005, the U.S. (not including Puerto Rico) produced 94 million metric tons of cement (USGS, 2007b: Table 3) and imported 34 million metric tons of hydraulic cement and clinker (USGS, 2007b: Tables 3 and 18). The level of imports to the U.S. is highly cyclical, with domestic producers importing primarily when domestic plants are at full capacity and cannot meet excess demand. Imports also occur when domestic producers seek to capture market share. Generally, imported cement and clinker make up 20 to 27% 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 clinker suppliers to the U.S. were, in descending order, Canada, China, Thailand, Greece, the Republic of Korea, Venezuela, Mexico, Colombia, Taiwan, and Sweden. The ten busiest ports of entry within customs districts existing in 2005 were, in descending order, New Orleans, Tampa, Los Angeles, Houston-Galveston, San Francisco, Miami, Seattle, Detroit, New York, and Charleston (South Carolina) (USGS, 2007b). Table 1-3 shows the major customs districts for hydraulic cement and clinker imports in the U.S. Figure 1-5 shows the imports of clinker and cement from 1998 to 2008.

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

Custom District	Import of Hydraulic Cement and Clinker

	Percentage of Total US Imports

	thousands of metric tons	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)

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

Cement Demand Centers

Because of the relatively high transportation costs, the U.S. cement industry is structured around state-specific cement demand centers. PCA reports that the vast majority of cement produced in the U.S. is being transported less than 300 miles by truck due to cement's low value by weight and high cost of transport (PCA, 2005). However, cement may be transported over longer distances, especially when the less expensive rail and water transportation modes are available (APCA, 1997).

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

Criteria pollutants, hazardous air pollutants, and carbon dioxide (CO2) are released during cement manufacturing. Nitrogen oxide (NOx) emissions from cement kilns result primarily from the combustion process: oxidation of fuel nitrogen (fuel NOx) and the oxidation of nitrogen in the combustion air (thermal NOx). EPA’s 2005 National Air Toxics Assessment (NATA) Inventory reports that cement kilns released 181,000 metric tons (200,000 short tons) of NOX emissions from the combustion of fuels (EPA, 2009).

Sulfur dioxide (SO2) emissions  from cement kilns result from the combustion of sulfur-bearing compounds in coal, oil, and petroleum coke, as well as sulfur compounds in raw materials. Sulfur in the fuel will oxidize to SO2 during pyroprocessing and a significant amount is likely to be captured in the form of sulfates as the flue gas passes through the calcination zone. Compared to long dry and wet kilns, preheater and precalciner kilns tend to be more effective at capturing fuel-generated SO2. Accordingly, oxidation of sulfur in the feed materials is likely to be the major component of total SO2 emissions. The 2005 NATA Inventory reflects that cement kilns released 133,000 metric tons (147,000 short tons) of SO2 emissions in 2005.

Particulate matter (PM) emissions result from quarrying operations (the crushing and grinding of raw materials and clinker) as well as kiln line operations. The 2005 NATA Inventory estimates show that, in 2005, cement kilns released 10,000 tons of PM10 emissions. The cement industry is also a source of HAPs (e.g., hydrochloric acid vapor and chlorine), as well as metals including but not limited to mercury, antimony, cadmium, and lead (EPA, 2008).

The cement manufacturing process is also a source of carbon dioxide (CO2) emissions. CO2 emissions are a product of the combustion of fuel as well as the calcination of the limestone in the raw meal. The CO2 from fuel combustion can be calculated from heat input and fuel characteristics combined with clinker production. The CO2 from calcination can be calculated taking into account the amount of limestone used as a source of clinker calcium. Limestone currently is the predominant source of calcium for the clinker. Pure limestone would produce 0.44 tons of CO2 for every ton of limestone completely calcined to calcium oxide. Substitute materials may be used in lieu of limestone with the effect of reducing the CO2 emissions from clinker production. The CO2 emissions reduction would be proportional to the amount of substitute materials added.

Multiple regulatory requirements to reduce criteria air pollutants and HAPs emissions currently apply to the cement industry sector. The New Source Performance Standards (NSPS) and the National Emissions Standards for Hazardous Air Pollutants (NESHAPs) are two of the federal requirements that apply to cement facilities. Additionally, state and local regulatory requirements might apply to individual cement facilities depending on their locations. In 2008, 44 cement facilities were located within ozone (O3) nonattainment areas (NAA), while 20 facilities were within PM2.5 nonattainment areas. Seventeen facilities were found to be located in (or within 30 miles of) Class 1 areas, as shown in Figure 1-6. Class 1 areas are areas of special natural, scenic, recreational, or historic (national or regional) value for which the Prevention of Significant Deterioration (PSD) regulations provide special protection.

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

Overview of ISIS

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

Optimal (least cost) industry operation.

Cost-effective controls to meet the demand for cement.

Emission reduction requirements over the time period of interest.

ISIS design allows for incorporating multiple industries within a multi-market, multi-product, multipollutant, and multi-region emissions trading framework. The objective function in ISIS maximizes total surplus and uses an elastic formulation of the demand function to estimate area under the demand curve.

The ISIS code is written in the General Algebraic Modeling System (GAMS) language. Input data, organized in various spreadsheets of a Microsoft Excel workbook, are passed on to the GAMS files. These input data consist of an industry database, which provides unit-level production, capacity, production cost, and emissions information. The controls database provides information regarding applicable air pollution control technologies and their cost and emission control characteristics. A policy module is used to specify various parameters of interest to the policy analyst, such as emissions cap, emission reduction scenarios, and discount rate. The input data, control data, and policy parameters are then transmitted to the optimization part of the ISIS model, where they are used to solve the selected base and policy cases. After solving, the results are post-processed to calculate values of various outputs of interest. The output data are exported to Excel spreadsheets for further analyses and graphical representation of selected results.

Within an industrial sector, generally emissions arise from four pathways: (1) on-site emissions due to combustion of fossil fuels for energy at plants; (2) on-site emissions due to processing of certain raw materials (e.g., limestone calcination in cement plants, non-energy uses of fossil fuels in chemical processing and metal smelting); (3) off-site emissions due to combustion of fossil fuels at power plants to generate the electricity needed by the industrial sector; and (4) overseas emissions associated with imports. These pathways are depicted in Figure 1-7.

Also shown in Figure 1-7 are the potential options for abating emissions from industrial sectors and multimedia impacts. The options shown in green are pollution prevention measures and the ones in red are mitigation measures. Clearly, the integrated picture presented in Figure 1-7 makes a compelling case for considering commodity production/supply activities along with emissions while developing holistic emission reduction strategies. While developing the ISIS framework, care has been taken to build the emission pathways and abatement options shown in Figure 1-7. Example emission reduction policies that can be evaluated using ISIS are:

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

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

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

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

Regional or national requirements.

Figure 1-7.	An Integrated View of Pollution Generation Pathways, Emissions Abatement Approaches, and Multimedia Impacts for an Industrial Sector

References for Chapter 1

APCA (1997). Comments on EPA’s Draft Economic Analysis of Air Pollution Regulations for the Portland Cement Industry (May 1996), prepared for American Portland Cement Alliance by Environomics, January 29, 1997.

