Document ID: EPA-HQ-OAR-2009-0491-3828
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2010-10-28T04:00Z

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.



This page intentionally left blank.



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”

References for Chapter 3

American University (2008). TED Case Studies: Cemex Case.  HYPERLINK
"http://www.american.edu/TED/cemex.htm"
http://www.american.edu/TED/cemex.htm , accessed October 21, 2008.

Andover Technology Partners (2008). Cost and Performance of Controls,
memorandum to U.S. EPA, September 25, 2008.

Andover Technologies Partners (2009a). Memorandum: NOX, SO2 and CO2
Emissions from Cement Kilns (Emissions Memo), from Jim Staudt to Ravi
Srivastava, Samudra Vijay, Elineth Torres.  Dated March 10, 2009 (see
Appendix A of this document).

Andover Technology Partners (2009b). Memorandum: Costs and Performance
Controls, from Jim Staudt to Ravi Srivastava, Samudra Vijay, Elineth
Torres.  Dated March 10, 2009 (see Appendix A of this document).

Andover Technologies Partners (2010). Memorandum: Wet Scrubber Cost
Algorithms, from Jim Staudt to Ravi Srivastava, Elineth Torres, Keith
Barnett.  Dated February 26, 2010 (see Appendix A of this document).

ARCADIS (1992). Theoretical Approach for Enhanced Mass Transfer Effects
in-Duct Flue Gas Desulfurization Processes. Final Report. ARCADIS, 1992.

ARCADIS (2009). Personal Comm. with E. Leamer of ARCADIS, 26 February,
2009.

Burtraw, D. (2010). Supply Elasticity Estimation, memorandum to Ravi
Srivastava, U.S. EPA, March 10, 2010.

Depro, B.M. (2007). RTI International. “Documentation for Portland
Cement Kiln Cost Functions (2005)”, Memorandum to Keith Barnett, US
EPA, August 31, 2007. (See Appendix A of this document).

Depro, Brooks. (2010). RTI International. “ISIS Cement Production
Costs”, Memorandum to Elineth Torres, US EPA, March 31, 2010. (See
Appendix A of this document).

Depro, Brooks and Lentz, Anthony. (2010). RTI International. “ISIS
Cement Production Costs”, Excel Spreadsheet Attachment to Memo of Same
Name from Brooks Depro (2010), February 16, 2010.

BLS (2008). Bureau of Labor Statistics, Databases, Tables & Calculators
by Subject, Major Sector Productivity and Costs Index. Series ID:
PRS30006112, Sector: Manufacturing.  HYPERLINK
"http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_nu
mbers&series_id=PRS30006112"
http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_num
bers&series_id=PRS30006112 , accessed November 24, 2008.

EIA (2008) Annual Energy Outlook 2008, DOE/EIA-0383(2008), June 2008.  
HYPERLINK "http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2008).pdf" 
http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2008).pdf , accessed October
21, 2008.

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

EPA (1995). Compilation of Air Pollution Emission Factors - Volume 1:
Stationary Point and Area Sources. Fifth Edition, Supplements A-F,
AP-42. U.S. Environmental Protection Agency, Research Triangle Park,
North Carolina.

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 (2004). Alternative Control Techniques Document - NOX Emissions from
Cement Manufacturing, dated March 1994 (EPA-453/R-94-004)

EPA (2005). EPA 2002 National Emission Inventory v3.  HYPERLINK
"http://www.epa.gov/ttn/chief/net/2002inventory.html"
http://www.epa.gov/ttn/chief/net/2002inventory.html , accessed October
21, 2008 

EPA (2006). EPA 2005 National Emissions Inventory Version 2.  HYPERLINK
"http://www.epa.gov/ttn/chief/net/2005inventory.html"
http://www.epa.gov/ttn/chief/net/2005inventory.html  accessed November
17, 2008

EPA (2007). November 2007. Alternative Control Techniques Document
Update - NOX Emissions from New Cement Kilns. EPA-453/R-07-006. Research
Triangle Park, NC. US Environmental Protection Agency.

EPA (2010). August 6, 2010. "Summary of Environmental and Cost Impacts
for Final Portland Cement NESHAP and NSPS" Memorandum prepared for Keith
Barnett by RTI. US Environmental Protection Agency.

FLSmidth & Co. A/S (2007). Q2 Report 2007. August 2007  HYPERLINK
"http://hugin.info/2106/R/1148414/219358.pdf"
http://hugin.info/2106/R/1148414/219358.pdf , accessed October 21, 2008.

NESCAUM (2000).  Status Report on NOX Controls for Gas Turbines, Cement
Kilns, Industrial Boilers, Internal Combustion Engines: Technologies &
Cost Effectiveness. Northeast States for Coordinated Air Use Management,
December 2000. Accessed at  HYPERLINK
"http://www.nescaum.org/documents/nox-2000.pdf/"
http://www.nescaum.org/documents/nox-2000.pdf/ 

OMB (1992). Guidelines and Discount Rates for Benefit-Cost Analysis of
Federal Programs. OMB Circular No.A-94 (Revised). U.S. Office of
Management and Budget, October 29, 1992.  HYPERLINK
"http://www.whitehouse.gov/omb/circulars/a094/a094.html" \l "8"
http://www.whitehouse.gov/omb/circulars/a094/a094.html#8 , accessed
October 21, 2008.

PCA (2004). Innovations in Portland Cement Manufacturing. Portland
Cement Association. Edited by J. I. Bhatty, F. M. Miller, and S. H.
Kosmatka. 2004.

PCA (2004a). U.S. and Canadian Portland Cement Industry: Plant
Information Summary of December 31, 2004.

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

PCA (2008). Forecast Report: Long-Term Cement Consumption Outlook. By Ed
Sullivan January 31, 2008.  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 Capacity Report. Flash Report.  Portland Cement
Association’s Economic Research Department.  Updated October 19, 2009.

