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---------------------- ---------------------------------- | |Charlotte Bertrand/DC/USEPA/US@EPA, Steven Silverman/DC/USEPA/US@EPA |

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-------------------------------------------------------- | |Fw: DRAFT ISIS TSD for OMB FOR DOCKET |

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-------------------------------------------------------- |Keith W. Barnett

USEPA/OAQPS/SPPD/MMG Mail Code D243

- 02 Research Triangle Park, NC 27711

919

- 541

- 5605 barnett.keith@epa.gov

----- Forwarded by Keith Barnett/RTP/USEPA/US on 07/29/2010 05:30 PM

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-------------------------------------------------------- | |Elineth Torres/RTP/USEPA/US |

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-------------------------------------------------------- | |Lisa Conner/RTP/USEPA/US@EPA, Ron Evans/RTP/USEPA/US@EPA, Charles Fulcher/RTP/USEPA/US@EPA, Ravi Srivastava/RTP/USEPA /US@EPA, Tom | |Walton/RTP/USEPA/US@EPA |

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-------------------------------------------------------- ||DRAFT ISIS TSD for OMB

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Keith,

Find attached the draft ISIS' TSD to be shared with OMB.

(See attached file: TSD ISIS NESHAP and NSPS with July 29.doc)

Elineth Torres Environmental Protection Specialist
U.S. EPA OAQPS/SPPD/Program Design Group
109 T.W. Alexander Dr. Durham, NC 27711
Phone: 919

- 541

- 4347
Fax: 919

- 541

- 5600i 

# DRAFT

Technical Support Document

The Industrial Sectors Integrated Solutions (ISIS)
Model and the Analys i s for the National Emission Standards for Hazardous Air Pollutants and New Source Performance Standards fo r 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

July 2010ii 

## Table of Contents

List of Tables

................................ ................................ ................................ ................................ ............... iv

List of Figures

................................ ................................ ................................ ................................ ............... v

Conversion Table

– English Units to SI Units

................................ ................................ ............................. ix

Chapter 1 Introduction

................................ ................................ ................................ ............................... 1

- 1

1.1 The US Cement Industry

................................ ................................ ................................ ............ 1

- 1

1.1.1 Cement Types and Categories

................................ ................................ ............................ 1

- 1

1.1.2 Overview of the Cement Manufacturing Process

................................ .............................. 1

- 2

1.1.3 Kiln Types and Their Use

................................ ................................ ................................ .. 1

- 5

1.1.4 Portland Cement Production in the U.S.

................................ ................................ ............ 1

- 7

1.1.5 Imports of Portland Cement in the U.S.

................................ ................................ ............. 1

- 8

1.1.6 Cement D emand Centers

................................ ................................ ................................ . 1

- 10

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

................................ . 1

- 10

1.3 Overview of ISIS

................................ ................................ ................................ ..................... 1

- 12
References for Chapter 1

................................ ................................ ................................ ......................... 1

- 14
Chapter 2 ISIS Mathematical Framework

................................ ................................ ................................ . 2

- 1

2.1 Indexes, Sets, and Mappings

................................ ................................ ................................ ...... 2

- 3

2.2 Objective Function

................................ ................................ ................................ ..................... 2

- 4

2.3 Supply

................................ ................................ ................................ ................................ ........ 2

- 8

2.4 Production Capacity and Supply Costs

................................ ................................ .................... 2

- 10

2.5 Emissions

................................ ................................ ................................ ................................ . 2

- 14

2.6 Controls and Costs

................................ ................................ ................................ ................... 2

- 16

2.7 Costs of Energy Efficiency Measures

................................ ................................ ...................... 2

- 23

2.8 Policy Options

................................ ................................ ................................ .......................... 2

- 26

2.9 Optimizatio n and Post

- Processing

................................ ................................ ........................... 2

- 27
References for Chapter 2

................................ ................................ ................................ ......................... 2

- 28
Chapter 3 Input Data for ISIS

- Cement Model

................................ ................................ ........................... 3

- 1

3.1 Data Requirements

................................ ................................ ................................ ..................... 3

- 1

3.2 Cement

- Specific Data

................................ ................................ ................................ ................ 3

- 1

3.2.1 Industry, Fuel, and Emissio ns

................................ ................................ ............................ 3

- 1

3.2.2 Control Technologies and Emission Abatement Approaches

................................ .......... 3

- 13

3.2.3 Policy and Economic Parameters

................................ ................................ ..................... 3

- 13
References for Chapter 3

................................ ................................ ................................ ......................... 3

- 24
Chapter 4 Model Calibration

................................ ................................ ................................ ..................... 4

- 1iii

4.1 Data for Ca libration

................................ ................................ ................................ ................... 4

- 1

4.2 Cement Prices in USGS Districts

................................ ................................ ............................... 4

- 1

4.3 Cement Production in USGS Districts

................................ ................................ ....................... 4

- 1

4.4 Cement Imports by Import Districts

................................ ................................ .......................... 4

- 1

4.5 Calibration Methodology

................................ ................................ ................................ ........... 4

- 5

4.6 Re

- calibration of the Model

................................ ................................ ................................ ....... 4

- 5
References for Chapter 4

................................ ................................ ................................ ........................... 4

- 9
Chapter 5 Analysis and Results of the Portland Cement NESHAP and NSPS

................................ .......... 5

- 1

5.1 Affected Sources

................................ ................................ ................................ ........................ 5

- 1

5.2 Emission Limits

................................ ................................ ................................ ......................... 5

- 2

5.3 Control Technologie s

................................ ................................ ................................ ................. 5

- 3

5.4 ISIS Runs and Results

................................ ................................ ................................ ................ 5

- 3

5.4.1 ISIS Results

................................ ................................ ................................ ........................ 5

- 4

Refer ences for Chapter 5

................................ ................................ ................................ ......................... 5

- 10

Disclaimer

................................ ................................ ................................ ................................ ................ 5

- 11
Appendix A Andover Technology Partners and RTI International Memos

................................ ................. 1iv



### List of Tables

Table 1

- 1. Typical Average Heat Input by Cement Kiln Type

................................ ........................... 1

- 5

Table 1

- 2. Number of Kilns by Kiln Type in the U.S. in 2005 and 2009

................................ ........... 1

- 6

Ta ble 1

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

....... 1

- 9

Table 3

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

................................ ...... 3

- 1

Table 3

- 2. Portland Cement D emand in Millions of Metric Tons

................................ ....................... 3

- 4

Table 3

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

................................ ..................... 3

- 6

Table 3

- 4. Portland Cement and Clinker Imports in Million Metric Tons, by Customs District i n

2005

................................ ................................ ................................ ................................ ... 3

- 6

Table 3

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

Types

................................ ................................ ................................ ................................ . 3

- 9

Table 3

- 6. Estimated Uncontrolled NO X Emission Intensities for Cement Kilns

............................. 3

- 10

Table 3

- 7. Average SO 2 Emissions for Each Kiln Type in Each State

................................ ............. 3

- 11

Table 3

- 8. Approximate CO 2 and H 2 O Produced from Combustion of Fuels

................................ ... 3

- 12

Table 3

- 9. NO X Control Technologies for Cement Kilns

................................ ................................ .. 3

- 15

Table 3

- 10. SO 2 Control Technologies for Cement Kilns

................................ ................................ ... 3

- 17

Table 3

- 11. CO 2 Control Technologies for Cement Kilns

................................ ................................ .. 3

- 19

Table 3

- 12. H Cl, Hg, and THC Control Technologies for Cement Kilns

................................ ........... 3

- 20

Table 3

- 13. Multimedia Impacts of Process Capacity Replacement on Cement Kiln Operation 1

...... 3

- 20

Table 3

- 14. Energy Efficiency Measures for Raw Materials Preparation

................................ ........... 3

- 21

Table 3

- 15. Energy Efficiency Clinker Making Measures

................................ ................................ .. 3

- 22

Table 3

- 16. Energy Efficiency Measures for Finish Grinding

................................ ............................ 3

- 23

Table 3

- 17. Energy Efficiency Plant

- wide Measures

................................ ................................ .......... 3

- 23

Table 4

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

................................ ................... 4

- 2

Table 4

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

................................ ............ 4

- 3

Table 4

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

tons)

................................ ................................ ................................ ................................ ... 4

- 4

Table 4

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

................................ ... 4

- 6

Table 4

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

................................ ... 4

- 7

Table 4

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

................................ ... 4

- 8

Table 4

- 7. Aggregate production (r eported and modeled) for 2005

- 2007

................................ .......... 4

- 8

Table 5

- 1. Final NESHAP Emission Limits

................................ ................................ ........................ 5

- 2

Table 5

- 2. Final NSPS Emission Limits

................................ ................................ .............................. 5

- 2

Table 5

- 3. Control Technology and C ontrol Efficiency Matrix by Pollutant

................................ ...... 5

- 3

Table 5

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

................................ ............................... 5

- 4

Table 5

- 5 Policy Impact on U.S. Cement Production

................................ ................................ ........ 5

- 5

Table 5

- 6 Policy Impact on U.S. Cement Imports

................................ ................................ ............. 5

- 5v

Table 5

- 6 Policy Impact on U.S. Cement Prices

................................ ................................ ................ 5

- 5

Table 5

- 7 Control Technology Installation

................................ ................................ ........................ 5

- 6

Table 5

- 8 Control Cost

................................ ................................ ................................ ...................... 5

- 6

Table 5

- 9 Cement Capacity

................................ ................................ ................................ ................ 5

- 6

Table 5

- 10 Kiln Population

................................ ................................ ................................ .................. 5

- 7

Table 5

- 11 Number of Cement Facilities

................................ ................................ ............................. 5

- 7

Table 5

- 12 Change in Emplo yee Hours

................................ ................................ ............................... 5

- 7

Table 5

- 13 PCA’s Labor Data: 2005 and 2008

................................ ................................ .................... 5

- 8

Table 5

- 14 Employment Impacts

................................ ................................ ................................ ......... 5

- 8

Table 5

- 15 Total Social Cost

................................ ................................ ................................ ................ 5

- 8

Table 5

- 15 Emissions

................................ ................................ ................................ ........................... 5

- 9



### List of Figures

Figure 1

- 1. Schematic of the Wet Cement Process

................................ ................................ ............... 1

- 3
Figure 1

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

................................ ......... 1

- 4
Figure 1

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

............................. 1

- 6
Figure 1

- 4. Portland Cement Plant Locations

................................ ................................ ....................... 1

- 8
Figure 1

- 5. Imports of Clinker an d Cement from 1998 to 2008 (PCA, 2009a)