CEMBUREAU (1999). Best Available Techniques for the Cement Industry, CEMBUREAU Report, The European Cement Association, December 1999. D/1999/5457/December, Brussels.  HYPERLINK "http://www.cembureau.be" http://www.cembureau.be

EPA (1998). June 1998. Regulatory Impact Analysis of Cement Kiln Dust Rulemaking. Washington, DC: U.S. Environmental Protection Agency.  HYPERLINK "http://www.epa.gov/osw/nonhaz/industrial/special/ckd/ckd/ckdcostt.pdf" http://www.epa.gov/osw/nonhaz/industrial/special/ckd/ckd/ckdcostt.pdf , accessed October 21, 2008.

EPA (2007a). Personal communication from Alvaro Linero, Florida Department of Environmental Protection to Keith Barnett, US EPA, on December 4, 2007.

EPA (2007b). Alternative Control Techniques Document Update – NOX Emissions from New Cement Kilns. EPA-453/R-07-006, November 2007.  HYPERLINK "http://www.epa.gov/ttn/catc/dir1/cement_updt_1107.pdf" 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.  HYPERLINK "http://www.epa.gov/ttn/chief/ap42/ch11/final/c11s06.pdf" http://www.epa.gov/ttn/chief/ap42/ch11/final/c11s06.pdf , accessed October 21, 2008. Complete document:  HYPERLINK "http://www.epa.gov/ttn/chief/ap42/" http://www.epa.gov/ttn/chief/ap42/ , accessed October 21, 2008.

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

NAS (2004). Air Quality Management in the United States. National Research Council (U.S.), Committee on Air Quality Management in the United States, National Academies Press, Washington, 2004.  HYPERLINK "http://books.nap.edu/catalog.php?record_id=10728" http://books.nap.edu/catalog.php?record_id=10728 , accessed October 21, 2008.

PCA (2005). Letter from David S. Hubbard, Director, Legislative Affairs, Portland Cement Association. RE: Hours of Service of Drivers; Proposed Rule (Docket Number FMCSA-2004-19608), March 10, 2005.  HYPERLINK "http://www.cement.org/exec/DHOS%20Comments%203.10.05.pdf" http://www.cement.org/exec/DHOS%20Comments%203.10.05.pdf , accessed October 21, 2008.

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

PCA (2008a). History & Manufacture of Portland Cement. Portland Cement Association.  HYPERLINK "http://www.cement.org/basics/concretebasics_history.asp" 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 Consumption Outlook. By Ed Sullivan January 31, 2008.  HYPERLINK "http://www.cement.org/econ/pdf/Long-TermFlashwinter2007nonmember.pdf" http://www.cement.org/econ/pdf/Long-TermFlashwinter2007nonmember.pdf , accessed October 21, 2008.

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

PCA (2009b).  PCA Capacity Report. Flash Report.  Portland Cement Association’s Economic Research Department.   Updated October 19, 2009.

Staudt, J. (2009). NOX, SO2 and CO2 Emissions from Cement Kilns. Andover Technology Partners, Memorandum to Ravi Srivastava, March 10, 2009.

USGS (2005). Background Facts and Issues Concerning Cement and Cement Data. U.S. Geological Survey, Open-File Report 2005-1152.  HYPERLINK "http://pubs.usgs.gov/of/2005/1152/2005-1152.pdf" http://pubs.usgs.gov/of/2005/1152/2005-1152.pdf , accessed October 21, 2008.

USGS (2007a). Mineral Commodity Summaries. pp. 40-41, January 2007,  HYPERLINK "http://minerals.usgs.gov/minerals/pubs/commodity/cement/cemenmcs07.pdf" 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,  HYPERLINK "http://minerals.usgs.gov/minerals/pubs/commodity/cement/cemenmyb05.pdf" http://minerals.usgs.gov/minerals/pubs/commodity/cement/cemenmyb05.pdf , accessed September 24, 2009.

van Oss, H.G. (2008). Personal communication from Hendrik G. van Oss, USGS, to Elineth Torres, US EPA, on July 7, 2008.

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ISIS Mathematical Framework

ISIS is a sector-based dynamic linear programming model that can determine optimal sector operation for meeting demand and pollution reduction requirements over specified time periods. The objective in an ISIS simulation is to maximize total surplus (see Figure 2-1) over a horizon of interest for an industry, which, in general, can be a multi-product one.

The general concept of spatial price equilibrium (SPE) in a network, where the mutual influences of production, transportation, and consumption patterns are given full consideration, has been developed over the past 6 decades. In SPE network models, interregional economies are simulated by finding the balance of demand, supply, and trade that will result in competitive market equilibrium among the regions. Enke (1951) first demonstrated how the cost of transportation might be included in an equilibrium analysis of spatially separated markets by means of analogy with resistance to the flow of current in an electric circuit. Shortly after Enke, Samuelson (1952) analyzed interregional flows of commodities and market equilibrium using a linear programming formulation. In this type of formulation, the equilibrium for each market of a sector is equivalent to the quantities and prices that result while maximizing the sum of consumer and producer surpluses for each market of the sector. This sum is referred to as the total surplus or net social payoff of the sector; McCarl and Spreen (1980) provide interpretation and justification. The linear programming formulation of the SPE problem was developed by Duloy and Norton (1975).

Using Figure 2-1, the definition of the suppliers’ surplus corresponding to a quantity Q of a commodity is the difference between the total revenue and the total cost of supplying the commodity. This surplus (gross profit) is given by the area under the horizontal line P1-P minus the area under the inverse supply curve up to point P. Similarly, the consumers’ surplus corresponding to a quantity Q is given by the area under the inverse demand curve up to point C minus the area under the horizontal line C1-C. This area is the cumulative opportunity gain of all consumers who purchase the commodity at a price lower than the price they would have been willing to pay. It is evident from Figure 2-1 that the total surplus is maximized exactly when Q is equal to the equilibrium quantity QE. This is a very useful result, as it provides a method for computing the equilibrium.

The total surplus concept has long been a mainstay of social welfare economics because it takes in to account the interests of both consumers and of producers (Samuelson and Nordhaus, 1977).

The broad modules and associated information flows utilized in the ISIS framework are shown in Figure 2-2. While ISIS structure permits accounting for multiple products, the description below is provided relative to one product to bring out the important elements of the formulation and not burden the reader with many details. Also, to make the description more readable and understandable, the input data (i.e., those supplied by the user, or derived from user-supplied data) are shown in bold and the variables, whose values are determined in the optimization process, are shown in italics. This scheme helps in organizing the numerous data elements and variables, and hopefully makes it easier for the reader to relate the data element to descriptions in the previous chapters. Finally, the names of variables and data elements have been chosen to be self-explanatory as far as possible.

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

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

Indexes, Sets, and Mappings

Before we start the description of the mathematical structure of ISIS, it is helpful to define the sets of relevant entities and mappings describing the relationships among various entities.