PCA (2009b). Forecast Report: Long-Term Cement Consumption Outlook. By
Ed Sullivan. October 28, 2009. Portland Cement Association. 

Ryan, S. (2006). The Costs of Environmental Regulation in a Concentrated
Industry. Department of Economics, MIT, Cambridge, MA, October 5, 2006. 
HYPERLINK "http://econ-www.mit.edu/files/1166"
http://econ-www.mit.edu/files/1166 , accessed October 21, 2008.

RTI (2010). ISIS Input Price Escalation Factors, Excel Spreadsheet.

TRAGIS (2003). Transportation Routing Analysis Geographic Information
System (TRAGIS) User’s Manual, Revision 0, Oak Ridge National
Laboratory, June 2003.   HYPERLINK
"https://tragis.ornl.gov/TRAGISmanual.pdf" 
https://tragis.ornl.gov/TRAGISmanual.pdf .

USGS (2005). Background Facts and Issues Concerning Cement and Cement
Data. U.S. Geological Survey, Open-File Report 2005-1152.  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). 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 October 21, 2008

USGS (2007b). Mineral Commodity Summaries: Cement, U.S. Geological
Survey, pp. 40-41, January 2007.  HYPERLINK
"http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/cemenmcs07.p
df"
http://minerals.er.usgs.gov/minerals/pubs/commodity/cement/cemenmcs07.pd
f , accessed October 21, 2008.

USGS (2009). 2005 Minerals Yearbook: Cement. U.S. Geological Survey
(Table 17), pp. 16.21.   HYPERLINK
"http://minerals.usgs.gov/minerals/pubs/commodity/cement/myb1-2007-cemen
.pdf" 
http://minerals.usgs.gov/minerals/pubs/commodity/cement/myb1-2007-cemen.
pdf .

Worrell, E.; Galitsky, C. (2004). Energy Efficiency Improvement
Opportunities for Cement Making; Lawrence Berkeley National Laboratory,
LBNL-54036, Jan 2004. An ENERGY STAR® Guide for Energy and Plant
Managers,
http://www.osti.gov/energycitations/servlets/purl/821915-Re2kcK/native/.

Yates, J.R. et al. (2003). CemStar Process and Technology for Lowering
Greenhouse Gases and Other Emissions while Increasing Cement Production.
 HYPERLINK
"http://www.hatch.ca/Environment_Community/Sustainable_Development/Proje
cts/Copy%20of%20CemStar-Process-final4-30-03.pdf"
http://www.hatch.ca/Environment_Community/Sustainable_Development/Projec
ts/Copy%20of%20CemStar-Process-final4-30-03.pdf   accessed February 27,
2009.



Model Calibration

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

Calibration Methodology

Calibration methodology utilizes the concept of calibration constant.
The calibration constant has been developed to account for possible
errors in costs. The value of calibration constant, calconst(i), is set
by trial and error during calibration.  The objective of the trial and
error approach is to minimize the absolute difference in the reported
and model-predicted prices (which are marginal values of the supply
equation) for each USGS district.

In the first step of calibration, the model is set to run for 2005-2007
by making appropriate changes in the input “Policy” worksheet, and
GAMS input files. The import quantities are then adjusted equal to
reported import quantity for each of the import district, except for
those of Mexico and Canada. 

In the next step, impact of changing the calibration constant is
monitored. This impact of the calibration constant is assessed on
estimated production quantities to keep the difference between reported
and model predicted production values within reasonable limits. The
calibration constant modifies each kiln’s variable cost of production.
“Calibration” worksheet within the Inputs workbook has values of
calibration constant assigned for each USGS district. Finally, in the
input GAMS file, the values are assigned for a given USGS district to
each of the kilns located in that USGS district.

The model is first calibrated for year 2005, to obtain values of the
calibration parameter calconst(i) for the year. Next, calconst(i) values
for 2005 are used as starting point and obtain values for the same
parameter for 2006. Similarly, the process is repeated to obtain the
values for 2007. Then, an average of the calconst(i) values over the
three years is taken and used for the future model runs. Current values
of the parameter Calconst(i) being used in the model runs can be found
in the worksheet “Calibration” of the “ISIS_Inputs.xls”
workbook.

Calibration is a dynamic process, and it is recommended that model
calibration be performed periodically. In this fashion, any available
new production, imports, or price data could be utilized in t he model..

Data for Calibration

Production quantities in various USGS production districts, import
levels in the import districts and reported cement prices for USGS
districts are the key quantities used for calibration of the model.
Reported data for years 2005, 2006 and 2007 were used to calibrate the
model and obtain values of appropriate calibration parameters. 

Cement Prices in USGS Districts

Reported cement prices in various USGS districts are shown in Table 4-1
for years 2005 through 2007. For a given year, the reported cement price
shows significant variation across USGS districts. For most districts
the cement price has increased over the three year period. USGS
districts assignation is the location of the reporting production
facilities.

Cement Production in USGS Districts

Reported cement production in various USGS districts is shown in Table
4-2 for years 2005 through 2007. For a given year, the reported cement
production shows significant variation across USGS districts. Generally
production dropped from 2005 to 2007, due to a decrease in demand
resulting from the economic downturn.

Cement Imports by Import Districts

Reported cement import quantity in various import districts is shown in
Table 4-3 for years 2005 through 2007. For a given year, the reported
cement imports show a significant variation across import districts.
Generally imports dropped from 2005 to 2007, due to a decrease in demand
resulting from the economic downturn. New Orleans (LA), San Francisco
(CA) and Tampa (FL) suffered large drops in the quantities of cement
imported in 2007 compared to that in 2005.