................................ .. 1

- 10
Figure 1

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

...................... 1

- 12
Figure 1

- 7. An Integrated View of Pollution Generation Pathways, E missions Abatement

Approaches, and Multimedia Impacts for an Industrial Sector

................................ ....... 1

- 14
Figure 2

- 1. Consumer and Producer Surplus in a Market

................................ ................................ .... 2

- 2
Figure 2

- 2. Broad Modules and Associated Information F lows Utilized in the ISIS Framework

........ 2

- 2
Figure 2

- 3. Stepwise Calculation of the Demand Curve

................................ ................................ ...... 2

- 6
Figure 3

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

................................ ........... 3

- 3
Figure 3

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

................................ ...... 3

- 8
Figure 5

- 1 ISIS Inputs: Portland Cement Facilities and Kilns Modeled in ISIS

- Cement

................... 5

- 1vi



### Acronyms and Abbrevi ations

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 Av erage 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 Ca pture

CEMBUREAU European Cement Association based in Brussels

CEMS Continuous Emission Monitoring System

CGC Convert to Reciprocating Grate Cooler

CIF Cost, Insurance & Freight

CKD Cement Kiln Dust

CO Colorado

CO Carbon Monoxide

CO 2 Carbon Dioxide

CSI Combustion System Improvement

DE Delaware

DLI Dry Lime Injection

EGFW Ex haust Gas Flow Rate (wet)

EIA Energy Information Administration

EMCS Energy Management and Control System

EMD Efficient Mill Drives

EMPC Energy Management and Process Cont rol

EPA U.S. Environmental Protection Agency

ESP Electrostatic Precipitator

ETS Efficient Transport System

FL Floridavii

FOM Fixed Operation and Maintenance

GA Georgia

GAMS General Algebraic Modeling System

GDP Gross Domestic Product

GHG Greenhouse G as

H 2 O Water

HAP Hazardous Air Pollutant

HCl Hydrochloric Acid

HEC High

- Energy Classifiers

HEM High Efficiency Motors

HERM High Efficiency Roller Mill
Hg Mercury

HPRP High

- Pressure Roller Press

HRPG Heat Recovery for Power Generation

ID Idaho

IF Indirect Firing

IGMBM Improved Grinding Media (Ball Mills)

IL Illinois

IPCC Intergovernmental Panel on Climate Change

IPM International Petroleum Monthly

ISIS Industrial Sectors Integrated Solutions

KJ Kilojoules

LI Lime Injection

LNB Low NO X Burn er

LWS Limestone Wet Scrubber

MBR Membrane Bags Retrofit

MI Michigan

MMBtu Million British Thermal Units

MO Missouri

MSA Mineralogical Society of America

MYB Minerals Yearbook (USGS)

NAA Nonattainment Area

NACT North American Cement Transportation

NAS National Academy of Science

NATA National Air Toxics Assessment

NC North Carolina

NEI National Emission Inventory

NESCAUM Northeast States for Coordinated Air Use Managementviii

NESHAP National Emission Standards for Hazardous Air Pollutants

NM New Mexico

NO X Oxides o f 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 Managem ent 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 C onsu mption

SG&A Sales, General and Administration

SHLR Shell Heat Loss Reduction

SLC Salt Lake City

SNCR Selective Non

- Catalytic Reduction

SO X Oxides of Sulfur

SPE Spatial Price Equilibrium

SR Seal Replacement

TDF Tire Derived Fuel

THC Total Hydrocarb ons
TRAGIS Transportation Routing Analysis Geographical Information

Systemix

USGS United States Geological Survey

UT Utah

WA Washington

WBCSD World Business Council for Sustainable Development

WDOT Washington State Department of Transportation

WMCCC W ash Mills with Closed Circuit Classifier



### Conversion Table

– English Units to SI Units

To Obtain F rom Multiply by

m ft 0.3048

m 2 ft 2 9.29 × 10 - 2

m 3 ft 3 2.83 × 10 - 2

°C °F 5/9 × (°F

– 32)
kg lb 0.454
J/kg Btu/lb 1.33 × 10 - 4

m 3 /s cfm 4.72 × 10 - 4

m 3 / s gpm 6.31 × 10 - 5

J/kWh Btu/kWh 1055.056

mills $ 0.001 kg/m 2 in. Hg 345.31

metric ton short ton 0.9071

- 1 

## Chapter 1 Introduction

In the National Academy of Science’s 2004 report, “Air Quality Management in the United States,” the National Research Council (NRC) recommended to the U.S. Environmental Protection Agency (EPA) that standard setting, planning, and control strategy development should be based on integrated assessments that consider multiple pollutants, and that these integrated assessments should be con ducted in a comprehensive and coordinated manner (NAS, 2004). With these recommendations, EPA began to move toward establishing multi pollutant and sector

- based approaches to manage air quality and environmental protection. The benefits of multi pollutant an d 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 compl exities and reducing conflict ing 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 a nalyses are greatly e n hanced by the use of an appropriate modeling framework. Accordingly, the Industrial Sector s Integrated Solutions (ISIS) model has been develo p ed at EPA. Currently, the ISIS m odel is populated with US cement manufacturing data, and eff orts 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

m odel and its application to the US cement manufacturing industry and the analysi s performed fo r the Portland C ement National Emission Standards for Hazardous Air Pollutants (NESHAPs) and the New Source Performance Standards (NSPS) for the Portland cement manufacturing industry

.



### 1.1 The US Cement Industry

1.1.1 Cement Types and Categories

Cement is a finely g round 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, ag gregate, sand, and water). Concrete production uses the majority of cement produced.

Portland cement and blended cement are used in concrete production

, but P ortland cement is by far the most common type of cement used for concrete production. By modifyin g the raw material mix and, to some degree, the temperatures utilized in manufacturing, slight compositional variations can be achieved to produce P ortland cements with slightly different properties. In the U.S., the different varieties of
P ortland cement are denoted per the American Society for Testing and Materials (ASTM) Specification
C

- 150. The ASTM standard C

- 150 recognizes eight types of P ortland cement:

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

.

 Type IA is sim ilar 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 concr ete must set and gain strength rapidly

.1

- 2

 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 s oil and groundwater

.
Portland cements are usually gray, but a more expensive white P ortland 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 addi tion to the eight types of P ortland cement listed above, small volumes of specialty cements are also manufactured. Other types of specialty cement include blended cement ( P ortland cement mixed together with blast furnace slag or other pozzolan), pozzolan

- l ime cement, masonry cement, and aluminous cement (PCA, 2008a). The common industry practice, and that of the US Geological Survey (USGS), includes, within the P ortland cement designation, a number of other cements not within ASTM C

- 150, which are composed largely of P ortland cement and are used for similar applications (e.g., concrete) (USGS, 2005). These c ements include blended cement, block cement, expansive cement, oil well cement, regulated fast setting cement, and waterproof cement. Plastic cements and P ortland

- lime cements are being grouped within the masonry cement designation. Because P ortland 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 b e adequately captured by describing the market processes associated with the production, distribution, and use of P ortland cement, in this work the focus
is on Portland cement. In 2006, P ortland cement’s market share in the U.S. was 9 4 % , while masonry

ceme nt’s market share comprised the remaining 6 % (USGS, 2007 a )

.



### 1.1.2 Overview of the Cement Manufacturing Process

Portland cement is produced from raw materials such as limestone, chalk, shale, clay, and sand . These raw materials are quarried, crushed, finely groun d, 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 heate d 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 t o produce P ortland cement. The main steps in the cement manufacturing process are illu strated in Figures 1

- 1 and 1

- 2 showing the wet process and the dry process with cyclone preheater. The s chematic for a precalciner kiln would be very similar to that show n 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 els e where. 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 moi s ture is driven off from the materials. Then the calcium
carbonate in limestone di s sociates into CO 2 and calcium oxide (free lime) during calcina tion. Finally, the lime and other minerals in the raw materials react to form calcium silicates and calcium aluminates, the main components of clin k er.1

- 3

Figure 1

- 1. Schematic of the Wet Cement Process

Source: CEMBUREAU, 19991

- 4

Figure 1

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

Source: CEMBUREAU, 19991

- 5



### 1.1.3 Kiln Types and T

heir Use

Rotary kilns are broadly categorized as dry

- and wet

- process kilns, d e pending on how the raw materials are prepared. Wet

- process ki lns are fed raw m a terial slurry with moisture content ranging between 30 and 40%. A wet

- process kiln needs a d ditional length to evaporate the water contained in the raw material feed. Nearly 33% additional kiln energy is consumed in evaporating the water i n the slurry.

In d ry

- 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 kiln s , preheater kiln s , and preheater/precalciner kiln s . In preheater kilns and preheater/preca lciner kilns, the early stages of pyro processing 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 sh ows heat input in terms of million s of British Thermal Units (MMBtu)/ton 1 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,

r eplacement of wet and long dry process capacity with more efficient kiln process technologies is expected.

Table 1

- 1. Typical Average Heat Input by Cement Kiln Type

Kiln Type Heat Input,

MMBtu/ton of clinker

Wet 6.0

Long Dry 4.5

Preheater 3.8

Preheater/ Precalciner 3.3

Source: EPA, 2007

b (Table 3

- 3)

As expected, a rec ent 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.

1 PLEASE NOTE: for the purposes of this document, short tons will be referred to simply as “tons” and represents 2000 lbs. A unit conversion table is provided in the front matter of this document.1

- 6 Table 1

- 2. Number of Kilns by Kiln Type in the U.S. in 2005 and 2 009

Kiln Type Number of Kilns (2005) Number of Kilns (2009)

Wet 50 4 1

Dry 48 32

Preheater 36 29

Precalciner 47 66

Source: PCA, 200 6 and 2009 a

Figure 1

- 3. Trends in Cement Kiln Type and Capacity in the U.S. (1995 to 200 8 )

Note: The PCA Plant Informa tion Summary became biennial in 2004. The 2005 data w ere 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 remain ing 5.5 % was produced by plants that operate both wet and dry kilns (USGS, 2007 b: Table 7 ).1

- 7



### 1.1.4 Portland Cement Production in the U.S.

The cement industry remains a vital industry in the U.S. and throughout the world. In the U.S.

, P ortland cement is a $12 billion industry ( van Oss, 2008 ). In 2005, the U.S. consumed a r ecord 128 million metric tons of P ortland cement (USGS, 2007 b: Table 1).