Indexes (one-dimensional sets)

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

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

i is the set of all units, including existing, replacement, expansion, and new units. Subsets of i described below define more-specific populations of units;

iE(i) is the set of existing units

irepl(i) is the set of replacement units

iExp(i) represents the set of expansion units

iNew(i) represents the set of new units

f is the set of fuels applicable to the sector;

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

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

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

ee is the set of energy efficiency measures; and

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

Mappings

The mappings used in ISIS are:

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

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

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

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

poltirk(t, ri(i,r),k) is the mapping relating availability and applicability of controls k to unit i located in geographic region r and year t; and

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.

Objective Function

As mentioned above, the objective function solved in the ISIS model corresponds to maximizing the total surplus (or minimizing the negative of the total surplus) for the sector of interest over the selected time horizon. This objective function is:

 	(2.2.1)

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

dis(t) is the discount factor,

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

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

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

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

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

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

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

  is the area under the demand-price curves for all markets dc.

Note that in the objective function the term with alprice(t,j) comes into effect only if the user provides values for alprice(t,j). If these values are specified, the model runs as described under the “Allowance Price Inputs” in a later section.

A stepwise approximation of the demand curves (FPL-PELPS, 2003) is used in ISIS so that relevant area can be computed in a linear programming scheme. This approximation is explained below.

Stepwise Approximation of Demand Curves in ISIS

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

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

 	 (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 equation and also reflects how a stepwise approximation of the price-demand curve can be created.

Figure 2-3.	Stepwise Calculation of the Demand Curve

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

 	(2.2.3)

 	(2.2.4)

where

range is a user-supplied parameter with value between 0 and 1. This parameter defines the interval Dmin - Dmax within which the new equilibrium demand quantity is expected to be found. Note range should be large enough to ensure that the solution does fall in the interval Dmin - Dmax. On the other hand a smaller value of range can increase precision of the stepwise approximation. In ISIS a value of 0.5 is used for range.

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

 	(2.2.5)

where

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

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

 	(2.2.6)

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

 	(2.2.7)

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

 	(2.2.8)

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

 	(2.2.9)

The above objective function is minimized with the constraints and related equations described in the following sections to arrive at the relevant optimum solution.

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

Supply

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

 	(2.3.1)

where

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

importedquantity(t,oi,id,dc) is the quantity of commodity received from the origin (country) of imports oi at the domestic import district id and supplied from that district to demand center dc in year t, and

demand(t, dc,dstep) is the demand for the commodity at the dstepth level of the price-demand curve for the demand center dc in year t. The construction of the price-demand curves has been explained above.

Note that transportation of quantity from production units and import districts to demand centers is implicit in the above equation.

The sum of all quantities in year t supplied from a kiln i to various demand centers dc must equal the production level of unit i in that year. Then,

 	(2.3.2)

where

prodn(t, i) is the production level (tons/year) of manufacturing unit i in year t. Note that, in general, production can be from existing units, units added at a plant (i.e., expansion units), newer units replacing units at a plant (i.e., replacement units), and new kilns. Treatment of these is explained in a subsequent section.

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

 	(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,

 	(2.3.4)

where

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

Imports(t,oi,id,istep) is the imported quantity available at the import district id in year t at the istepth level of the imports curve for country oi. Construction of the country (or region) and import district specific imports-cost curves is explained below.

Stepwise Approximation of Imports Curves in ISIS

The treatment of imports is similar to the treatment for elastic demand curves. Again, for clarity, the subscripts t, oi, and id corresponding to time, origin of imports, and import district are dropped in the following explanation.

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

 	(2.3.5)

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

 	(2.3.6)

 	(2.3.7)

where

range is a user-supplied parameter with value between 0 and 1. This parameter defines the interval Imin - Imax within which the new equilibrium imports quantity is expected to be found. Note range should be large enough to ensure that the solution does fall in the interval Imin - Imax. On the other hand a smaller value of range can increase precision of the stepwise approximation. In ISIS a value of 0.5 is used for range.

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

 	 (2.3.8)

where

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

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

 	(2.3.9)

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

 	(2.3.10)

Equation (2.3.10) is used to generate the imports-cost curves for all time periods, origins of imports, and import districts. However, this equation needs specifications of one point on each import function in each time period (i.e., [Iprice0{t, oi, id}, Imports0{t, oi, id}]). To determine such points, a single run of the inelastic version of the ISIS model (with exogenous Imports0[t, oi, id] corresponding to capacities of import districts) is made and then the resulting Iprice0(t, oi, id) are used in Equation (2.3.10) to generate the imports-cost curves for all time periods, origins of imports, and import districts.

Production Capacity and Supply Costs

The ISIS model includes constraints for ensuring that endogenous production capacity changes occur in a realistic way. This section describes how the capacity changes take place in the ISIS framework and the treatment of related costs. Note that various cost elements (e.g., capital cost of a unit [$/ton clinker] in the cement sector) are escalated appropriately to reflect values in years of interest.

Production Capacity Related Constraints

Added capacity in year t is given by

When t = 1,

	(2.4.1a)

For t > 1,

 			(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 vth 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.

	(2.4.2)

The annual costs associated with meeting the demand for the commodity include: (1) annualized capital costs associated with capacity growth (i.e., replacement units, expansion units, and new capacity) and projected units, (2) annual fixed operation and maintenance (FOM) costs, (3) annual variable costs associated with use of labor, raw material, fuel, electricity, and operation and maintenance, (4) annual transportation costs, and (5) annual cost of imports. These costs are described below.

Capital Recovery

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

 	(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

 	(2.4.4)

where

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

 	(2.4.5)

where

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

Variable Costs

The annual variable cost at a unit is calculated using

 	(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:

 	(2.4.7)

where

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

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

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

Annual water consumption related cost is given by

 	(2.4.8)

With annual water consumption given by

 	(2.4.9)

where

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

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

Annual solids discharge related cost is given by

 	(2.4.10)

With annual solids discharges given by

 	(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

 	(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

 	(2.4.13)

where

Iprice(t,oi,id,isteps) is the price (value) of importing commodity from origin oi (foreign country) at the import district dc, at the istepth level of the relevant price-import curve.

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

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

 	(2.4.14)

where

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

imprttransportcostt(t,id,dc) is the cost of transporting one ton of commodity ($/ton) from imprt district id to demand center dc.

Emissions

As discussed in the previous chapter, emissions can be generated from fuel firing and also from use of raw materials (e.g., CO2 emissions from calcination of limestone in cement kiln). As such, both of these emission generation pathways are included in ISIS. Further, the ISIS framework includes algorithms to account for tracking multiple pollutant streams associated with uncontrolled emissions, controlled emissions, pollution prevention from process modifications and energy efficiency measures, and any controls-related effects (e.g., generation of CO2 in a wet SO2 scrubber). These algorithms are described below.