Results of Calibration

Reported and calculated values of cement prices by USGS district are
shown for years 2005, 2006, and 2007 in Table 4-4, Table 4-5, and Table
4-6, respectively. As can be seen from these tables, in most of the
districts reported and calculated cement prices are within 10% range,
whereas in a few they are higher. It should be noted that in general the
price differentials are smaller in the year 2005 and have increased in
2007. In some USGS districts the reported and calculated prices are
different than the model predicted prices due to several factors,
including error in reported prices, discontinuities in the
transportation matrix, and/or unique factors for some specific markets. 

Specifically, the Arizona and New Mexico USGS district has a price
differential due to capacity constraints in New Mexico. New Mexico has
only two small, very old kilns with a capacity half than the demand in
New Mexico. The balance demand is met by kilns in Texas, and imports,
which incur a significant transportation cost, resulting in a high price
for cement in this market. Similarly, Oregon and Washington USGS
District is dependent on supplies from other states. While demand in
Washington is met by domestic production, and imports from Canada,
Oregon, relies on imports from Canada, and other nearby states,
resulting in higher cost.

Generally, individual market prices are within the criteria specified in
the QA document, the aberrations are explained by the demand-supply gap
and transportation cost. Moreover, root-mean-square values of the price
difference for 2005 and 2006 are about 12% and 10% respectively, and 18%
for 2007.

Further while calibrating for price, aggregate production level is also
tracked to make sure that it is within reasonable limits. The reported
and predicted aggregate production levels for 2005-2007 are shown in
Table 4-7. 

The current set of calibration constant values are averaged over the
years of calibration, and are available in the “Calibration”
worksheet in the “ISIS_Inputs.xls” workbook.