In 2005 , Portland cement was produced at 107 plants in the U.S., including 37 s tates and Puerto Rico. The locations of the clinker

- producing P ortland cement plant facilities in 2005 a re shown in Figure 1

- 4. T he

c ement m anufacturing 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, 2007 b ). Together, ten companies accounted for about 80 % of the total cement production in U.S. for 2005 ( USGS, 2007 b ). 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, 2007 b: Table 3 ).

I n 2009

, PCA project ed a capacity expansion of 2 7 million tons between 2008 and 201 3 , a n 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

- l ine between 2009 and 2013 (PCA, 200 9 b ). The investment in the se 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.
B uilding 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 f or a brownfield facility (Staudt, 2009 ).1

- 8 Figure 1

- 4. Portland Cement Plant Locations



### 1.1.5 Imports of Portland Cement in the U.S.

Portland cement is not only produced and consumed domestically, but it is also traded internationally. In 2005, the U.S. (not in cluding Puerto Rico) produced 94 million metric tons of cement (USGS

, 2007 b: Table 3) and imported 3 4 million metric tons of hydraulic cement and clinker (USGS, 2007 b: Tables 3 and 18 ). The level of imports to the U.S. is highly cyclical, with domestic pro ducers importing primarily when domestic plants are at full capacity and cannot meet excess demand. I mports also occur when domestic producers seek to capture market share. Generally, imported cement and clinker make up 20 to
27 % of domestic cement consump tion. 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, 2007 b ).

In 2005, the ten leading international cement an d clinker suppliers to the U.S. were, in descending order, Canada, China, Thailand, Greece, the Republic of Korea, Venezuela, Mexico, Colombia, Taiwan, and1

- 9

Sweden. The ten busiest ports of entry within 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, 2007 b ). Table 1

- 3 shows the major customs districts for hydraulic cement and clinker imports in the U.S . Fi gure 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 thousa nds 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 1,4 89 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, 2007

b (Table 18)1

- 10 Figure 1

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



### 1.1.6 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 w a ter transportation modes are available (APCA, 1997).



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

Criteria pollutants, hazardous air pollutants, and carbon dioxide ( CO 2 ) are released during cement manufacturing. N itrogen oxide ( NO

x ) emissions from cement kil ns result prima r ily from the combustion process: oxidation of fuel nitrogen (fuel NO

x ) and the oxidation of nitrogen in the combustion air (thermal
NO

x ). EPA’s 2005 National Air Toxics Assessment (NATA) Inventory reports that cement kilns released 181,000 metric tons (200,000 short tons) of NO X emissions from the combustion of fuels (EPA, 2009).

Sulfur dioxide (SO 2 ) emissions 2 from cement kilns result from the combustion of sulfur

- bearing compounds in coal, oil, and petroleum coke, as well as sulfur compou nds in raw materials. Sulfur in the fuel will oxidize to SO 2 during pyroprocessing and a significant amount is likely to be captured in the
form of su l fates 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 captu r ing fuel

- generated SO 2 . Accordingly,

2 Small amounts of SO 3 may be released in addition to bulk SO 2 but the SO 3 emissions are treated as SO 2 for computation al purposes. Small amounts of CO may be released in addition to bulk CO 2 but CO emissions are treated as CO 2 for computational

purposes.1

- 11

oxidation of sulfur in the feed materials is likely to be the major component of total SO 2 emissions. The 2005 NATA Inve n tory reflects that c e ment kilns released 133,000 metric tons (147,000 short tons) of SO 2 emissions in 2005.

Particulate matter (PM) emissions result from q uarrying operations (the crushing and grinding of raw materials and clinker) as well as kiln line operations. The 2005 NATA Inventory estimat es show that, in
200 5

, cement kilns released 10,000 tons of PM 10 emissions. The cement industry is also a source of HAPs (e.g., hydrochloric acid vapor and chlorine ) , as well as metals including but not limited to mercury, antimony, cadmium

, and lead (EPA, 2008).

The c ement manufacturing process is also a source of carbon dioxide ( CO 2 ) emissions

. CO 2 emissions are a product of the combustion of fuel as well as the calcination of the limestone in the raw meal. The CO 2 from fuel combustion can be calculated from heat input and fuel characteristics combined with clinker production. The CO 2 from calcination can be calculated taking into account the amount of limestone used as a source of clinker calcium. Limestone currently is the predominant source of calcium for the clinker. Pure limestone would produce 0.44 tons of CO 2 for every ton of limestone completely calcined to calcium oxide. Substitute materials may be used in lieu of limestone with the effect of reducing the CO 2 emission s from clinker production. The CO 2 emission s 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 Sta ndards (NSPS) and the National Emissions Standards for Hazardous Air Pollutants (NESHAP s ) 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 (O 3 ) nonattainment areas (NAA), while 20 facilities were within PM 2.5 nonattainment areas. Seventeen facilities were found to be located in (or within 30 miles of ) Class 1 areas, as shown in Figure 1

- 6. Class 1 areas are areas of special natural, scenic, recreational, or historic (national or regional) value for which the Prevention of Significant Deterioration (PSD) regulations provide special protection.1

- 12 Figure 1

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



### 1.3 Overview of ISIS

ISIS , a sector

- based dynamic programming model, will facilitate the analys e s of emission reduction

stra tegies for multiple pollutants while taking into account plant

- lev el 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 i s able to provide information on the following:

 O ptimal (least cost) industry operation

.

 C ost

- effective controls to meet the demand for cement

.

 E mission reduction requirements over the time period of i nterest

.1

- 13

ISIS design allows for incorporating multiple industries within a multi

- market, multi

- product, multi pollutant , 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 a rea 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 i nput data consist of a n 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 mod el, 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

rep resentation 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 pr evention measures and the ones in red are mitigation measures. Clearly, the integrated picture presented in F igure 1

- 7 makes a compelling case for considering commodity production/supply activities along with emissions while developing holistic emission re duction 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 (NO X , SO 2 , PM, CO)

– emission limits and/or cap

- and

- trade

.

 Hazardous Air Pollutants ( e.g. Hg, HCl)

– emission limits

.

 CO 2 – cap

- and

- trade and/or emission taxes

.

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

.

 Regional or national requirement s

.1

- 14

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 Regulat ions 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, Dece mber 1999. D/1999/5457/December, Brussels. http://www.cembureau.be

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

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

EPA (2007b). Alternative Control Techniques Document Update

– NO X Emissions from New Cement Kilns. EPA

- 453/R

- 07

- 006, November 2007. http://www.epa.gov/ttn/catc/dir1/cement_updt_1107.pdf

, accessed October 21, 2008.

EPA (2008). AP 42, Fifth Edition, Compilation of Air Pollutant Emission Factors, Volume I, Chapter 11: Mineral Products Industry. http://www.epa.gov/ttn/chief/ap42/ch11/final/c11s06.pdf , accessed October 21, 2008. Complete document: http://www.epa.gov/ttn/chief/ap42/ , accessed October 21, 2008.1

- 15

EPA (20 09). 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 State s, National Academies Press, Washington, 2004. http://books.nap.edu/catalog.php?record_id=10728 , accessed October 21, 2008.

PCA (2005). Letter from David S. Hubbard, Director, Legislative Affai rs, Portland Cement Association. RE: Hours of Service of Drivers; Proposed Rule (Docket Number FMCSA

- 2004

- 19608), March 10,
2005. http://www.cement.org/exec/DHOS%20Comments%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. 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

. http://www.cement.org/econ/pdf/Long

- TermFlashwinter2007nonmember.pdf , accessed October 21,
2008.

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

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

Staudt, J. (200 9 ). NO X , SO 2 and CO 2 Emissions from Cement Kilns. Andover Technology Partners, Memorandum to Ravi Srivastava, March 10, 2009

.

USGS (2005). Background Facts and Issues Concerning Cement and Cement Data. U.S. Geological Survey, Open

- File Report 2005

- 1152. http://pubs.usgs.gov/of/2005/1152/2005

- 1152.pdf , accessed October 21, 2008.

USG S (2007a). Mineral Commodity Summaries. pp. 40

- 41, January 2007, http://minerals.usgs.gov/minerals/pubs/commodity/cement/cemenmcs07.pdf

, accessed September 24,
2009.

USGS (2007 b ). 2005 Minerals Yearbook: Cement. U.S. Geological Survey, p. 16.2, February 2007, http://minerals.usgs.gov/minerals/pubs/commodity/cement/cemenmyb05.pdf , accessed S eptember 24, 2009.

van Oss, H.G. (2008). Personal communication from Hendrik G. van Oss, USGS, to Elineth Torres, US EPA, on July 7, 2008.1

- 16 This page intentionally left blank.2

- 1 

## Chapter 2 ISIS Mathematical Framework

ISIS is a sector

- based dynamic linear programming model that can determine optimal sector operation for meeting demand and pollution 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 gener al, 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 th e past six 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

c ommodities 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 surp luses 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 exactl y 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 interes ts of both consumers and of producers ( Samuelson and Nordhaus , 1977)

.
The broad modules and associated information flows utilized in the ISIS framework are shown in Figure
2

- 2. While ISIS structure permits accounting for multiple products, the description below is provided relative to one product to bring out the important elements of the formulation and not burden the reader with many details. Also, to make the description more readable and understandable, the input data (i.e., those supplied by the user, or derived from user

- supplied data) are shown in bold and the variables, whose values are determined in the optimization process, are shown in italics . This scheme helps in organizing the numerous data elements and variables, and hopefully makes it easier for the reader to relate the data element to descriptions in the previous chapters. Finally, the names of variables and data elements have been chosen to be self

- explanatory as far as possible.2

- 2 Figure 2

- 1. Consumer and P roducer S urplus in a M arket

Fig ure 2

- 2. B road M odules and A ssociated I nformation F lows U tilized in the ISIS F ramework2

- 3



### 2.1 Indexes, Sets, and Mappings

Before we start the description of the mathematical structure of ISIS, it is helpful to de fin e the sets of relevant entities and mappings des cribing 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 uni ts, including existing, replacement, expansion, and new units. Subsets of i
described below define more

- specific populations of units;

o iE(i) is the set of existing units

o irepl(i) is the set of replacement units

o iExp(i) represents the set of expansion units

o iNew(i) represents the set of new units

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

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

 j is the set of sector

- specific pollutants of interest;

 k is the set of sector

- specific controls relevant to the emission reduction policy of interest;

 ee is the set of energy efficiency measures; and

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

Mappings

The mappings used in ISIS are:

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

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

 poltik(t,i,k) is the mapping relating availability and app licability 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 con trols k to unit i located in geographic region r and year t ; and2

- 4

 poltiree(t, ri(i,r),ee) is the mapping relating availability and applicability of energy efficiency

measures ee to unit i located in geographic region r and year t

.