 	(2.5.2)

where

polfuel(t,i,f,j,k,ee) is emission (tons pollutant per ton product) of pollutant j resulting from processing (firing) fuel f and application of any control k and/or energy efficiency measure ee at unit i in year t, taking into account whether a unit is available for operation in that year;

emintensityfuel(i,f,j) is the emission intensity (tons pollutant per ton product) of pollutant j resulting from processing (firing) fuel f at unit i, taking in to account whether any controls (e.g., Best Available Control Technology [BACT]) are already installed on the unit;

modeintensity(i,k,ee) is the modified energy intensity (MMBtu per ton product) needed to produce 1 ton of product at unit i. This energy intensity takes into account any heat input changes accompanying a control k and/or an energy efficiency measure ee;

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

primaryHIchange(i,k) is the change in primary heat input (MMBtu per ton product) due to application of control k at unit i;

secondaryHIchange(i,k) is the change in heat input (MMBtu per ton product) due to any secondary fuel addition resulting from application of control k at unit i;

eHIdispl(i,ee) is the amount of heat input (MMBtu per ton product) displaced (reduced) due to application of energy efficiency measure ee at unit i; and

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

Similarly,

 	(2.5.3)

where

polrmt(t,i,m,j,k,ee) is emission (tons pollutant per ton product) of pollutant j resulting from processing raw material m and application of any control k and/or energy efficiency measure ee at unit i in year t, taking into account whether a unit is available for operation in that year;

emintensityrmt(i,m,j) is the emission intensity (tons pollutant per ton product) of pollutant j resulting from processing raw material m at unit i, taking into account whether any controls (e.g., BACT) are already installed on the unit;

primaryRMTchangepercent(i,k) is the percent change in primary raw material input (tons raw material per ton product) due to application of control k at unit i; and

secondaryRMTchangepercent (i,k) is the percent change in raw material input (tons raw material per ton product) due to any secondary raw material addition resulting from application of control k at unit i.

Finally,

 	(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

 	(2.5.5)

where

varprodn(t,i,f,m,j,k,ee) is the production variable associated with use of kth control and/or eeth energy efficiency measure for jth 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

 	(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

 	(2.5.7)

with,

 	(2.5.8)

where

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

Controls and Costs

The ISIS framework includes constraints to ensure that endogenous applications of sector-based controls and energy efficiency options occur in a realistic manner. A description of these constraints and costs for 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.

 	(2.6.1)

where

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

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

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

 						(2.6.2a)

For t > 1,

 		(2.6.2b)

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

 	(2.6.2c)

where

k and kk are incompatible controls.

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

 	(2.6.3)

where

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

In general, the costs associated with controls comprise the following components: (1) capital and fixed operation and maintenance costs, (2) costs associated with any reagent and/or catalyst consumption, (3) costs associated with any reduction in fuel and/or raw material use, (4) cost associated with electricity consumption, (5) cost associated with byproduct(s), and (6) costs associated with water use. The calculations of these costs are described below. Note that various cost elements (e.g., capital cost of a control [$/ton clinker] for the cement sector) are escalated appropriately to use values in years of interest.

Capital Recovery and Fixed Cost

Annual recovery of capital cost of control k is given by

 	(2.6.4)

Similarly, annual FOM cost is given by

 	(2.6.5)

where

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

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

capcost_c(t,i,k) is the annualized capital cost ($) of kth control application on ith unit,

FOMcost_c(t,i,k) is the annual fixed operation and maintenance cost ($) of kth control application on ith unit, and

CRFcontrol(k) is the capital recovery factor calculated using an appropriate interest rate and time period, ecolifecontrols(k), for capital recovery.

Variable Costs

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

 	(2.6.6)

where

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

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

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

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

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

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

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

 	(2.6.7)

where

primaryRMTchangepercent is the percent change in primary raw material with use of kth technology,

secondaryRMTchange(i,k) is the percent change raw material input corresponding to any secondary raw material addition with use of kth technology, and

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

Annual reagent consumption costs are

 	(2.6.8)

The annual consumption of a reagent given by

 	(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

 	(2.6.10)

with

 	(2.6.11)

where

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

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

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

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

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

Annual cash flow associated with byproduct generation disposal/sale is

 	(2.6.12)

with annual generation of a byproduct given by

 	(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,

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

 	(2.6.14)

The cost of electricity consumption is

 	(2.6.15)

where

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

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

Annual water consumption associated with control k is

 	(2.6.16)

The cost of water consumption is

 	(2.6.17)

where

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

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

 	(2.6.18)

Costs of Energy Efficiency Measures

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

Energy Efficiency Measures Related Constraints

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

Only one vintage of an energy efficiency measure on a unit is possible for the period starting when the vintage comes on line and ending with its technical life.

 	(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 vth vintage of measure ee online on unit i in year t, and

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

Energy efficiency measure capacity is given by the following equations.

For t = 1,

 					(2.7.2a)

For t > 1,

 	(2.7.2b)

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

 	(2.7.2c)

where

ee and eee are incompatible measures.

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

 	(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.

 	(2.7.5)

where

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

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

ts_ee(t,i,ee,v) is a binary variable that can bring vth 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 ith unit, and

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

 	(2.7.6)

where

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

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

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

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

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

 	(2.7.7)

The cost of electricity consumption is

 	(2.7.8)

where

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

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

Total Annual Cost

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

 	(2.7.9)

Policy Options

ISIS can help design and evaluate a number of emissions reduction policy options including cap-and- trade, emissions taxes, and emissions limits. Additionally, appropriate combinations of these options can also be evaluated. The policy options included in ISIS are described below.

Cap-and-Trade

Under this option, an emissions cap is set on the amount of a pollutant that can be emitted. Sources, companies, or other groups are issued emission permits (allowances) which represent the right to emit a specific amount of the pollutant. Allowances may be banked for use in future. The total amount of allowances available in the current period and those banked in previous periods cannot exceed the cap in the current period. Sources or companies that need to increase their emissions must buy allowances from those who pollute less. The transfer of allowances is referred to as a trade. In effect, the buyer is paying a charge for polluting, while the seller is being rewarded for having reduced emissions by more than was needed. Thus, in theory, those that can easily reduce emissions most cheaply will do so, achieving the pollution reduction at the lowest possible cost to society.

Generally, annual caps have been utilized in ongoing programs (e.g., Title IV SO2 reduction program). However, ISIS does permit evaluation of potential programs with caps over user-selected time periods (e.g., 5-yearly caps). This evaluation is accomplished using,

 	(2.8.1)

where

ec(t,j) is the emission cap for pollutant j in year t, and

bnk(t+period, j) are the allowances of the pollutant j banked in year t for the year t + period.

Cap-and-Trade with a Minimum Reduction Requirement

While designing a cap-and-trade program, there may be an interest in requiring a minimum level of emission reduction from each affected entity. Such a requirement may be able to help address any local emissions-related concerns. In ISIS, this requirement can be imposed using

 	(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

 	(2.8.3)

where

el(t,j) is the rate-based emission limit for pollutant j in year t and every affected unit complies with this limit.

Allowance Price Inputs

In some cases, there may be an interest in endogenously determining the level of emission reduction corresponding to a certain allowance price. This information may be useful, for example, in a situation where an allowance price is set for reducing emissions from many industrial sectors. In such a case the levels of emission reductions corresponding to the same allowance price may be different for the sectors under consideration. The emissions response to a given allowance price is driven by the following term in the objective function (see section 2.1),

 	(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.

Optimization and Post-Processing

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

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.

Input Data for ISIS-Cement Model

Data Requirements

Data requirements for ISIS include sector-specific (in this case, cement-specific) data as well as policy and economic parameters. The cement-specific data requirements are discussed below.