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

USGS District	2005	2006	2007

Alabama	74.61	81.74	87.09

Illinois	80.29	89.81	91.17

Indiana	72.03	80.04	79.99

Kansas	76.58	86.50	92.87

Maryland	74.76	81.19	80.18

Missouri	78.92	86.61	89.36

Ohio	82.08	90.24	91.47

South Carolina	68.57	80.46	87.54

Maine and New York	80.74	92.53	96.62

Pennsylvania	79.73	89.99	90.85

Michigan and Wisconsin	79.83	84.82	90.12

Iowa, Nebraska and South Dakota	78.72	89.83	93.08

Florida	77.56	90.44	95.55

Georgia, Virginia and West Virginia	84.49	95.93	93.60

Kentucky, Mississippi and Tennessee	84.37	89.81	88.90

Arkansas and Oklahoma	89.00	96.99	97.74

Texas	74.72	85.10	88.33

Arizona and New Mexico	75.76	84.16	86.64

Colorado and Wyoming	82.24	92.90	92.73

Idaho, Montana, Nevada and Utah	83.61	90.26	95.25

California	88.20	99.09	100.24

Oregon and Washington	79.38	90.72	90.72

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

USGS District	2005	2006	2007

Alabama	5,647,141	5,733,121	5,578,650

Illinois	3,568,182	3,425,984	3,434,254

Indiana	3,370,868	3,334,492	3,285,999

Kansas	3,182,373	3,310,241	3,039,164

Maryland	2,813,098	2,922,227	3,305,160

Missouri	5,877,524	5,776,111	5,763,479

Ohio	1,086,879	1,064,833	1,010,077

South Carolina	3,601,251	3,654,162	4,057,414

Maine and New York	3,572,591	3,699,357	3,471,388

Pennsylvania	6,931,334	6,631,505	6,239,551

Michigan and Wisconsin	6,171,841	5,993,267	6,047,588

Iowa, Nebraska and South Dakota	4,962,606	5,024,335	4,889,607

Florida	6,311,835	6,477,181	6,076,137

Georgia, Virginia and West Virginia	2,612,478	2,696,253	2,528,648

Kentucky, Mississippi and Tennessee	3,649,753	3,849,271	3,770,250

Arkansas and Oklahoma	3,097,495	2,979,547	2,879,982

Texas	12,737,207	12,510,131	12,038,919

Arizona and New Mexico	3,073,244	2,809,792	2,902,131

Colorado and Wyoming	2,918,920	2,842,861	2,797,240

Idaho, Montana, Nevada and Utah	3,400,630	3,354,333	3,308,645

California	12,747,128	12,069,207	11,941,198

Oregon and Washington	2,175,963	2,101,005	2,103,481

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

Import District	2005	2006	2007

Baltimore, MD	146,300	203,500	18,700

Boston, MA	145,200	50,600	3,300

Buffalo, NY	6,600	4,400	0

Charleston, SC	1,212,200	1,100,000	363,000

Chicago, IL	1,100	1,100	0

Cleveland, OH	0	1,100	0

Columbia-Snake, ID-OR-WA	831,600	1,115,400	1,180,300

Detroit, MI	59,400	0	0

Duluth, MN	0	0	0

El Paso, TX	0	0	0

Great Falls, MT	0	0	0

Houston-Galveston, TX	2,880,900	3,705,900	3,641,000

Laredo, TX	0	0	0

Los Angeles, CA	3,357,200	3,759,800	2,029,500

Miami, FL	2,395,800	2,311,100	980,100

Minneapolis, MN	0	0	0

Mobile, AL	565,400	573,100	0

New Orleans, LA	4,503,400	5,090,800	1,171,500

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

Nogales, AZ	0	0	0

Norfolk, VA	767,800	793,100	447,700

Ogdensburg, NY	0	0	0

Pembina, ND	0	0	0

Philadelphia, PA	544,500	665,500	342,100

Portland, ME	0	0	0

Providence, RI	814,000	680,900	509,300

San Diego, CA	620,400	708,400	427,900

San Francisco, CA	2,599,300	3,081,100	1,524,600

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

Seattle, WA	368,500	733,700	644,600

St. Albans, VT	0	0	0

St. Louis, MO	0	0	3,300

Tampa, FL	3,825,800	3,791,700	1,523,500

Wilmington, NC	427,900	416,900	284,900

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

%Δ

Alabama	74.61	78	5

Illinois	80.29	83	3

Indiana	72.03	84	16

Kansas	76.58	81	6

Maryland	74.76	74	-1

Missouri	78.92	82	4

Ohio	82.08	83	1

South Carolina	68.57	76	10

Maine and New York	80.74	73	-9

Pennsylvania	79.73	77	-3

Michigan and Wisconsin	79.83	81	2

Iowa, Nebraska and South Dakota	78.72	81	3

Florida	77.56	75	-3

Georgia, Virginia and West Virginia	84.49	80	-6

Kentucky, Mississippi and Tennessee	84.37	81	-4

Arkansas and Oklahoma	89.00	87	-2

Texas	74.72	78	4

Arizona and New Mexico	75.76	105	39

Colorado and Wyoming	82.24	98	20

Idaho, Montana, Nevada and Utah	83.61	70	-16

California	88.20	85	-4

Oregon and Washington	79.38	69	-13

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

USGS District	Reported	Calculated	%Δ

Alabama	81.74	85	4

Illinois	89.81	83	-8

Indiana	80.04	85	6

Kansas	86.50	83	-4

Maryland	81.19	80	-1

Missouri	86.61	81	-6

Ohio	90.24	83	-8

South Carolina	80.46	85	6

Maine and New York	92.53	78	-16

Pennsylvania	89.99	80	-11

Michigan and Wisconsin	84.82	80	-6

Iowa, Nebraska and South Dakota	89.83	81	-10

Florida	90.44	82	-10

Georgia, Virginia and West Virginia	95.93	86	-10

Kentucky, Mississippi and Tennessee	89.81	84	-6

Arkansas and Oklahoma	96.99	91	-6

Texas	85.10	81	-4

Arizona and New Mexico	84.16	105	25

Colorado and Wyoming	92.90	100	8

Idaho, Montana, Nevada and Utah	90.26	83	-8

California	99.09	85	-14

Oregon and Washington	90.72	74	-18

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

USGS District	Reported	Calculated	%Δ

Alabama	87.09	73	-17

Illinois	91.17	72	-21

Indiana	79.99	75	-7

Kansas	92.87	71	-24

Maryland	80.18	74	-8

Missouri	89.36	71	-21

Ohio	91.47	76	-17

South Carolina	87.54	69	-21

Maine and New York	96.62	74	-24

Pennsylvania	90.85	75	-17

Michigan and Wisconsin	90.12	76	-15

Iowa, Nebraska and South Dakota	93.08	71	-23

Florida	95.55	66	-31

Georgia, Virginia and West Virginia	93.60	74	-20

Kentucky, Mississippi and Tennessee	88.90	71	-20

Arkansas and Oklahoma	97.74	88	-10

Texas	88.33	80	-10

Arizona and New Mexico	86.64	101	17

Colorado and Wyoming	92.73	89	-4

Idaho, Montana, Nevada and Utah	95.25	83	-13

California	100.24	81	-20

Oregon and Washington	90.72	75	-18

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

Year	Reported Production

(million short tons)	Calculated Production

(million short tons)	%Δ

2005	111.84	112.54	0.63

2006	110.30	111.23	0.84

2007	108.17	101.95	-5.75

 

Conclusions

If any of the key input parameters, specifically those relating to
production quantities and costs, are refined or otherwise modified or
additional observed data becomes available, the calibration of the model
should be repeated. Transportation matrix, modes, and cost of
transportation also have significant impact on the behavior of
production distribution across the districts and production prices.
Therefore, if any further modifications or refinements are made to the
transportation matrix, the model needs to be re-calibrated.

At the time of calibration of the current version of the model,
production, import and price values for only 2005 to 2007 were
available. As discussed above, when the new values become available, the
model needs to be calibrated again. Calibration of the model should be
repeated as soon as new information or new observed data become
available. Due to practical limitations it is recommended that the
calibration of the model be repeated every two years. Further, since the
calibration data is available only for three years, equal weight was
given to the parameters obtained for each year. Once larger data-set is
available, a modified weighing system can be adopted to give highest
weight to the most-recent year data.

 

References for Chapter 4

United States Geological Service. Mineral Yearbook 2008. Table 5. 
HYPERLINK "http://minerals.usgs.gov/minerals/pubs/commodity/cement/"
http://minerals.usgs.gov/minerals/pubs/commodity/cement/ 

United States Geological Service. Mineral Yearbook 2008. Table 3. 
HYPERLINK "http://minerals.usgs.gov/minerals/pubs/commodity/cement/"
http://minerals.usgs.gov/minerals/pubs/commodity/cement/ 

United States Geological Service. Mineral Yearbook 2008. Table 11. 
HYPERLINK "http://minerals.usgs.gov/minerals/pubs/commodity/cement/"
http://minerals.usgs.gov/minerals/pubs/commodity/cement/ 



Analysis and Results of the Portland Cement NESHAP and NSPS

Affected Sources

EPA is finalizing amendments to the NESHAP for the Portland cement
manufacturing industry and the NSPS for Portland cement plants.  The
final amendments to the NESHAP add or revise, as applicable, emission
limits for mercury (Hg), total hydrocarbons (THC), and particulate
matter (PM) from non-hazardous waste (nhw) kilns located at major or
area sources, and hydrochloric acid (HCl) from nhw kilns located at
major sources.  The amendments to the NSPS add or revise, as applicable,
emission limits for PM, NOx and SO2 for facilities that commence
construction, modification, or reconstruction after June 16, 2008.  