### 2.2 Objective Function

As ment ioned 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)

w here 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 tra nsporting 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 marke ts dc

.2

- 5

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

A stepwise approximation of the demand curves ( FPL

- PELPS , 2003) is used in ISIS so that relevant area can be computed in a linear programming scheme. This approximation is explained below.

Stepwise Approximation of Demand Curves in ISIS

For clarity, the subs cripts 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)

w here

D is the demand for the commodity with corresp onding price P ( demand ) ;

is the elasticity of demand relative to price ;

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 ste pwise approximation of the price

- demand curve can be created.2

- 6 Figure 2

- 3

. Stepwise C alculation of the D emand C urve

First the range of the demand

- price curve is defined using

(2.2.3)

(2.2.4)

w here,

range is a user

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

.
Next, a series of steps is defined within the interval D min - D
max with the width of each step given by

(2.2.5)

w here,

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 us ed for Number of steps

.2

- 7

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 approximate d 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 desc ribed 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 funct ion 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
E quation (2.2.7) to gene rate price

- demand curves for all time periods and demand centers. These curves are used in the last term of the objective function and in the supply constraint, Equation (2.3.1), presented
in the next section.2

- 8



### 2.3 Supply

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

(2.3.1)

w here,

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

,
imported quantity(t, oi, i d

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

year t

, and

demand (t, dc ,dstep ) is the demand for the commodity at the dstep th level of th e 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 equati on.

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)

w here

prodn(t, i) is the production level (tons/year) of manufacturing unit i in year t . Note that, in general,

p roduction can be from existing unit s, units added at a plant (i.e., expansion units), newer units replacing unit s at a plant (i.e., replacement unit s), and new kilns. Treatment of these is explained in a subsequen t section.

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

(2.3.3)

w here

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 distr ict 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)2

- 9

w here

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

Imports(t, oi, id,istep) is the imported quantity available at the import district id in year t at the istep th level of the imports curve for country oi

. Construction of the country (or region) and import district specific imports

- cost curves is explained below.

Stepwise Approximation of Imports Curves in ISIS

The treatment of imports is similar to th e treatment for elastic demand curves. Again

, for clarity, the subscripts t , oi, and id corresponding to time, origin of imports, and imp ort 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)

w here

Imports is the imports of the commodity arriving from oi at id with corresp onding imports price (value) Iprice

is the elasticity of imports relative to imports price

.

{ Imports 0 , Iprice 0 } 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)

w here

r ange is a user

- supplied parameter with value between 0 and 1. This parameter defines the interval
I min - I
max within which the new equilibrium imports quantity is expected to be found. Note r ange should be large enough to ensure that the solution does fall in the interval I min - I
max

. On the other hand a smaller value of r ange can increase precision of the stepwise approximation. In ISIS a value of 0.5 is used for range

.
Next, a series of steps is defined within the interval I min - I
max with the width of each step given by

(2.3.8)

w here2

- 10

Number of steps is another user

- supplied parameter. Increasing the value of this parameter will increase precision of the stepwise approximat ion , 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 i step 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., { Iprice 0(t, oi, id ), Imports 0(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 E quation (2. 3

. 10 ) to generate the imports

- cost curves for all time periods , origins of imports, and import districts

.



### 2.4 Production Capacity and Supply Costs

The ISIS model includes constraints for ensuring that endog enous 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) i n 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

(2.4.1)

w here

v is the set of vintages of unit i

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

tcap (t,i, v ) is a binary variable that can bring v th vintage of unit i online in year t , and

techlifeplants is the technical life of a unit.2

- 11

Only one vintage of a unit i s 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 wi th 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 operat ion and maintenance ; (4) annual transportation costs ; and ( 5 ) annual cost of import s

. These costs are described below.

Capital Recovery

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

(2.4.3)

w here

pl antcapcost(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)

w here

p capcostt(t,i) is the capital cost ( e.g., $/ton of clinker for the cement secto r ) of i th unit in year t , and

CRFplant is the capital recovery factor calculated using an appropriate interest rate and time period, ecolifeplants , for capital recovery.

Annual capital costs for all populations except existing and projected is

(2.4.5)

w here

vcapcostt(v ,i) is the capital cost ( e.g., $/ton of clinker for the cement sector ) of vintage v for the i th

unit

.2

- 12

Variable Costs

The annual variable cost at a unit is calculated using

(2.4.6)

where

RMTt(t,i) is the raw material cos t ($/ton product ) at unit i in year t

,

VOM t ( t, i) is the cost of operation and maintenance ($/ton product ) at unit i in year t

,

LBR t ( t, i) is the cost of labor ($/ton product ) at unit i in year t

,

ELC t ( 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)

w here

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.

An nual water consumption related cost is given by

(2.4.8)

With annual water consumption given by

(2.4.9)

w here2

- 13

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

H2Ointensity(i) is the gallons of wat er 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)

w here

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 Annu al 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)

w here

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

- import curve.

InsF reight( 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 cal culated using2

- 14 (2.4.14)

w here

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

imprt transportcostt(t,i d ,dc) is the cost of transporting one ton of commodity ($ /ton) from imprt
district i d to demand center dc

.



### 2.5 Emissions

As discussed in the previous chapter, emissions can be generated from fuel firing and also from use of raw materials (e.g., CO 2 emissions from cal cination of li mestone in cement kiln). As such, bo th of these emission generation pathways are included in ISIS. Further, the ISIS framework includes algorithms to
account for tracking multi ple pollutant streams associated with uncontrolled emissions, controlled emissions, pollution prevention from proces s modifications and energy efficiency measures, and any
controls

- related effects (e.g., generation of CO 2 in a wet SO 2 scrubber). These algorithms are described below.

(2.5.1)

(2.5.2)

w here

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 operat ion in that year ;

emintensity fuel(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 a ny controls [ (e.g., Best Available Control Technology ( BAC T) ] are already installed on the unit ;

modeintensity(i,k,ee) is the modified energy intensity ( MM Btu 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 ;2

- 15

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 ( MM Btu per ton product) due to application of control k at unit i;

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

e HI displ (i, ee ) is the amount of heat input ( MM Btu per ton product) displaced (redu ced) 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)

w here

pol rmt (t,i, m ,j,k,ee) is emission (tons pollutant per t on 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 a ny controls (e.g., BACT) are already installed on the unit ;

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

secondaryRMT change percent (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)

w here2

- 16

pol(t,i, f,m ,j,k,ee) is emission (tons pollutant per ton product) of pollutant j resulting from processing fuel f and raw material m , an d 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 )

w h ere

var prodn(t,i,f,m,j,k

,ee ) is the production variable associated with use of k th control and/or ee th energy efficiency measure for j th pollutant at unit i using fuel f and raw material m in year t ;

emissions(t,i,j) are the emissions (tons of pollutant pe r 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 )

w here

totalemissions(t, ,j) are total emissions (tons of pollutant per ton of clinke r) 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 )

w here

prodnpol(t,i,f,m,j,) product ion variable associated with pollutant j

.



### 2.6 Controls and Costs

The ISIS framework includes constraints to ensure that endogenous applications of sector

- based controls and energy efficiency options occur in a realistic manner

. A description of these constrain ts and costs for controls is presented in this section. The treatment of energy efficiency measures is described in a subsequent section.

Controls Related Constraints2

- 17

Only one vintage of a control on a unit is possible for the period starting when the vin tage comes on line and ending with its technical life.

(2. 6.1 )

w here

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

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

ts_c (t,i, k,v ) is a binary variable that can bring v th vintage of control k for unit i online in year t , and

techlifecontrols(k) is the technical life of control k

.

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

(2.6.2)

w here

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 )

w here

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 an d/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 cos t of a control ($/ton clinker) for the cement sector] are escalated appropriately to use values in years of interest.

Capital Recovery and Fixed Cost2

- 18

Annual recovery of capital cost of control k is given by

(2.6.4)

Similarly, annua l FOM cost is given by

(2.6.5)

w here

vcntrlcapitalcostt(v ,i, k ) is the capital cost ($/ton of clinker) of application of v th vintage of control k

on i th unit

,

v cntrlfixedcostt(t,i, v ) is the annual fixed operation and maintenance (FO M) cost ($/ton of clinker) of application of v th vintage of control k on i th unit

,

capcost _c (t,i,k) is the annualized capital cost ($) of k th control application on i th unit

,

FOMcost _c (t,i,k) is the annual fixed operation and maintenance cost ($) of k th c ontrol application on i th unit , and

CRFcontrol is the capital recovery factor calculated using an appropriate interest rate and time period, ecolife control s , for capital recovery.

Variable Costs

Change in fuel cost associated with application of controls i s given by

(2. 6.6 )

w here2

- 19

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

,

second aryHIchange(i,k) is the primary heat input change (MMBtu per ton clinker) with use of k th

tech nology

,

fuelcost t ( 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 te chnologies.

poltir k (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 )

w here

primary RMT change percent is the percent change in primary raw material wi th use of k th technology

,

second ary RMT change(i,k) is the percent change raw material input corresponding to any secondary raw material addition with use of k th technology , and

RMTt(t,i) is the unit

- specific cost of raw material ($/ton clinker) in year t.

A nnual reagent consumption costs are

(2. 6.8 )

The annual consumption of a reagent given by2

- 20 (2. 6.9 )

w here

reagentconsumptfuel(i,f,k,j,rgnt) is the reagent consum ption 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 con sumption due to control (tons reagent/year).

Catalyst consumption cost is

(2. 6.10 )

w ith

(2. 6.11 )

w here

catalystconsumpt (i,k,j,cat) is the catalyst consumption rate (ft 3 catalyst/10000 ft 3 flue gas)

,

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

,2

- 21

catalystpricet(t,cat) is the price of the catalyst ($/ft 3 ) in year t

,

catconsumpt

_c (t,i,k ,cat ) is the catalyst consumpti on rate (ft 3 /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 )

w here

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 reag ent/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).2

- 22

Annual electricity consumption (kWh/yr) due to control is

(2. 6.14 )

The cost of electricity consumption is

(2. 6.15 )

w here

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 contro l k is

(2. 6.16 )

The cost of water consumption is

(2. 6.17 )

w here

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

,

H2Ocost t(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)

.2

- 23

T otal Annual Cost

U sing the above costs, the total annual cost of controls is

(2. 6.18 )



### 2.7 C osts of Energy Efficiency Measures

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

Energ y 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)

w here

v is the set of vintages of measure ee ,
poltiee(tpolc(t) is the mapping described before,

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

ts_ee(t,i,ee,v) is a binary variable that can bring v th vintage of measure ee online on unit i in year t, and

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

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

(2.7.2)2

- 24

w here

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)

w here

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, respect ively

.