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.

Industry, Fuel, and Emissions

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 capacity (by state) and replacements (by kilns)].

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

Kiln Population	Number of Kilns

Existing  (2009)	168

PCA’s Projected New Kilns (2010 – 2012)	9

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

Average Variable Costs

In previous economic analyses, five input variables in cement production have been identified to determine the kiln-level AVC functions: raw materials, repair and maintenance, labor, electricity, and fuel (Depro, 2007, Depro, 2010, Depro and Lentz, 2010). Raw materials serve as the kiln feed, and repair and maintenance are required for periodic upkeep of the kiln. Labor is used in the quarry, in the operation of the kiln, and for packing. Electricity is consumed mainly by the auxiliary equipment and fuel is largely consumed in the kilns. The AVC for raw materials, labor, repair and maintenance, and electricity was determined following the methodology in the EPA regulatory impact analysis of the cement kiln dust rulemaking (EPA, 1998).

Cement Demand Centers

The U.S. cement markets are organized in state-specific demand centers. Figure 3-1 shows the distribution of Portland cement kilns operating in 2009. Each state containing at least one kiln is shaded. The ISIS-cement model simulates each cement plant’s ability to compete in each of the demand centers as a function of the plant’s production cost and transportation cost associated with supply to each demand center.

Portland Cement Demand

One of the key data inputs for the ISIS-cement model is the demand projection for each demand center. In general, this demand is a function of gross domestic product (GDP) growth, interest rates, special construction projects (e.g., highways), and public sector construction spending. Portland cement demand was 128 million metric tons in 2005. PCA expects cement demand will reach 192 million metric tons by 2035, which reflects an increase of nearly 64 million metric tons with a compound annual growth rate of 1.4%. Cement demand through year 2035 is reported in the PCA Long-Term Cement Consumption Outlook (PCA, 2009c). PCA projections of cement demand (in million metric tons) by state for 2005 through 2035, in 5-year increments, are shown in Table 3-2.

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

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

State Demand Center	2005	2010	2015	2020	2025	2030	2035

Alabama	1.92	1.45	2.02	2.16	2.32	2.49	2.67

Arizona	4.77	2.21	3.85	5.16	6.55	7.95	9.59

Arkansas	1.30	0.93	1.29	1.41	1.54	1.67	1.81

California	16.01	8.33	13.88	15.43	17.11	18.95	20.96

Colorado	2.55	1.74	2.76	3.08	3.47	3.90	4.33

Connecticut	0.82	0.54	0.77	0.83	0.88	0.94	0.98

Delaware	0.22	0.18	0.26	0.28	0.29	0.31	0.32

District of Columbia	0.21	0.15	0.18	0.18	0.17	0.18	0.18

Florida	12.28	4.77	8.99	11.05	13.47	16.33	19.67

Georgia	4.75	2.52	3.95	4.96	5.67	6.17	6.65

Hawaii	0.44	0.35	0.47	0.49	0.52	0.55	0.57

Idaho	0.70	0.44	0.70	0.82	0.92	1.03	1.15

Illinois	4.64	3.10	4.29	4.62	4.99	5.44	5.98

Indiana	2.27	1.69	2.30	2.49	2.73	2.98	3.25

Iowa	1.94	1.59	1.98	2.04	2.10	2.15	2.20

Kansas	1.55	1.36	1.65	1.71	1.78	1.85	1.91

Kentucky	1.60	1.06	1.48	1.59	1.71	1.84	1.97

Louisiana	2.23	2.44	2.91	3.15	3.29	3.42	3.52

Maine	0.35	0.20	0.29	0.31	0.32	0.34	0.35

Maryland	1.66	1.12	1.63	1.78	1.94	2.12	2.31

Massachusetts	1.26	0.81	1.11	1.21	1.31	1.43	1.56

Michigan	3.06	1.54	2.05	2.19	2.44	2.68	2.75

Minnesota	2.06	1.29	1.86	2.09	2.34	2.57	2.80

Mississippi	1.14	1.07	1.50	1.58	1.66	1.76	1.85

Missouri	2.87	2.03	2.71	3.18	3.58	3.90	4.29

Montana	0.38	0.30	0.42	0.45	0.48	0.51	0.53

Nebraska	1.37	1.14	1.46	1.55	1.66	1.76	1.87

Nevada	2.63	1.20	2.33	3.26	4.27	5.14	6.16

New Hampshire	0.37	0.23	0.33	0.36	0.39	0.42	0.45

New Jersey	2.06	1.45	1.89	1.99	2.09	2.18	2.28

New Mexico	0.91	0.65	0.94	1.00	1.07	1.12	1.15

New York	3.29	2.84	3.60	3.73	3.87	3.98	4.08

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

Transportation-Interregional Trade

In ISIS-cement, a transportation matrix is used to describe the costs for transporting cement from kiln and import district locations to demand centers. To develop these costs, information on distances between supply and demand points and costs of transportation modes (truck, rail, or water transport) was obtained. In particular, the Transportation Routing Analysis GIS (TRAGIS) model (TRAGIS, 2003) was used to develop the origin-destination distances. Also, in the matrix, the applicable lowest cost transportation option is used to connect a supply point with a destination. While the cement demand centers are interlinked through a transportation matrix, the competition is generally maintained on a regional level because the cost of transporting cement is relatively high.

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. Shipments 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, transportation costs for each mode of transportation (truck, rail, and barge) are calculated from each kiln and import district to each demand center. For a given origin-destination pair, the dominant mode (lowest cost of shipment) is used to determine the transportation cost for that route.  The TRAGIS model was used to arrive at the origin-destination distances and feasible routes for rail and barge; Google Maps was used for truck transportation. The shipment costs shown in Table 3-3 were used to determine the transportation costs for the routes in ISIS.

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

Mode	Journal of Transport

Geography Report, 2005	N&S American Cement, 2004	American

University, 1994	Averages Used in Model

	BTS1	WDOT2	NACT3

Truck	33.06	7.64	11.02	7.18	14.73

Rail	2.78	3.32	5.51	N/A	3.87

Barge	0.89	1.05	2.20	N/A	1.38

1	(Bureau of Transportation Statistics) General Bulk Transportation "…can only be considered the upper boundary of transport costs for each mode assuming each industry is operating at-cost or at a profit".

2	(Washington State Department of Transportation) Cost study to examine options for the transport of wheat (Jessup and Casavant, 1998).

3	(North American Cement Transportation) Report covers cement-specific transportation costs, assumes short tons--converted to metric.