ISIS-cement was used to conduct an integrated engineering and economic
analysis for the final Portland Cement NESHAP and final NSPS.  Figure
5-1 shows the cement facilities and the kilns modeled in ISIS for the
final NESHAP and final NSPS analysis. Even though the Portland cement
NESHAP affects only nhw kilns, in ISIS-cement all cement kilns projected
to be in the U.S. in 2013 are modeled to meet the cement demand. 

Figure 5-1 	Portland Cement Facilities and Kilns Modeled in ISIS-Cement

From the 108 Portland cement facilities modeled in ISIS-cement for the
final NESHAP and final NSPS analysis, 97 facilities or 157 nhw kilns are
projected to be subject to the final NESHAP.  From these 157 nhw kilns
projected to be subject to the NESHAP, 16 kilns are projected to be
subject to the limits for new sources while 137 kilns are projected to
be subject to the limits for existing sources. Seven facilities or 7
kilns are projected to be subject to the final NSPS.  

Emission Limits

Table 5-1 shows the emission limits for the final NESHAP analyzed in
ISIS, while Table 5-2 shows the emission limits for the final NSPS.
Emission limits in tables 5-1 and 5-2 are average annual emissions. 
EPA’s analysis using ISIS-cement focused on the individual rules and
also on the combination of the final NESHAP and the final NSPS. The
limits used in the analysis were input as lbs/ton cement produced
annually.

Table 5-1.  Final NESHAP Emission Limits

NESHAP Limits for ISIS'  Runs

Pollutant	Existing	New	Units

	Average	Average

	Hg	31.7	14	lb/MM ton clinker

	0.0000317	0.000014	lb/ton clinker

	0.000029164	0.00001288	lb/ton cement

THC	5	5	ppmv

	0.050856164	0.050856164	lb THC/ton clinker

	0.046787671	0.046787671	lb/ton cement

HCl	0.4	0.4	ppmv

	0.003378699	0.003378699	lb HCl/ton clinker

	0.003108403	0.003108403	lb/ton cement

PM	0.012	0.005	lb/ton clinker

	0.01104	0.0046	lb/ton cement

Table 5-2.  Final NSPS Emission Limits

NSPS Limits for ISIS' Runs

Pollutant	New	Units

	Limit 	Average

	NOx	1.5	1.5	lb/ton clinker

	1.38	1.38	lb/ton cement

SO2	0.4	0.4	lb/ton clinker

	0.368	0.368	lb/ton cement

Control Technologies

Kilns subject to the final NESHAP and/or final NSPS are expected to add
one or more control devices to comply with the final emission limits.  
Table 5-3 shows control technologies and control efficiencies used in
the final NESHAP and final NSPS analysis. The cost of these control
technologies can be found in the "Summary of Environmental and Cost
Impacts for Final Portland Cement NESHAP and NSPS".  August 6, 2010. 
The final NESHAP and final NSPS rules also include additional testing
and monitoring requirements for affected sources.  In the final NESHAP,
in addition to meeting the emission limits, each affected kiln will be
required to install Continuous Emission Monitoring Systems (CEMS) to
monitor Hg, THC and HCl, while bag leak detector (BLD) will be required
to monitor performance of all baghouses (fabric filters). In the final
NSPS, in addition to meeting the emission limits, each kiln affected
will be required to install CEMS for NOx and SO2.  The cost of these
devices can also be found in "Summary of Environmental and Cost Impacts
for Final Portland Cement NESHAP and NSPS", August 6, 2010.

Table 5-3.  Control Technology and Control Efficiency Matrix by
Pollutant

Control Technologies	Pollutants

	Hg	THC	HCl	PM	SO2	NOx

Limestone Wet Scrubbers (LWS)	80%

99%

90%

	Activated Carbon Injection (ACI) with polishing baghouse	90%	80%

99.9%

Regenerative Thermal Oxidizer (RTO)

98%

Dry Lime Injection (DLI)

	75%

70%

	Low NOx Burner (LNB) +  Selective Non-Catalytic Reduction (SNCR) 

65%

Baghouse with Membrane Bags (BHMB)

99.9%

Membrane Bags Retrofit (MBR)

99.9%

ACI + LWS	90%	80%	99%	99.9%	90%

	LWS + RTO	80%	98%	99%

90%

	

ISIS Runs and Results	

EPA’s analysis focuses on the results of the final NESHAP and final
NSPS. We also present additional information on different combinations
of the regulatory programs to help stakeholders better understand the
size and scope of each. The three policies analyzed are:

Final NSPS only,

Final NESHAP only, and

Final NSPS and NESHAP.

Because all existing sources under the NESHAP will be required to come
into compliance in 2013, the analysis presented here reflects the impact
of the final NESHAP and final NSPS in 2013.

As explained in Chapter 3, one of the key data inputs for the
ISIS-cement model is the demand forecast for each demand center. For
cement, the demand is a function of gross domestic product (GDP) growth,
interest rates, special construction projects (e.g., highways), and
public sector construction spending. In 2005, Portland cement demand
reached a record high of 128 million metric tons. The economic distress
in the US during 2008 and 2009 resulted in a decline in cement demand. 
The demand for cement compared with 2005 levels declined from nearly 89%
in 2007 to less than 60% in 2009 (PCA, 2009).  Per PCA, cement demand is
not expected to reach past cyclical peak levels (2005) until 2015 (PCA,
2010).  Since multiple years were not modeled, the average demand for
the years 2013 through 2018 of PCA’s 2009 Long-Term Cement Consumption
Outlook (PCA, 2009) was used as the exogenous demand in 2013.  This
average demand is expected to be more representative of what will be
typical after implementation of the regulation.  