(2.7.4)

(2.7.5)

w here

ve capitalcostt( v ,i, ee ) is the capital cost ($/ton of clinker) of v th vintage of ee th energy efficiency

measure application on i th unit

,

vefixedcostt(v ,i, ee ) is the annual fixed o peration and maintenance (FOM) cost ($/ton of clinker) of

v th vintage of ee th energy efficiency measure application on i th unit

,

ts_ee (t,i, ee,v ) is a binary variable that can bring v th vintage of measure ee online on unit i in year t

,

capcost _ee (t,i, ee ) is the annualized capital cost ($) of ee application on i th unit , and

FOMcost _ee (t,i, ee ) is the annual fixed operation and maintenance cost ($) of ee application on i th

unit , and2

- 25

CRF ee is the capital recovery factor calculated using an appropriate interest r ate and time period, ecolife ee , for capital recovery.

Variable Costs

Change in fuel cost associated with application of ee measures is given by

(2.7.6)

w here

e HI dispi (i, ee ) is the displacement of primary heat input (MMBtu per ton c linker) with use of ee th energy efficiency measure

,

fuelcost t ( 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

,
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)

w here

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

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

Total Annual Cost

U sing the above costs, the total annual cost of energy efficiency measures is2

- 26 (2.7.9)



### 2.8 Policy O ptions

ISI S 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 ISI S 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 amou nt 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 the ir 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
need ed. 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 SO 2 reduction progr am). 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)

w here

ec(t,j) is the emission cap for pollutant j in yea r t

,
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 emiss ion 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)

w here

ecpminer(t,i,j) is the unit

- specific minimum e mission reduction requirement for pollutant j in year t

.

Emissions Limits2

- 27

ISIS permits evaluation of the costs and emissions reductions associated with more traditional emission reduction programs utilizing unit

- specific rate

- based emission limits. Such re quirements are imposed using

( 2

. 8 .3)

w here

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 endogen ously 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 th e 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 drive n by the following term in the objective function ( see section 2.1 ),

(2.8.4)

w here

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 ev ery unit of pollution they produce. Thus this scheme can also be used to evaluate such programs.



### 2.9 Optimization and Post

- Processing

In ISIS, the input data are pre

- processed to arrive at suitable input parameters for use in the model equations explained in t his chapter. Once the data have been pre

- processed, ISIS solves for the appropriate levels of production, imports and controls required to meet the constraints associated with commodity demand and emissions, while maximizing total surplus. Once the surplus maximization problem has been solved, the results are post

- processed to obtain parameters and level values of the variables of interest. The key variables of interest are: production level of each production unit to meet regional demand, level of imports in each region, installation of various controls, emissions, and various costs. Output data are written in appropriate worksheets in an Excel workbook and further linked to various plots to enable visual presentation and analyses of the results.2

- 28



### Referen ces 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 programmin g 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

.2

- 29 This page intentionally left blank.3

- 1 

## Chapter 3 Input Data for ISIS

- Cement Model

3.1 Data Requirements

Data requirements for ISIS include sector

- specific (in this case, cement

- specific) data as well as policy and economic para meters. The cement

- specific data requirements are discussed below.



### 3.2 Cement

- Specific Data

The inputs to the ISIS model for the cement industry can be broadly categorized into three main components:

 Industry p roduction, fuel, and emissions ;

 Control technolog ies, and emission abatement approaches ; and

 Policy and economic parameters

.



### 3.2.1 Industry, Fuel, and Emissions

3.2.1.1 Existing, Planned/Committed, and Potential Units

T he ISIS cement industry model contains information on 1 68 cement kilns that were in existence in 20 0 9 and PCA’s projected capacity expansions from 20 10 to 201 2 , as shown in Table 3

- 1 (PCA, 200 9b ). Additionally, the model also includes potential kiln representations that may come on line as a result of endogenous capacity addition [i.e., new production c apacity (by state ) and rep lacements (by kilns) ].

Table 3

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

Kiln Population Number of Kilns

Existing (2009) 1 68 3

PCA’s Projected New Kilns (20 10

– 2012) 9

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

.

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

4 Annual clinker capacity as reported b y PCA refers to the kiln daily capacity multiplied by 365 less normal downtime days, where daily capacity is the normal clinker output a kiln can produce per day given a realistic work pattern and normal downti me days are number of days of downtime require d for maintenance, repair or cleanup

.3

- 2

3.2.1.2 Average Variable Costs

In previous economic analyses, five input variables in cement production have been identified to
determine the kiln

- level AVC function s : 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 r aw materials, labor, repair and maintenance, and electricity was determined following the methodology in the EPA regulatory impact analysis of the cement kiln dust rulemaking (EPA, 1998).

3.2.1.3 Cement Demand Centers

T he U.S. cement markets are organized in state

- specific d e mand centers. Figure 3

- 1 shows the distribution of Portland cement kiln s operating in 2009. Each state containing at least one kiln is shaded.
The ISIS

- cement model sim ulates each cement plant’s ability to compete in each of the demand centers as a function of the plant’s production cost and transportation cost associated with supply to each demand

center.

3.2.1.4 Portland Cement Demand

One of the key data inputs for the ISIS

- c ement 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 cem ent demand
was 1 28 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 c ement demand (in million metric tons) by state for 2005 through 2035 , in 5

- year increments, are shown in Table 3

- 2.3

- 3 Figure 3

- 1. Distribution of Cement Kilns in t he United States as of 20093

- 4 Table 3

- 2. Portland Cement Demand in Millions of Metric Tons

State Demand Center 2005 2010 2015 2020 2025 2030 2035

Alabama 1.92 1.45 2.02 2.16 2.32 2.49 2.67

Arizona 4.77 2.21 3.85 5.16 6.55 7.95 9.59

Arkansas 1.30 0.93 1. 29 1.41 1.54 1.67 1.81

California 16.01 8.33 13.88 15.43 17.11 18.95 20.96

Colorado 2.55 1.74 2.76 3.08 3.47 3.90 4.33

Connecticut 0.82 0.54 0.77 0.83 0.88 0.94 0.98

Delaware 0.22 0.18 0.26 0.28 0.29 0.31 0.32

District o f 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 1.6 6 1.12 1.63 1.78 1.94 2.12 2.31

Massachusetts 1.26 0.81 1.11 1.21 1.31 1.43 1.56

Michigan 3.06 1.54 2.05 2.19 2.44 2.68 2.75

Minnesota 2.06 1.29 1.86 2.09 2.34 2.57 2.80

Mississippi 1.14 1.07 1.50 1.58 1.66 1.76 1.85

Missouri 2.87 2.03 2.71 3.18 3.58 3.90 4.29

Montana 0.38 0.30 0.42 0.45 0.48 0.51 0.53

Nebraska 1.37 1.14 1.46 1.55 1.66 1.76 1.87

Nevada 2.63 1.20 2.33 3.26 4.27 5.14 6.16

New Hampshire 0.37 0.23 0.33 0.36 0.39 0.42 0.45

New Jersey 2.06 1.45 1.89 1.99 2.09 2.18 2.28

New Mexico 0.91 0.65 0.94 1.00 1.07 1.12 1.153

- 5

State Demand Center 2005 2010 2015 2020 2025 2030 2035

New York 3.29 2.84 3.60 3.73 3.87 3.98 4.08

North Carolina 3.25 2.16 3.43 3.92 4.50 5.16 5.88

North Dakota 0.36 0.39 0.47 0.47 0.46 0.44 0.42

Ohio 4.06 2.59 3.61 3.75 3.90 4.02 4.12

Oklahoma 1.67 1.57 1.68 1.76 1.87 1.98 2.11

Oregon 1.24 0.83 1.22 1.40 1.61 1.86 2.15

Pennsylvania 3.44 2.61 3.49 3.64 3.78 3.89 3.97

Rhode Island 0.19 0.12 0.17 0.18 0.19 0.19 0.20

South Carolina 1.94 1.07 1.81 1.99 2.19 2.38 2.56

South Dakota 0.48 0.44 0.55 0.57 0.61 0.64 0.67

Tennessee 2.52 1.54 2.38 2.61 2.89 3.19 3.53

Texas 15.09 12.11 17.51 21.03 24.10 27.48 30.58

Utah 1.53 1.22 1.82 2.08 2.40 2.78 3.23

Vermont 0.16 0.11 0.16 0.17 0.18 0.20 0.21

Virginia 2.87 1.87 2.84 3.13 3.45 3.80 4.17

Washington 2.24 1.68 2.50 2.75 3.04 3.36 3.73

West Virginia 0.54 0.47 0.59 0.60 0.60 0.59 0.58

Wisconsin 2.37 1.59 2.18 2.32 2.45 2.59 2.74

Wyoming 0.47 0.43 0.55 0.55 0.55 0.55 0.54

Total 128.03 83.54 122.81 139.06 155.70 173.14 191.51

3.2.1.5 Transportation

- Interregional Trade

In ISIS

- cement, a transportation matrix is used to describe the costs for transpor t ing cement from kiln and import district locations to demand centers. To d e velop these costs, information on distances between supply and demand points and cost s of transportation modes (truc k, 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 u sed to connect a supply point with a destination. While the cement demand ce n ters 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 c ost 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. Shipme nts to terminals are more than 80% by rail or
water, rat her 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

-3

- 6

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

BTS 1 WDOT 2 NACT 3

Truck 33.06 7.64 11.02 7.18 14.73

Rail 2.78 3.32 5.51 N/A 3 .87

Barge 0.89 1.05 2.20 N/A 1.38

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

- cost or at a profit"

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

. 3 (North American Cement Transportation) Report covers cement

- specific transportation costs, assumes short tons

-- converted to metric

.

3.2.1.6 Imports

U.S. cement markets receive imported quantities of cement and clinker from a number of countries

, and these imports arrive at more than 30 import districts (USGS

, 2009). In ISIS

- cement, international supplies from exporting countries to U.S. import districts are modeled using supply elasticity and then these
i m ports are transported to the demand centers.