Imports

U.S. cement markets receive imported quantities of cement and clinker from a number of countries, and these imports arrive at more than 30 import districts (USGS, 2009). In ISIS-cement, international supplies from exporting countries to U.S. import districts are 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 conducted to provide an estimate of international supply elasticity for supplies from these countries and the rest of the world. The results of this study (Burtraw, 2010) reflected that the best estimate of the international supply elasticity of cement and clinker from China, Thailand, Venezuela, South Korea, Greece, and the rest of the world into the U.S. is 3.94. This value indicates that if the price of cement were to increase by 1% 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	1.07

California, Los Angeles	3.05

California, San Diego	0.72

California, San Francisco	2.36

Florida, Miami	2.27

Florida, Tampa	3.48

Georgia, Savannah	0.08

Hawaii, Honolulu	0.43

Illinois, Chicago	0.01

Louisiana, New Orleans	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	0.13

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

Capacity Changes

Cement plants have a relatively long lifespan, typically 50 years or more (FLSmidth, 2007). Various factors, including (but not limited to) raw material availability in the quarry, technology changes, productivity, efficiency, longevity, reliability, maintenance, and long-term costs can affect the lifespan of a cement kiln/plant. In ISIS-cement, retirements from 2002 to 2009 and projected retirements after 2009 of existing kilns were based on information from PCA on capacity expansion estimates. These estimates were supplemented with information from individual cement companies on their plans for shut-downs, new construction, and kiln consolidation (PCA, 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 associated 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).

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. Coal is projected to remain the dominant fuel used by the U.S. cement industry. However, there has been an increasing trend towards using other fuels, particularly alternative fuels, such as coke, waste tires, and other wastes, especially oily wastes.

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

Source: EIA, 2008a

In the ISIS-cement model, to determine the fuel intensity of each kiln, correlations of kiln type to heat input and/or gas flow were developed (see Appendix A). Once determined, the kiln’s specific fuel intensity is used to calculate fuel cost for each kiln. PCA’s data on heat input to various kilns by type were used to develop kilns’ fuel intensities. The data, expressed in heat input per unit of clinker (specific fuel consumption [SFC]) and exhaust gas flow rate (wet) (EGFW) per unit of clinker, are summarized in Table 3-5.

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

Kiln Type	SFC	EGFW

	MMBtu/tona	Nm3/kg	SCF/tona

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 ton

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 the optimal fuel type(s) based on the regional cost of the chosen fuel and the kiln’s specific fuel intensity.

Emissions

The design of the ISIS Model can accommodate any number of pollutants of interest. In ISIS-cement, each kiln is characterized by its NOx, SO2, PM, hydrochloric acid (HCl), mercury (Hg), total hydrocarbon (THC), and CO2 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).

NOX emissions from cement kilns result primarily from the following combustion process: oxidation of fuel nitrogen (fuel NOX) and the oxidation of nitrogen in the combustion air (thermal NOX). Oxidation of nitrogen in the feed materials (feed NOX) can also influence total NOX emissions. Table 3-6 shows NOX emission intensities for cement kilns in lb/ton of clinker and in lb/MMBtu (EPA, 2007a).

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

Kiln Type	Heat Input,

MMBtu/ton of clinker	Uncontrolled NOx 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)

SO2 emissions from cement kilns are the product of sulfur in the fuel as well as sulfur in the feed materials. Sulfur in the fuel will oxidize to SO2 during pyroprocessing, and a significant amount is likely to be captured in the form of sulfates as the gas passes through the calcination zone. Compared to long dry and wet kilns, preheater and preheater/precalciner kilns tend to be more effective at capturing fuel-generated SO2. Accordingly, oxidation of sulfur in the feed materials is likely to be a major component of total SO2 emissions. Table 3-7 shows average SO2 emissions for each kiln type for each state (Andover Technology Partners, 2009a). State-specific emission intensities of SO2 were determined from emissions reported for the kilns in that state. For any state where the emission intensity was not available for a kiln-type, the national average emission intensity was assigned to kilns in that state.

CO2 emissions from cement kilns result from limestone calcination and fuel combustion. Calcination releases 0.52 tons of CO2 per ton of clinker produced, while fuel-based CO2 emission factors range from 199.52 lb CO2/MMBtu for coal to 105.02 lb CO2/MMBtu for natural gas (Andover Technology Partners, 2009b; Andover Technology Partners, 2010). Table 3-8 shows approximate CO2 and H2O produced from combustion for the most important fuels for cement kilns.

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

SO2 (lb/ton clinker)

State	Precalciner	Preheater	Dry	Wet

AL	0.09	0.61	9.02	13.99

AZ	1.30	0.07	8.51	7.81

AR	2.94	2.32	3.45	7.59

CA	0.33	1.12	0.81	0.00

CO	0.32	0.10	1.07	4.24

CT	1.15	2.32	9.02	7.81

DE	1.15	2.32	9.02	7.81

FL	0.45	0.02	9.02	7.81

GA	0.95	2.73	11.42	12.25

ID	0.13	2.32	9.02	0.70

IL	5.58	5.77	5.88	9.50

IN	1.15	2.32	9.02	7.81

IA	1.15	2.32	9.02	7.81

KS	1.25	5.88	24.85	8.61

KY	0.19	5.04	12.53	7.81

LA	1.15	2.32	9.03	7.81

ME	0.30	0.30	?	11.98

MD	3.14	3.95	7.31	7.49

MA	1.15	2.32	9.03	7.81

MI	5.23	2.32	5.31	18.00

MN	1.15	2.32	9.03	7.81

MS	0.09	0.61	9.02	13.99

MO	2.18	2.32	1.51	7.02

MT	0.13	2.32	9.02	0.70

NE	1.25	5.88	24.85	8.61

NV	0.49	0.05	1.51	7.81

NH	1.15	2.32	9.02	7.81

NJ	1.15	2.32	9.02	7.81

NM	1.30	0.07	8.51	7.81

NY	0.30	0.30	9.02	11.98

NC	1.15	2.32	9.02	7.81

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 CO2 and H2O Produced from Combustion of Fuels

Variable Name	Flue Gas	Tires	Petroleum Cokes	Heavy Fuel Oil	Rosemont PRB Logan, WV BIT	Natural Gas

LBMCO2MMBTU	lbmoles CO2/MMBtu	4.26	4.83	3.85	4.25	4.53	2.39

LBCO2MMBTU	lb CO2/MMBtu	187.44	212.56	169.32	186.83	199.52	105.02

LBMH2OMMBTU	lbmoles H2O/MMBtu	2.76	2.76	2.23	3.23	4.14	2.77

LBH2OMMBTU	lbs H20/MMBtu	49.71	49.73	40.26	58.21	74.64	49.92

PRB = Powder River Basin coal		WV BIT = West Virginia bituminous coal

Control Technologies and Emission Abatement Approaches

ISIS-cement contains information on abatement approaches for NOx, SO2, PM, HCl, Hg, THC, and CO2 emissions described above. The three categories of abatement approaches included are: process modifications and upgrades, raw material and/or fuel substitution, and mitigation technologies. For each emission abatement approach, where possible, information on the following parameters was developed (Andover Technology Partners, 2008) and included in the model: capital cost, fixed operating cost, variable operating cost, emission reduction performance for all of the pollutants, impacts on fuel and/or raw material use, impact on electricity consumption, byproduct generation and cost, and impact on water use.

To estimate capital recovery factors for capital costs associated with control technologies, economic life values of 15 and an interest rate of 7% are generally used, but different values can be selected by the user. Payback periods and technical life for the energy efficiency measures shown in Tables 3-14 through 3-17 are given in Worrell and Galitsky (2004). Economic life for each of these measures can be taken to be the average of the technical life and the payback period. Again, an interest rate of 7% can be used for capital recovery in the absence of more specific information.