On the domestic supply side,  ISIS uses the industry data for all the
kilns operating in 2009 (see Figure 5-1) and optimizes the industry in
2013 taking into account retirements, replacements, and new kilns
projected by PCA from 2010 to 2013.  For the policy scenarios analyzed
here, ISIS simultaneously estimated 1) optimal industry operation to
meet demand and emission reduction requirements; 2) the suite of control
technologies needed to meet the emission limits; 3) the engineering cost
of controls; and 4) the economic impacts of the policies.

In the analysis shown below, EPA assumed that all the projected new
kilns shown in Figure 5-1 will be active in 2013. To accommodate for
this assumption, in the NSPS policy run, ISIS required the 7 new kilns
projected to be subject to the final NSPS to run in both the reference
and policy case.  In the NSPS policy case these 7 kilns were required to
meet the NSPS limits by installing controls.  In the NESHAP and the
combination of NSPS and NESHAP, ISIS required the 16 new kilns projected
to be subject to the NESHAP limits for new sources to run in both the
reference and policy case.  In the NESHAP policy case these 16 kilns
were required to meet the NESHAP limits for new sources by installing
controls.  On the other hand, in the NSPS and NESHAP combination the 16
kilns projected to be subject to the NESHAP limits for new sources as
well as the 7 kilns projected to be subject to the NSPS were required to
meet both the NSPS and NESHAP limits for new sources by installing
controls.

ISIS Results 

Cement Demand

As mentioned in Chapter 3, in ISIS-cement the U.S. cement industry is
organized in state-specific demand centers.  The model simulates each
cement kiln’s ability to compete in each of the demand centers as a
function of the kiln’s production cost and transportation cost
associated with supplying to each demand center.  At the same time the
cement demand at each demand center can be met by local production,
foreign imports, or by inter-market trading (i.e., shipping from other
demand center).  

The exogenous cement demand used in ISIS for this analysis was the
average demand for 2013 through 2018 or 123.6 million metric tons of
cement.  Table 5-4 shows the impact of the two policies analyzed on the
cement demand in 2013.  As shown in Table 5-4, in 2013 the US cement
demand could drop 0.65% under the NSPS, 5.70% under the NESHAP, and 6.27
under the combination of the NSPS and NESHAP.  

Table 5-4 Policy Impact on U.S. Cement Demand in 2013

Cement Demand (metric tons of cement)

Policy Run	Reference Case	Policy Case	Change in Demand	% Change

NSPS 	123,653,270	122,844,362	-808,907	-0.65

NESHAP 	123,653,270	116,608,341	-7,044,929	-5.70

NSPS and NESHAP	123,653,270	115,894,658	7,758,612	-6.27

Cement Production

Table 5-5 shows the impact of the policies analyzed on the US cement
production in 2013.    As shown in Table 5-5, U.S. cement production
could drop 1.4% under the NSPS, 9.60% under the NESHAP, and 10.62% under
the combination of NSPS and NESHAP. 

Table 5-5 Policy Impact on U.S. Cement Production

Cement Production (metric tons of cement)

Policy Run	Reference Case	Policy Case	Change in Production	% Change

NSPS 	94,036,939	92,680,715	-1,356,224	-1.4

NESHAP 	94,141,619	85,101,425	-9,044,929	-9.60

NSPS and NESHAP	94,141,619	84,145,488	-9,996,131	-10.62

Cement Imports

Table 5-6 shows the impact of the policies analyzed on US cement imports
in 2013.  As shown in Table 5-6, U.S. cement imports could rise 1.84%
under the NSPS, 6.76% under the NESHAP, and 7.58 under the combination
of NSPS and NESHAP.

Table 5-6 Policy Impact on U.S. Cement Imports

Cement Imports (metric tons of cement)

Policy Run	Reference Case	Policy Case	Change in Imports	% Change

NSPS 	29,616,331	30,163,647	547,316	1.84

NESHAP 	29,511,650	31,506,916	1,995,266	6.76

NSPS and NESHAP	29,511,650	31,749,170	2,237,520	7.58

Cement Prices

Table 5-7 shows the estimated average national price in 2013 for
Portland cement under the policies analyzed.  As shown in Table 5-7,
cement prices may increase 0.74% under the NSPS, 6.83% under the NESHAP,
and 7.58% under the combination of NSPS and NESHAP. 

Table 5-7 Policy Impact on U.S. Cement Prices

Cement Price ($/metric ton of cement)

Policy Run	Reference Case	Policy Case	Change in Price	% Change

NSPS 	87.77	88.42	0.65	0.74

NESHAP 	84.81	90.60	5.79	6.83

NSPS and NESHAP	84.81	91.24	6.42	7.58

Control Technology Installation

Table 5-8 shows the projected control technology installation in 2013
for the policies analyzed.  

Table 5-8 Control Technology Installation

Controls

Policy Run	LNB +

SNCR	LWS	ACI	LWS +

ACI	MBR	RTO	LWS +

RTO	DLI	BHMB

NSPS 	7	7	 -	 -	- 	 -	- 	- 	- 

NESHAP 	0	30	42	29	6	15	6	1	2

NSPS and NESHAP	7	27	41	22	8	11	12	1	1

Table 5-9 shows control cost associated with the control installation
shown in Table 5-8.  Table 5-9 also shows the monitoring cost associated
with the three policy scenarios analyzed. 

Table 5-9 Control Cost

Control Cost ($)

Policy Run	Control Cost	Monitoring	Total Cost

NSPS 	32,135,140	1,386,821	33,521,691

NESHAP 	333,536,975	17,236,198	350,773,173

NSPS and NESHAP	325,654,798	16,839,964	342,494,762

Cement Capacity

One important feature of the ISIS-cement model is that the cement
capacity is dynamically optimized by endogenously installing new
capacity, replacing existing capacity and retiring non-competitive
existing capacity.  Table 5-10 shows the cement capacity in 2013 for the
policies analyzed.  