The five largest international suppliers of cement and clinker to the U.S. are Ch i na, Thailand, Venezuela, South Korea, and Greece. An econometric study was conducted to provid e an estimate of international supply elasticity for supplie s 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 c e ment 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 w ere to increase by 1% within any i m port 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

. P ortland Cement and Clinker Imports in Million Metric Tons, by Customs District in 2005

USGS Customs District Quantity, million metric tons

Alabama, Mobile 0.51

Alaska, Anchorage 0.14

Arizona, Nogales; Mexico 1.073

- 7 USGS Customs District Quantity, million metric tons

California, Los Angeles 3.05

Californi a, 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, L aredo; Mexico 0.14

U.S. Virgin Islands 0.06

Vermont, St. Albans; Canada 0.133

- 8 USGS Customs District Quantity, million metric tons

Virginia, Norfolk 0.7

Washington, Seattle 1.49

Wisconsin, Milwaukee; Canada 0.19

Total 33.33

Source: USGS, 2007

. Data do not include Puerto Rico

3.2.1.7 Capacity Changes

Cement pla nts 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, an d 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 me n tioned earlier, ISIS

- cement includes algorithms for endogenous capacity growth and retiremen t of kilns. To determine capital recovery factor for capital costs a s sociated with kiln capacity changes, an economic life of 2 5 years and an interest rate of 15 % are used. Capital costs in 2005 $ per short ton of clinker for new, r e placement of wet, and r eplacement of dry capacity are 208, 296, and 238, respe c tively (PCA, 2009a)

.

3.2.1.8 Fuel Intensity

The Annual Energy Outlook (2008) energy use profile for 2005 is shown in Figure 3

- 2. In 2005, the cement sector consumed 451.2 trillion Btu (476.0 trillion kJ) of e nergy (EIA, 2008c). As shown in Figure
3

- 2, t he 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, 5 waste tires, and other wastes, especially oily wastes.

Figure 3

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

5 PCA does not specify if “coke” is metallurgical coke or petroleum coke. Authors believe it is the latter.3

- 9 Source: EIA, 2008a

In the ISIS

- cement model, t o 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 devel op 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 Ex ha ust Gas Flow Rate (wet) for Various Kiln Types

Kiln Type SFC EGFW

MMBtu/ton a Nm 3 /kg SCF/ton a

Wet 6.0 3.4 108,990

Dry 4.5 1.8 57,701

Preheater 3.8 1.5 48,084

Preheater/ Precalciner 3.3 1.4 44,878

a short t on Note: SFC=specific fuel consumption; Source : EPA, 2007 a (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.

3.2.1.9 Emission s

The design of the ISIS Model can accommodate any number of pollutants of interest. In ISIS

- cement, each kiln is characterized by its NO

x , SO 2 , PM, hydrochloric acid (HCl), mercury (Hg) , total hydrocarbon (THC), and CO 2 emission i ntensities. The characterization by kiln of the NOx, SO2, PM, HCl, mercury and THC emissions is presented in "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS" ( August 6, 2010 ) (EPA, 2010)

.

NO X emissions from cement kilns result primarily from the following combustion process: oxidation of fuel nitrogen (fuel NO X ) and the oxidation of nitrogen in the combustion air (thermal NO X ). Oxidation of nitrogen in the feed materials (feed NO X ) can also influence total NO X emissions. Table 3

- 6 shows NO X emission intensities for cement kilns in lb/ton of clinker and in lb/MMBtu (EPA, 2007a)

.3

- 10 Table 3

- 6

. Estimated Uncontrolled NO X Emission Intensities for Cement Kilns

Kiln Type Heat Input,

MMBtu/ton of clinker Uncontrolled NO
x Emissi ons

lb/ton of clinker * lb/MMBtu

Wet 6.0 9.7 1.62

Long Dry 4.5 8.6 1.91

Preheater 3.8 5.9 1.55

Preheater/Precalciner 3.3 3.8 1.15

* Average

Source: EPA 2007 (Table 3

- 3 and Table 6

- 1)

SO 2 emissions from cement kilns are the product of sulfur in the fuel as well as sulfur in the feed materials. Sulfur in the fuel will oxidize to SO 2 during pyroprocessing, and a significant amount is likely to be captured in the form of sulfates as the gas passes through the calcination zone. Compared to long dry and w et kilns, preheater and preheater/ precalciner kilns tend to be more effective at capturing fuel

generated SO 2
. Accordingly, oxidation of sulfur in the feed materials is likely to be a major component of total SO 2 emissions. Table 3

- 7 shows average SO 2 emis sions for each kiln type for each state ( Andover Technology Partners , 2009a ). State

- specific emission intensities of SO 2 were determined from emissions reported for the kilns in that state. For any state where the emission intensity was not available for a kiln

type, the national average emission intensity was assigned to kilns in that state.

CO 2 emissions from cement kilns result from limestone calcination and fuel combustion. C alcination releases 0.5 2 tons of CO 2 per ton of clinker produced, while fuel

- b ased CO 2 emission factors range from 199.52 lb CO 2 /MMBtu for coal to 105.02 lb CO 2 /MMBtu for natural gas ( Andover Technology Partners

, 200 9b; Andover Technology Partners , 2010 ). Table 3

- 8 shows approximate CO 2 and H 2 O produced from
combustion for the most important fuels for cement kilns.3

- 11 Table 3

- 7

. Average SO 2 Emissions for Each Kiln Type in Each State

SO 2 (lb/ton clinker)

State Precalciner Preheater Dry Wet

AL 0.09 0.61 9.02 13.99

AZ 1.30 0.07 8.51 7.8 1

AR 2.94 2.3 2 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 7.8 1

GA 0.95 2.73 11.42 12.25

ID 0.13 2.3 2 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 5.0 4 12.53 7.8 1

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 2.3 2 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 2.3 2 1.51 7.02

MT 0.13 2.3 2 9.02 0.70

NE 1.25 5.88 24.85 8.61

N V 0.49 0.05 1.51 7.8 1

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 7.8 1

NY 0.30 0.30 9.02 11.983

- 12 SO 2 (lb/ton clinker)

State Precalciner Preheater Dry Wet

NC 1.15 2.32 9.02 7.81

ND 1.15 2.32 9.02 7.81

OH 0.19 5.04 12.53 7.8 1

OK 1.25 5.88 24.85 8.61

OR 0.13 2.3 2 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 2.3 2 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 2.3 2 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 2.3 2 9.02 7.8 1

Table 3

- 8

. Approximate CO 2 and H 2 O Produced from Combustion of Fuels

Variable Name Flue Gas Tires Petroleum Cokes Heavy

Fuel Oil Rosemont

PRB Logan,

WV
BIT Natural

Gas

LBMCO2MMBTU lbmoles

CO 2 /MMBtu 4.26 4.83 3.85 4.25 4.53 2.39

LBCO2MMBTU lb CO 2 /MMBtu 187.44 212.56 169.32 186.83 199.52 105.02

LBMH2OMMBTU lbmoles

H 2 O/MMBtu 2.76 2.76 2.23 3.23 4.14 2.77

LBH2OMMBTU lbs H 2 0/MMBtu 49.71 49.73 40.26 58.21 74.64 49.92

PRB = Powder River Basin coal WV BIT = West Virginia bituminous coal3

- 13



### 3.2.2 Control Technologies and Emission Abatement Approaches

ISIS

- cement contains information on abatement approaches for NO

x , SO 2 , PM, HCl, Hg , THC, and CO 2 emissions described above. The three categories of a b atement approaches included are: process modifications and upgrades, raw mat e rial and/or fuel substitution, and mitigation technologies. For each
emission abat e ment approach, where possible, information on the following parameters was developed (Andover Technology Partners, 2008) and included in the model: c a p ital cost , fixed o perating cost

, variable o perating cost , e mission reduction pe r formance for all of the pollutants , i mpacts on fuel and/or raw material use , i mpact on electricity consumption , by product generation and cost , and i mpact on water

use

.
To estimate capital recovery factor s for capital costs associated with control technologies, economic life values of 15 and an interest rate of 7 % are generally used , but different value s can be selected by the user

. Pa y back periods and technical life for the energy efficiency measures shown in Tables 3

- 14 through 3

- 17 are given in Worrell and Galitsky (200 4 ). Economic life for each of these measures can be taken to be the average of the technical life and the payback period. Again, an interest rate of 7 % can be used for capital

recovery in the absence of more specific information

.

Table s 3

- 9 through 3

- 12 show the NO X , SO 2

, CO 2 , HCl

, Hg

, THC e missions control technologies being
used in the ISIS

- cement model . T able s 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 elec tricity consumption and heat input changes accomplished as a result of implementatio n of energy efficiency measures for R aw Materials Preparation

, Clinker Making

, Finish Grinding , and Plant

- Wide Measures , respectively.



### 3.2.3 Policy and Economic Parameters

3.2.3.1 Polic y 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 deminim i s requirements), emissions charge policy, or rate

- based pol i cies. I n 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 deminim i s requirements,
where the user defines a minimum level of emission r e duction 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 scena rios (rate

- based policies) with unit specific emission redu c tion requirements spec i fied by the user can be modeled in ISIS.

The user can specify the policy horizon (time period) to be used for the model runs. T he user can also choose to run ISIS with blo cks of years (e.g., 5

- year blocks). The sim u lation horizon and blocks of years can be chosen by the user subject to availability of data.

3.2.3.2 Additional Economic Parameters

In the ISIS framework, the following additional economic parameters are used : discount rate, escalation rates, and demand elasticity. For the ISIS

- cement model, the default discount rate has been chosen as 7%, as recommended by the U.S. Office of Management and Budget (OMB) for project eval uation (OMB, 1992). E scalation rates used can be fo und in the “ISIS_Inputs.xls” workbook. E scalation rate s 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 escalation3

- 14

factor s for variable operating and maintenance costs are based on Chemical Engineering Index. Escalation factors for various fuels and electricity are estimated based on data from the EIA’s Annual Energy Outlook 2008 (RTI, 2010 ). Demand for cement is relatively inelastic and an elasticity value of

- 0.88 is used in the model (EPA, 1998).3

- 15 Table 3

- 9

. NO X Control Technologies for Cement Kilns

Control Type Impact on Emissions,

± % change Electricity Consumption,

kWh/ton of cement Water Consumptio n, gal/ton of

cement NO X SO 2 PM Hg Other Grinding Kiln Operation

Low NO X Burners

– Indirect Firing - 20% to

- 30% 1 No impact 2 0 3

- 1.2 4 0 3

Mid Kiln Firing

- Tires - 20% to

- 40% 1 May vary 5 0 3 0 6 0 3

Low NO X Burner + Mid Kiln Firing

- Tires - 20% to

- 40% 1 May vary 5 0 3

- 1.2 4,6 0 3

Low NO X Burners + Tire Derived Fuel - 20% to

- 40% 7 May vary 5 0 3

- 1.2 4,6 0 3

Low NO X Burner + Selective Non Catalytic Reduction - 50% 2 No data 5 No data 8 No data 8 No data 8 0

- 1.2 4,9 +1.25 9

Low NO X Burner + Selective Catalytic
Reduction - 90% 2 Oxidation 10 No data 8 Oxidation 10 No data 8 0

- 1.2 4,9 +1.25 9

Low NO X Burners + CemStar a - 30% 11 - 1.3 (wet process);

- 1.9 (dry process) 12 - 1.2 4,12 from LNB

and

- 1.5 (wet process)

or

- 2.2 (dry process) from CemStar 13 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

1. See EPA (2004), Table 5

- 1.3

- 16

2. Andover Technology Partners 2009 b

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

4. Conversion from direct firing systems typical of wet and dry process kilns and older preheater kilns to indirect firing sys tems, required to implement
low NO X 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.