Tables 3-9 through 3-12 show the NOX, SO2, CO2, HCl, Hg, THC emissions control technologies being used in the ISIS-cement model. Tables also reflect the impacts of these technologies on pollution reduction, electricity use, and water use. Multimedia impacts of changes to the capacity of cement kilns are listed in Table 3-13. Tables 3-14 through 3-17 show the electricity consumption and heat input changes accomplished as a result of implementation of energy efficiency measures for Raw Materials Preparation, Clinker Making, Finish Grinding, and Plant-Wide Measures, respectively.

Policy and Economic Parameters

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.

Additional Economic Parameters

In the ISIS framework, the following additional economic parameters are used: discount rate, escalation rates, and demand elasticity. For the ISIS-cement model, the default discount rate has been chosen as 7%, as recommended by the U.S. Office of Management and Budget (OMB) for project evaluation (OMB, 1992). Escalation rates used can be found in the “ISIS_Inputs.xls” workbook. Escalation rates for labor are based on historical data from Bureau of Labor and Statistics (BLS). Raw materials escalation factors are calculated from historical price data of crushed sandstone and gypsum from USGS. The escalation factors for variable operating and maintenance costs are based on Chemical Engineering Index. Escalation factors for various fuels and electricity are estimated based on data from the EIA’s Annual Energy Outlook 2008 (RTI, 2010). Demand for cement is relatively inelastic and an elasticity value of -0.88 is used in the model (EPA, 1998).

Table 3-9.	NOX Control Technologies for Cement Kilns

Control Type	Impact on Emissions,

±% change	Electricity Consumption,

kWh/ton of cement	Water Consumption,

gal/ton of cement

	NOX	SO2	PM	Hg	Other	Grinding	Kiln Operation

	Low NOX Burners –

Indirect Firing	-20% to -30%1	No impact2

03	-1.24	03

Mid Kiln

Firing-Tires	-20% to -40%1	May vary5

03	06	03

Low NOX Burner + Mid Kiln Firing- Tires	-20% to -40%1	May vary5

03	-1.24,6	03

Low NOX Burners + Tire Derived Fuel	-20% to -40%7	May vary5

03	-1.24,6	03

Low NOX Burner + Selective Non Catalytic Reduction	-50%2	No data5	No data8	No data8	No data8	0	-1.24,9	+1.259

Low NOX Burner + Selective Catalytic Reduction	-90%2	Oxidation10	No data8	Oxidation10	No data8	0	-1.24,9	+1.259

Low NOX Burners + CemStara	-30%11

	-1.3 (wet process);

-1.9 (dry process) 12	-1.24,12 from LNB and

-1.5 (wet process) or

-2.2 (dry process) from CemStar13	0

CemStar/Fly Ash Injection	-20%14

	-1.3 (wet process);

-1.9 (dry process) 13	-1.5 (wet process) or

-2.2 (dry process) from CemStar 13	0

Notes to Table 3-9

See EPA (2004), Table 5-1.

Andover Technology Partners 2009b.

These technologies do not use and do not affect the consumption of water in raw mix preparation or significantly affect electric power consumption in cement manufacturing processes.

Conversion from direct firing systems typical of wet and dry process kilns and older preheater kilns to indirect firing systems, required to implement low NOX burner (LNB) technology, could result in reductions in primary air fan and kiln-induced draft fan power requirements and concomitant slight increases in coal conveying power requirements. A reduction in fan/blower power on the order of 100 hp might be anticipated for a 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 anticipated if the kiln system is already indirect-fired.

SO2 emissions 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 SO2 emissions to the use of these NOX control methods.  With respect to use of an SO2 wet scrubber using ground limestone, it is assumed that, for wet process kiln systems, uncontrolled SO2 emissions of 8.2 lb per ton of clinker (8.9 lb/ton of cement) (EPA Pub. AP 42, 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% limestone slurry. 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 that uncontrolled SO2 emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement) (EPA, 1995: Table Table 11.6-8) are treated similarly (stoichiometric amount 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 a precalciner kiln, it is assumed that uncontrolled SO2 emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement) (EPA, 1995: Table 11.6-8) are treated similarly (stoichiometric amount 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.

Kiln system and raw materials grinding electric power consumption would not be significantly affected by introduction of tires or tire-derived fuel.

Combined effect will vary. Low NOx 20 to 40%, MKF 20 to 40%.

While there may be theoretical or limited experimental bases to assume increases or decreases in emissions of various pollutants in connection with NOX emission controls, statistics on such effects are not available. See, generally, EPA (2007), Chapter 11.

Assumes SNCR or SCR both stoichiometric additions of NH3 in 20-percent solution. Typical precalciner NOX emissions of 4.2 lb/ton clinker before treatment (EPA 1995, Table 11.6-8), and 0.92 tons of clinker are used in each ton of cement. It should also be noted that SNCR is not applicable to wet process, long dry process, and many preheater kilns because the kiln gas exit temperatures are too low from those units for the necessary reactions to take place. See EPA (2007a). SNCR is assumed to achieve 63% reduction in NOX emissions 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 in gas volume handled by the kiln’s fan system. The increase is not considered in the calculation.

Typically, up to about 1% of SO2 can be oxidized to SO3, Elemental form of Hg oxidized across SCR provided sufficient concentration of halogens in flue gas.

On a lb/ton of clinker basis.

Use of the CemStar process may have multiple effects on electric power consumption, including reduced raw mix preparation costs in the event that unground CemStar material is fed to the rotary kiln, and reduced fan power requirements resulting from reduced kiln gas volumes in connection with both combustion gases and raw mix calcination. Assuming a 5% replacement of raw mix with carbonate-free CemStar material, a concomitant 5% reduction in raw mix preparation energy is also assumed. A very approximate resulting 5% reduction in kiln electrical energy consumption is assumed based on a roughly 5% reduction in kiln exit gases from both calcination and combustion.

CemStar and fly ash injection are expected to have similar effects on raw mix preparation and kiln process electrical energy requirements.

See NESCAUM (2000) for effect of CemStar on NOX reductions from cement kilns. Investigation of the combined effects of multiple technologies on pollutant emissions was not carried out for this summary.

General Notes

A.	Cement kiln processes do not use steam.

B.	Cement kiln dust (CKD) disposal rates as functions of NOX or SO2 emissions control technology has not been reported. Typical CKD disposal rates range from 0.042 to 0.115 tons CKD/ton clinker (0.046 to 0.125 tons CKD/ton cement assuming 0.92 tons clinker per ton of cement).

C.	CKD disposal costs vary widely by region, CKD characteristics, CKD volumes, and location of disposal site (on-site or off-site). In addition, disposal costs are expected to be of comparable cost to transportation costs in connection with off-site disposal. For example, prices at three northern California landfills range from $26/short ton to $69/short ton of CKD plus transportation, which may 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 generally unaffected by the type of cement kiln pollution controls employed.