Table 5-10 Cement Capacity

Capacity (metric tons cement)

Policy Run	Reference Case	Policy Case	Capacity Change	% Change 

NSPS 	94,036,939	92,680,715	-1,356,224	-1.4

NESHAP 	94,141,619	85,101,425	-9,044,929	-9.60

NSPS and NESHAP	94,141,619	84,145,488	-9,996,131	-10.62

With respect to the reference case in 2013 ISIS identified that 1.3
million metric tons of existing capacity may idle under the NSPS, 9.0
million metric tons of existing capacity may idle under NESHAP, while
9.9 million metric tons of existing capacity may idle under the
combination of NSPS and NESHAP.

Table 5-11 shows the number of kilns active in each policy analyzed in
ISIS.  A kiln that is active in ISIS-cement will run at 100% of its
capacity after taking into account normal downtime days. A kiln is
considered to be idling if it was producing cement in the reference case
but is not in the policy case.  As shown in Table 5-11, 1 kiln may idle
under the NSPS, 12 kilns may idle under the NESHAP, and 14 kilns may
idle under the combination of NSPS and NESHAP. If kilns owners decide to
run their kilns at a lower utilization rate a lower number of kilns will
be expected to idle under each policy scenario.  

Table 5-11 Kiln Population

Kilns

Policy Run	Reference Case	Policy Case	Kilns Idling	% Change

NSPS 	127	126	1	-0.78

NESHAP 	113	101	12	-11.88

NSPS and NESHAP	113	99	14	-12.38

Table 5-12 shows the number of cement facilities that may idle as result
of the policies analyzed in ISIS.  The changes in cement capacity and
kiln population may cause 4 facilities to idle under the NESHAP, and may
cause 8 facilities to idle under the combination of NSPS and NESHAP.  A
facility is considered idling if one or more kilns at the facility were
producing cement in the reference case but they are not producing in the
policy case.  

Table 5-12 Number of Cement Facilities

Facilities

Policy Run	Reference Case	Policy Case	Facilities Idling	% Change

NSPS 	86	86	0	0.00

NESHAP 	81	77	4	-4.93

NSPS and NESHAP	81	73	8	-9.87

Employment Impacts

Table 5-13 shows the total number of employee hours for each policy
analyzed.  The number of employee hours for each policy is a function of
cement production.  In each policy analyzed, the number of employee
hours is proportional to the cement production.  The difference between
the number of hours in the reference and policy case in each scenario
gives an estimate of the change in the number of employee hours due to
the policy analyzed.  Using PCA’s labor use data, we can calculate the
number of jobs that could potentially be affected by each policy under
analysis.  Table 5-14 shows PCA’s labor use data for 2005 and 2008
(PCA 2005, 2008).  Table 5-15 shows the estimated impact on employment
for each policy under analysis. 

Table 5-13 Change in Employee Hours

Employee Hours

Policy Run	Reference Case	Policy Case	Difference 	% Change

NSPS 	24,586,999	24,385,731	-201,269	-0.82

NESHAP 	24,086,384	21,735,357	-2,351,111	-9.76

NSPS and NESHAP	24,086,384	21,484,727	-2,601,741	-10.80

Table 5-14 PCA’s Labor Data: 2005 and 2008

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

2005	Wet Process	3,970,188	1,962	2,024

	Dry Process	35,448,534	16,562	2,140

	Total	39,418,722	18,524	2,128

	Average	19,709,361	9,262	2,082

2008	Wet Process	3,607,766	1,769	2,039

	Dry Process	34,767,447	16,504	2,107

	Total	38,375,213	18,273	2,100

	Average	19,187,607	9,137	2,073

Table 5-15 Employment Impacts

Employment Impacts

Policy Run	Change in Employee Hours	Change in Employment

NSPS	-201,269	94-97

NESHAP	-2,351,111	1,105-1,134

NSPS and NESHAP	-2,601,741	1,223-1,255

Total Social Cost

In ISIS, total social cost is the cost to comply with the policy under
analysis.  This cost includes installation of controls and monitoring
requirements as well as the cost to society due to the changes in
supply, demand, employment and price effects.  Table 5-16 shows the
change in surplus for each policy analyzed in ISIS.  As shown in Table
5-16 the surplus for the cement industry may fall by $95 million under
the NSPS, $520 million under the NESHAP, while it may fall $385 million
under the combination of NSPS and NESHAP.

Table 5-16 Total Social Cost

Surplus Loss (Million Dollars)

Policy Run	Reference Case	Policy Case	Change in Surplus	% Change

NSPS 	10,050	9,955	-95	-0.95

NESHAP 	9,229	8,808	-421	-4.56

NSPS and NESHAP	9,229	8,844	-385	-4.17

Emissions

Table 5-17 shows the estimated emission changes in 2013 for Hg, THC, PM,
HCl, SO2 and NOx for each policy analyzed.     

Table 5-17 Emissions

Emissions (tpy)

Policy Run	Pollutant	Reference Case	Policy Case	Change in Emissions
Percent Change

NSPS	PM	7,645	7,561	83	1.09

	Hg	5.78	5.32	0.46	7.94

	THC	12,207	12,185	22	0.18

	HCl	4,611	3,925	686	14.88

	NOx	126,751	116,043	10,708	8.45

	SO2	122,825	114,481	8,344	6.79

 

NESHAP	PM	6,349	620	5,729	90.23

	Hg	7.25	1.24	6.01	82.91

	THC	11,915	1,106	10,809	90.72

	HCl	4,434	128	4,307	97.12

	NOx	123,372	109,214	14,159	11.48

	SO2	115,163	34,919	80,245	69.68

	NSPS and NESHAP	PM	6,349	622	5,727	90.20

	Hg	7.25	1.25	6.00	82.76

	THC	11,915	1,060	10,8545	91.10

	HCl	4,434	137	4,297	96.91

	NOx	123,372	96,154	27,218	22.06

	SO2	115,164	35,775	79,389	68.94

References for Chapter 5

EPA (2010). August 6, 2010. "Summary of Environmental and Cost Impacts
for Final Portland Cement NESHAP and NSPS" Memorandum prepared for Keith
Barnett by RTI. US Environmental Protection Agency.