5. SO 2 emissions from cement kilns are strongly related to fuel and raw materials sulfur content and to method of kiln operation. Su lfur content of TDF (typically, 1.24% by weight, dry) may be higher or lower than the sulfur content of other fuels co mmonly used in cement kilns, such as coal or coke. Therefore it is not practical to relate SO 2 emissions to the use of these NO X control methods. With respect to use of an SO 2 wet scrubber using ground limestone, it is assumed that, for wet process kiln systems, uncontrolled SO 2 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 SO 2 ) of limestone of 90% purity in a 15% limestone
slur ry. 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 SO 2 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 SO 2 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.

6. Ki ln system and raw materials grinding electric power consumption would not be significantly affected by introduction of tires or tire

- derived fuel.

7. Combined effect will vary. Low NO
x 20 to 40%, MKF 20 to 40%.

8. While there may be theoretical or limited exper imental bases to assume increases or decreases in emissions of various pollutants in connection with
NO X emission controls, statistics on such effects are not available. See, generally, EPA (2007), Chapter 11.

9. Assumes SNCR or SCR both stoichiometric addit ions of NH 3 in 20

- percent solution . T ypical precalciner NO X 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

proce ss, long dry process, and many preheater kilns because the kiln gas exit temperatures are too low from those units for the ne cessary reactions to take place. See EPA (2007 a ). SNCR is assumed to achieve 63% reduction in NO X emissions from applicable kiln sy stems. See EPA (2007 a ), Chapter 8.
T here may be an attendant small increase in kiln system electric power in connection with injection of water into the kiln sys tem due to an increase in gas volume handled by the kiln’s fan system. The increase is not cons idered in the calculation.

10. Typically, up to about 1% of SO 2 can be oxidized to SO 3 , Elemental form of Hg oxidized across SCR provided sufficient concentration of halogens in flue gas.

11. On a lb/ton of clinker basis.

12. Use of the CemStar process may have multip le 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 ener gy consumption is assumed based on a roughly 5% reduction in kiln exit gases from both calcination and combustion.

13. CemStar and fly ash injection are expected to have similar effects on raw mix preparation and kiln process electrical energy requirem ents.3

- 17

14. S ee NESCAUM (2000) for effect of CemStar on NO X 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 ste am.
B. Cement kiln dust (CKD) disposal rates as functions of NO X or SO 2 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 cli nker 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 connec tion 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 Pollut ion 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 G as Desulfurization Processes. Final Report. 1992.

Table 3

- 10. SO 2 Control Technologies for Cement Kilns

Control Type Impact on Emissions,

± % change Electricity Consumption,

kWh/ton of cement Water Consumption, gal/ton of

cement NO X SO 2 PM Hg Other Grin ding Kiln Operation

Wet Scrubber

- 90% to

- 95% 1

- 80%

- 99.9% for HCl +0.2 (wet process kilns) 2 +5 2 +12 (wet process); +1.5 (dry process) 3

Dry Lime Injection

- 50% 4

- 75% for HCl 5 5 5

Notes to Table 3

- 10

1. Andover Technology Partners 2009 b

. 2. 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 ex haust 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 plu s increased gas volume3

- 18 demand on kiln ID fans due to the addition of water in the limestone slurry. Slurr y 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 t on per year cement kiln process.

3. SO 2 emissions from cement kilns are strongl y 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.
T herefore it is not practical to relate SO 2 emissions to the use of these NO X control methods. With respect to use of an SO 2 wet scrubber using ground limestone, it is assumed that, for wet process kiln systems, uncontrolled SO 2 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 SO 2 ) of limestone of 90% purity in a 15% limestone slurry. Limestone and water consumption are 15.5 lb and 87.7 lb

, res pectively

, per ton of cement produced. For a precalciner kiln, it is assumed that
uncontrolled SO 2 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 i n a 15% limestone slurry). Limestone and water consumption are 2.1 lb and 11.8 lb per ton of cement produced.

4. 20 percent a t Ca:S stoichiometry of 2:1 to 5:1

. Dry Sorbent Injection report. (ARCADIS, 1992)

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

General Notes: See Table 3

- 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 Contr ols, March 10, 2009

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

- 1393. Theoretical Approach for E nhanced Mass Transfer Effects in

- Duct Flue Gas Desulfurization Processes. Final Report. ARCADIS, 1992.

EPA ( 1995 ) . Compilation of Air Pollution Emission Facto rs

- Volume 1: Stationary Point and Area Sources. Fifth Edition, Supplements A

- F, AP

- 42. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.3

- 19 Table 3

- 11. C O 2 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 NO X SO 2 PM Hg CO 2 Other

Solvent CC 1 New

- 99%

- 99%

- 85%

- 0.022 0.429 4.82

Solvent CC 1 Retrofit

- 9 9%

- 99%

- 85%

- 0.103 0.160 4.82

Oxy

- combustion

- 99%

- 99%

- 85% 0.174 14.3

Notes to Table 3

- 11

1. CC = carbon capture

References for Table 3

- 11 Andover Technology Partners , Cost and Performance of Contr ols, March 10, 2009

. EPA, Report to Congress on Ce ment Kiln Dust, Chapter 3: CKD Generation and Characteristics; US Environmental Protection Agency: 1993.3

- 20 Table 3

- 12

. HCl, Hg, and THC Control Technologies for Cement Kilns

Control Type 1 Impact on Emissions,

± % change

NO X SO 2 PM Hg Other

ACI 2

- 99.9 %

- 90%

- 50% for THC

Membrane Bag

- 99.9%

RTO 3

- 98% for THC

Dry Lime Injection

- 50%

- 75% for HCl

Wet Scrubber - 90% to

- 95% - 80%

Notes to Table 3

- 12

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

2. AC I = a ctivated carbon injection.

3. RTO = regenerative thermal oxidizer

Table 3

- 13. Multimedia Impacts of Process Capacity Replacement on Cement Kiln Operation 1

Kiln Type Water

Consumption, gal/ton of

cement 2 Electricity Consumption,

kWh/ton of cement Waste

Grinding

3 Kiln Operation 4 Generatio

n Rate, ton/ton of
cement 5 Disposal Cost,

$/ton of cement

Wet to Precalciner - 214 2, 6

- 12 8

- 7 8

- 0.072 see notes 9,10

Long Dry to Precalciner No change 2 Not available 7 Not available 7

- 0.062 see notes 9,10

Preheater to Precalciner No change 2 Not available 7 Not available 7 0 11 see notes 9,10

Notes to Table 3

- 13.
1. Unless specifically noted otherwise, impacts are presented on a per

- short

- ton

- of

- cement basis and generally make use of published data reported on a per

- short

- to n

- of

- clinker basis. These values are converted to a per

- ton

of

- cement basis by assuming that cement consists of 92

- percent clinker. All weight units in this table are short

tons. 2. Water is not normally consumed in dry process cement kiln processes, except o n 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 circ uit 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 differen t between wet process and dry process plants.
3. Power data reported in Worrell and Galitsky (2004) are used for raw mix preparation and kiln process electrical energy consumption for wet and all dry processes.3

- 21

4. Air pollution control device contributions to el ectric power consumption data on cement kiln process systems are assumed to include only existing particulate matter control devices and not scrubbers for SO 2 or other criteria pollutants.
5. Solid waste from all process types is assumed to be cement kiln dus t (CKD). Data used here are as reported in EPA (1993).
6. Cement is assumed to consist of 92

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

- percent water. 7. Industry

- wide statistics on electrical energy use do not distinguish among the various cement processing stages between the various dry processes

– all dry process plants are averaged together. Published data that distinguish between various process types combine all process phases without indicating energy use for individual process phases. Therefore, electrical energy use is reported here to be the same for all dry process plants. See Worrell and Galitsky (20 04).
8. As stated in note 7, industry data on electric power use in raw materials preparation and kiln processing are not available for every dry process. Data are for all dry processes are combined.
9. Data reported in EPA (1993) on CKG generation by cement pla nts doe not distinguish between preheater and precalciner kilns with respect to net CKD generation rates.
10. CKD disposal prices vary widely by location. Transportation of CKD is a significant component of the cost of disposal if disposal is off

- site. For exa mple, 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

- si te disposal costs would include costs of transportation, dust control, and landfill operation. These costs have not been determined and would most certainly vary widely by location based on terrain, site geology, landfill operating requirements, and permit and future closure costs.
11. Modern preheater cement kilns and precalciner cement kilns generate similar volumes of CKD. Older preheater cement kilns are similar in CKD generation to long dry process cement kilns.

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 Classifi ers)

- 5.05

- 5.05

- 5.05

- 5.053

- 22 Table 3

- 15

. Energy Efficiency Clinker Making Measures

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

Dry Wet 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

- 2.003

- 23 Table 3

- 16. Energy Efficiency Measures for Finish Grinding

Energy Efficiency Improvement Method Electricity Consumption Change, kWh/ton clinker

D ry Wet Preheater Precalciner

EMPC ( Energy M anagement and P rocess C ontrol )

- 1.60

- 1.60

- 1.60

- 1.60

IGMBM ( Improved G

rinding M edia [B all M ills ] )

- 1.80

- 1.80

- 1.80

- 1.80

HPRP (High

- Pressure Roller Press)

- 16.00 - 16.00

- 16.00

- 16.00

HEC (High

- Efficiency Cl assifiers)

- 3.85

- 3.55

- 3.85

- 3.85

Table 3

- 17

. Energy Efficiency P l ant

- 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

- 2.5 0

HEM ( High Efficiency Motors )

- 2.50

- 2.50

- 2.50

- 2.50

ASD ( Adjustable Speed Drives )

- 6.25

- 6.00

- 6.25

- 6.25

OCAS ( Optimization of Compressed Air Systems )

- 1.00

- 2.50

- 1.00

- 1.00 *This is the only occurrence wherein “PM” does not stand for “particulate matter”3

- 24



### References for Chapter 3

American University (2008). TED Case Studies: Cemex Case

. http://www.american.edu/TED/cemex.htm

, accessed October 21, 2008.