References for Table 3-9 (references called out in the above notes can be found at the end of the chapter)

Air Pollution Controls and Efficiency Improvement Measures for Cement Kilns, Prepared by ARCADIS Under Contract 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.	SO2 Control Technologies for Cement Kilns

Control Type	Impact on Emissions,

±% change	Electricity Consumption,

kWh/ton of cement	Water Consumption,

gal/ton of cement

	NOX	SO2	PM	Hg	Other	Grinding	Kiln Operation

	Wet Scrubber

-90% to -95%1

-80%	-99.9% for HCl	+0.2

(wet process kilns) 2	+52	+12 (wet process); +1.5 (dry process) 3

Dry Lime Injection

-50%4

	-75% for HCl	5	5	5

Notes to Table 3-10

Andover Technology Partners 2009b.

Electric power consumption in connection with wet scrubbing is assumed to be primarily a result of: 1) grinding of limestone to below 45 to 74 micrometers in particle size and pumping limestone slurry; and 2) increased kiln exhaust gas fan power requirements. The latter energy increase is caused by increased pressure drop demand on kiln ID fans due to conveying kiln exit gases across the scrubber spray tower plus increased gas volume demand 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 of limestone. 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.

SO2 emissions 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 SO2 emissions to the use of these NOX control methods.  With respect to use of an SO2 wet scrubber using ground limestone, it is assumed that, for wet process kiln systems, uncontrolled SO2 emissions 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 15.5 lb and 87.7 lb, respectively, per ton of cement produced. For a precalciner kiln, it is assumed that uncontrolled SO2 emissions of 1.1 lb per ton of clinker (1.2 lb/ton of cement) (EPA, 1995 Table 11.6-8) are treated similarly (stoichiometric amount 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.

20 percent at Ca:S stoichiometry of 2:1 to 5:1. Dry Sorbent Injection report.(ARCADIS, 1992)

Dry lime injection technology is not well developed for the cement industry and no statistics are available.

General Notes: See Table 3-9

References for Table 3-10 (references called out in the above notes can be found at the end of the chapter)

Andover Technology Partners, Cost and Performance of Controls, March 10, 2009.

Srivastava et al., ES&T, March 2006, pp.1385-1393.

Theoretical Approach for Enhanced Mass Transfer Effects in-Duct Flue Gas Desulfurization Processes. Final Report. 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.

Table 3-11.	CO2 Control Technologies for Cement Kilns

Control Type	Impact on Emissions,

+/- % change	Electricity Consumption,

MWh/ton of clinker	Process Water Consumption,

ton/ton of clinker	Cooling Water Consumption,

ton/ton of clinker

	NOX	SO2	PM	Hg	CO2	Other

	Solvent CC1 New	-99%	-99%

	-85%

-0.022	0.429	4.82

Solvent CC1 Retrofit	-99%	-99%

	-85%

-0.103	0.160	4.82

Oxy-combustion	-99%	-99%

	-85%

0.174

14.3

Notes to Table 3-11

CC = carbon capture

References for Table 3-11

Andover Technology Partners, Cost and Performance of Controls, March 10, 2009.

EPA, Report to Congress on Cement Kiln Dust, Chapter 3: CKD Generation and Characteristics; US Environmental Protection Agency: 1993.

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

Control Type1	Impact on Emissions,

± % change

	NOX	SO2	PM	Hg	Other

ACI2

	-99.9%	-90%	-50% for THC

Membrane Bag

	-99.9%

RTO3

	-98% for THC

Dry Lime Injection

-50%

	-75% for HCl

Wet Scrubber

-90% to

-95%

-80%

	Notes to Table 3-12

Feasible combinations of the above control technologies may be utilized as necessary.

ACI = activated carbon injection.

RTO = regenerative thermal oxidizer

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

Kiln Type	Water Consumption,

gal/ton of cement2	Electricity Consumption,

kWh/ton of cement	Waste

Grinding3	Kiln Operation4	Generation Rate, ton/ton of cement5	Disposal Cost,

$/ton of cement

Wet to Precalciner	-2142,6	-128	-78	-0.072	see notes 9,10

Long Dry to Precalciner	No change2	Not available7	Not available7	-0.062 see notes 9,10

Preheater to Precalciner	No change2	Not available7	Not available7	011 see notes 9,10

Notes to Table 3-13.

Unless specifically noted otherwise, impacts are presented on a per-short-ton-of-cement basis and generally make use of published data reported on a per-short-ton-of-clinker basis. These values are converted to a per-ton-of-cement basis by assuming that cement consists of 92-percent clinker. All weight units in this table are short tons.

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 with 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.

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.

Air pollution control device contributions to electric power consumption data on cement kiln process systems are assumed to include only existing particulate matter control devices and not scrubbers for SO2 or other criteria pollutants.

Solid waste from all process types is assumed to be cement kiln dust (CKD). Data used here are as reported in EPA (1993).

Cement is assumed to consist of 92-percent clinker. Clinker is assumed to require 1.42 tons of raw kiln feed (dry) per ton of clinker. Kiln feed loss on ignition is assumed to be 35 percent. Kiln feed slurry is assumed to contain 36-percent water.

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).

As stated in note 7, industry data on electric power use in raw materials preparation and kiln processing are not available for every dry process. Data are for all dry processes are combined.

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.

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.

Modern preheater cement kilns and precalciner cement kilns generate similar volumes of CKD. Older preheater cement kilns are similar in CKD generation to long dry process cement kilns.

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

Energy Efficiency Improvement Method	Electricity Consumption Change,

kWh/ton clinker

	Dry	Wet	Pre-

heater	Pre-

calciner

ETS  (Efficient Transport System)	-3.20

-3.20	-3.20

RMB (Raw Materials Blending)	-2.70

-2.70	-2.70

PCVM  (Process Control Vertical Mill)	-0.90

-0.90	-0.90

HERM (High Efficiency Roller Mill)	-11.05

-11.05	-11.05

SBH (Slurry Blending and Homogenization)

-0.35

WMCCC (Wash Mills with Closed Circuit Classifier)

-12.00

RMTHEC (High-Efficiency Classifiers)	-5.05	-5.05	-5.05	-5.05

Table 3-15.	Energy Efficiency Clinker Making Measures

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

	Dry	Wet	Pre-

heater	Pre-

calciner	Dry	Wet	Pre-

heater	Pre-

calciner

EMCS (Energy Management and Control System)	-1.90	-1.50	-1.90	-1.90 -0.15	-0.21	-0.15	-0.15

SR (Seal Replacement)

	-0.02	-0.02	-0.02	-0.02

CSI (Combustion System Improvement)

	-0.25	-0.35	-0.25	-0.25

IF (Indirect Firing)

	-0.16	-0.16	-0.16	-0.16

SHLR (Shell Heat Loss Reduction)

	-0.20	-0.20	-0.20	-0.20

OGR (Optimize Grate Cooler)	0.90

0.90	0.90	-0.09	-0.10	-0.09	-0.09

CGC (Convert to reciprocating grate cooler)	2.40	2.40	2.40	2.40	-0.23 -0.24	-0.23	-0.23

HRPG (Heat Recovery for Power Generation)	-18.0

	EMD (Efficient Mill Drives)	-2.00	-1.70	-2.00	-2.00

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”

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