PCA. December 2005  XE “Portland Cement Association. December 2005” 
. U.S. and Canadian Labor-Energy Input Survey 2005. Skokie, IL: PCA’s
Economic Research Department. Portland Cement Association

PCA. December 2008  XE “Portland Cement Association. December 2005” 
. U.S. and Canadian Labor-Energy Input Survey 2008. Skokie, IL: PCA’s
Economic Research Department. Portland Cement Association

PCA. 2009. Forecast Report: Long-Term Cement Consumption Outlook. By Ed
Sullivan. October 28, 2009. Portland Cement Association

PCA. 2010. Forecast Report: Capacity Update. By Ed Sullivan. April 28,
2010. Portland Cement Association

Disclaimer

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

 

 PLEASE NOTE: for the purposes of this document, short tons will be
referred to simply as “tons” and represents 2000 lbs. A unit
conversion table is provided in the front matter of this document.

 Small amounts of SO3 may be released in addition to bulk SO2 but the
SO3 emissions are treated as SO2 for computational purposes. Small
amounts of CO may be released in addition to bulk CO2 but CO emissions
are treated as CO2 for computational purposes.

 Does not include 2 white cement kilns neither 2 cement kilns in Puerto
Rico. 

.

 PCA does not specify if “coke” is metallurgical coke or petroleum
coke. Authors believe it is the latter.

-

G 

L

«

\

Í

(

)

*

D

E

F

I

J

K

ÿࡵᔛ콨硓ᘀꝨ☈　⹊洀H渄H甄ĈᘑꝨ☈洀H渄H甄ĈᴀK

L

M

N

j

k

l

m

p

q

‡

ˆ

‰

£

¤

¥

¨

©

ª

«

¬

É

Ê

Ë

Ì

Ñ

Ò

í

î

ï

	



8

9

:

T

U

V

Y

Z

[

\

]

^

z

{

ᔛ콨硓ᘀꝨ☈　⹊洀H渄H甄ĈᘕꝨ☈䌀ᡊ洀H渄H甄Ĉ℀{

|

}

‚

ƒ

©

ª

«

Å

Æ

Ç

Ê

Ë

Ì

Í

Î

Ï

ë

ì

í

î

ó

ô

j¸

j;

 h

 h

h

h

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

hß&

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

hß&

hß&

hß&

瑹倌

kd

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

瑹倌

hß&

h

h

4

4

4

4

4

h¼

#

#

#

#

#

#

#

#

#

#

#

#

h¸

h

h

j˜þ

j

j

ć

!

)

*

-

 

*

+

1

3

`

a

b

c

k

p

x

Ê

×

ü

ý

ÿ

(*

+

1

c

Ê

C

d

k

C

D

W

X

Y

Z

a

b

k

m

q

s

kd

h

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

;

7 

 h³

 h³

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

k

kd7

 h³

”ÿ 	•

”ÿ 	•

”ÿ 	•

”ÿ 	•

”ÿ 	•

 h³

hë

hë

hë

hë

‚

š

æ

kd

úø#

ú

úø#

ú

 h 

 h

,

7

L

W

h

q

F

f

g

{

ƒ

…

ž

Ÿ

¡

¬

Í

Î

Ï

Ó

OÏ

ä

瑹⇹Ԁ

hd

b

b

b

b

b

b

b

b

b

b

b

b

¦

'Ï

¦

¦

¦

¦

hd

hr"

 h

h

h

 h

h

h

 h

h

h

 h

h

h

h

h

  h

 h

  h

h

 h

  h

 h

  h

 h

  h

 h

  h

h

 h

 h

h

h

 h

  h

 h

  h

 h

h:

h:

h:

h:

h:

yt:

€&š

yt:

耀騦

耀騦

耀騦

愀Ĥ摧睰!

愀Ĥ摧睰!

愀Ĥ摧睰!

愀Ĥ摧睰!

愀Ĥ摧睰!

h¡

愀Ĥ摧睰!

 h¡

h¡

 h

᷌

᷍

ᘑꝨ☈　ⵊ䌀ቊ䠀*ᔗ୨驪ᘀꝨ☈　ⵊ䌀ቊ䠀*䄀d reported
values may be higher in certain markets.

 Under the NESHAP any source that commenced construction after May 6,
2009 is a new source.  Under the NSPS, any source constructed,
reconstructed or modified after June 16, 2008 is a new source.

 Seven of the 16 new kilns projected to be subject to the NESHAP limits
for new sources are also kilns projected to be subject to the NSPS.

 PAGE   

 PAGE   1-2 

  PAGE  1-16 

  PAGE  2-29 

  PAGE  3-14 

  PAGE  3-19 

  PAGE  4-11 

 PAGE   5-10 

ISIS Inputs: 2013

Total Cement Facilities = 108

Total Cement Kilns = 177

Total Cement Capacity = 127.6 Million metric tons

Non-hazardous Waste Facilities = 97

Total Non-hazardous Waste Kilns = 157

Total Cement Capacity = 115.2 Million metric tons

Hazardous Waste Facilities = 11

Total Kilns = 20

Total Cement Capacity = 12.4 Million metric tons

Existing Facilities in 2009 = 93

Existing Kilns = 148

Total Cement Capacity = 103.8 Million metric tons

Projected New Facilities in 2010-2012 = 4

Projected New Kilns = 9

Total Cement Capacity = 11.4 Million metric tons