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

Andover Technologies Partners (2009a). Memorandum: NO X , SO 2 and CO 2 Emissions from Cement Kilns (Emissions Memo) , from Jim Staudt to Ravi Srivastava, Samudra Vijay, Elineth Torres. Date d March 10, 2009 (see Appendix A of this document).

Andover Technolog y 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 Enhance d Mass Transfer Effects in

- Duct Flue Gas Desulfurization Process es. 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.

Depro, Brooks. (2010). RTI International. “ISIS Cement Production Costs”, Memorandum to Elineth Torres, US EPA, March 31, 2010.

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). Bu reau of Labor Statistics, Databases, Tables & Calculators by Subject, Major Sector Productivity and Costs Index. Series ID: PRS30006112, Sector: Manufacturing. http://data.bls.gov/PDQ/servlet/SurveyOutputServlet?data_tool=latest_numbers&series_id=PRS30006112

, accessed November 24, 2008.

EIA (2008) Annual Energy Outlook 2008, DOE/EIA

- 0383(2008), June 2008. http://www.eia.doe.gov/oiaf/aeo/pdf/0383(2008).pdf , accessed October 21, 2008.

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

EPA ( 1995 ) . Compilation of Air Pollution Emission Factors

- Volume 1: Stationary Point and Area Sources. Fifth Edition, Supplements A

- F, AP

- 42. U.S. Environmental Protection Agency, Research Triangle Park, North Carolina.3

- 25

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

EPA (2004). Alternative Control Techniques Document

- NO X Emissions from Cement Manufacturing, dated March 1994 (EPA

- 453/R

- 94

- 004)

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

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

EPA (2007). November 2007. Alternative Control Techniques Document Update

- NO X Emissions from New Cement Kilns. EPA

- 453/R

- 07

- 006 . Research Triangle Park, NC. US Environmental Protection Agency.

EPA (2010). August 6, 2010

. "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS" Memorandum prepared for Keith Barnett by RTI. US Environmental Protection Agency

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

NESCAUM (2000). Status Report on NO X Controls for Gas Turbines, Cement Kilns, Industrial Boilers, Internal Combustion Engines: Technologies & Cost E ffectiveness. Northeast States for Coordinated Air Use Management, December 2000. Accessed at http://www.nescaum.org/documents/nox

- 2000.pdf/

OMB (1992). Guidelines and Discount Rates for Benefit

- Cost Analysis of Federal Programs. OMB Circular No.A

- 94 (Revised). U.S. Office of Management and Budget, October 29, 1992. http://www.whitehouse.gov/omb/circulars/a094/a094.html#8

, accessed October 21, 2008.

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

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

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

PCA (2008). Forecast Report: Long

- Term Cement Consumption Outlook. By Ed Sullivan January 31,
2008

. http://www.cement.org/econ/pdf/Long

- TermFlashwinter2007nonmember.pdf , accessed October 21,
2008.

PCA (2009 a ). PCA Capacity Report. Flash Report. Portland Cement Associa tion’s Economic Research Department. Updated October 19, 2009.

PCA (2009 b ). Forecast Report: Long

- Term Cement Consumption Outlook. By Ed Sullivan. October 28, 2009. Portland Cement Association.3

- 26

Ryan, S. (2006). The Costs of Environmental Regulation in a Concentrated Industry. Department of Economics, MIT, Cambridge, MA, October 5, 2006. http://econ

- www.mit.edu/files/116 6 , accessed October 21, 2008.

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

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

.
USGS (2005). Background Facts and Issues Concerning Cement and Cement Data. U.S. Geological Survey, Open

- File Report 2005

- 1152

. http://pubs.usgs.gov/of/2005/1152/2005

- 1152.pdf , accessed October 21, 2008.

USGS (2007a). 2005 Minerals Yearbook: Cement. U.S. Geological Survey, p. 16.2, February 2007, http://minerals.usgs.gov/minerals/pubs/commodity/cement/ceme nmyb05.pdf , accessed October 21, 2008

USGS (2007b). Mineral Commodity Summaries: Cement, U.S. Geological Survey, pp. 40

- 41, January 2007. http://minerals.er.usgs.gov/mi nerals/pubs/commodity/cement/cemenmcs07.pdf , accessed October 21, 2008.

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

- 2007

- cemen.pdf

.
Worrell, E.; Galitsky, C. (2004 ). Energy Efficiency Improvement Opportunities for Cement Making; Lawrence Berkeley National Laboratory, LBNL

- 54036, Jan 2004. An ENERGY STAR ® Guid e for Energy and Plant Managers, http://www.osti.gov/energycitations/servlets/purl/821915

- Re2kcK/native/.

Yates, J.R. et al. (2003). CemStar Process and Technology for Lowering Greenhouse Gases and Other Emissions while Increasing Cement Production. http://www.hatch.ca/Environment_Community/Sustainable_Development/Projects/Copy%20of%20Cem
Star

- Process

- final4

- 30

- 03.pdf accesse d February 27, 2009.4

- 1 

## Chapter 4
M odel Calibration

4.1 Data for Calibration

Production quantities in various USGS production districts, import levels in various import districts and reported cement prices for various 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.



### 4.2 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 increas ed over the three year period.



### 4.3 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 dec rease in demand resulting from the economic downturn.



### 4.4 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 sig nificant 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 imported i n 2007 compared to that in 2005.4

- 2 Table 4

- 1. Cement Prices ($/short ton) for Various 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 8 1.19 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

Colora do and Wyoming 82.24 92.90 92.73

Idaho, Montana, Nevada and Utah 83.61 90.26 95.25

California 88.20 99.09 100.24

Oregon and Washington 79.38 90.72 90.724

- 3 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 1,0 64,833 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 12,73 7,207 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 Wa shington 2,175,963 2,101,005 2,103,4814

- 4

Table 4

- 3. Cement Imports (except from Canada and Mexico) for Various 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, N Y 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

Houst on

- 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 620,4 00 708,400 427,900
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 41 6,900 284,9004

- 5



### 4.5 Calibration Methodology

First, 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. Then the value of calibration parameter, calconst(i) , is set by trial and error, to minimize the absolute difference in the reported and model predicted prices (which are marginal values of the su pply equation) for each USGS district. Further, we also monitor impact of changing the calibration constant on estimated production quantities to keep the difference between reported and model predicted production values within reasonable limits 6 . The cali bration 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. Then in the input GAMS file, we assign the values for a given USGS dis trict to each of the kilns located in that USGS district. Reported and calculated values of prices by USGS district are shown (Table 4

- 4, Table 4

- 5 and Table 4

- 6, ) for year 2005

- 2007. In most of the districts reported and calculated prices are within 10% range, whereas in a few they are higher. 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. The model is first calibrated for year 2005, to obtain values of the calibration parameter calconst(i) for the year. Next, we use calconst(i) values for 2005 as starting point and obtain values for the same parameter for 2006. Similarly, the process is repeated to obtain the values for 2007. Then, we take an average of the calconst(i) values over the three years, and use it for the future model runs. Calibration is a dynamic process, and it is recommended that model calibration be performed periodically, to incorporate availability of the new production, imports and price data. We note that in general the price differentials are smaller in the year 2005 and have increased in 2007. Further while calibrating for price we also track aggregate production level to make sure that it is within reasonable limits (Table 4

- 7). 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, and potentially missing information in the transportation matrix

.



### 4.6 Re

- calibration of the Mode l

If any of the key input parameters, specifically those relating to production quantities and costs, are refined or modified, model needs to be re

- calibrated. Transportation matrix, modes, and cost of transportation also have significant impact on the beh avior 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.

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

Further, at the time of calibration of the current version of the model, production, import and price values for only 2005 to 200 7 were available. As the new values become available, the model needs to be recalibrated. The model can be and should be recalibrated as soon as new information or actual observed data become available. However, due to practical limitations it is recommend ed that the model be re

calibrated every two years. Further, since the calibration data is available only for three years, we have given equal weight to the parameters obtained for each year. Once larger data

- set is available, a modified weighing system ca n be adopted to give highest weight to the most

- recent year data.

6 There is no standard method to guide us to determine acceptable level of “error” in the reported and predic ted values for the purpose of calibration. We have set acceptable level for the absolute gap between the individual reported and predicted value s to ± 15%, although effort has been made to keep this level below 10% for most of the quantities. However, due to discontinuities in the transportation matrix, error in the reported data, or other unknowns, in certain markets the gap in the estimated and rep orted values may be higher.4

- 6 Table 4

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

USGS District Reported Calculated %Δ

Alabama 74.61 78 5

Illinois 80.29 83 3

Indiana 72.03 84 16

Kansas 76.58 81 6
Maryland 74.76 74

- 1

Missouri 78.92 82 4

Ohio 82.08 83 1

South Carolina 68.57 76 10

Maine and New York 80.74 73

- 9
Pennsylvania 79.73 77

- 3
Michigan and Wisconsin 79 .83 81 2

Iowa, Nebraska and South Dakota 78.72 81 3

Florida 77.56 75

- 3
Georgia, Virginia and West Virginia 84.49 80

- 6
Kentucky, Mississippi and Tennessee 84.37 81

- 4

Arkansas and Oklahoma 89.00 87

- 2

Texas 74.72 78 4

Arizona and New Mexico 75.76 1 05 39
Colorado and Wyoming 82.24 98 20

Idaho, Montana, Nevada and Utah 83.61 70

- 16

California 88.20 85

- 4

Oregon and Washington 79.38 69

- 134

- 7 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 Wiscons in 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 Mex ico 84.16 105 25
Colorado and Wyoming 92.90 100 8

Idaho, Montana, Nevada and Utah 90.26 83

- 8

California 99.09 85

- 14

Oregon and Washington 90.72 74

- 184

- 8 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

Ye ar Reported Production

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

2005 111.84 112.54 0.63

2006 110.30 111.23 0.84

2007 108.17 101.95

- 5.754

- 9



### References for Chapter 4

United States Geological Service. Mineral Yearbook 2008. Tab le 5. http://minerals.usgs.gov/minerals/pubs/commodity/cement/

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

United States Geological Service. Mineral Yearbook 2008. Table 11. http://minerals.usgs.gov/minerals/pubs/commod ity/cement/5

- 1 

## Chapter 5 Analysis and Results of the Portland Cement NESHAP and NSPS

5.1 Affected Sources

EPA is finalizing amendments to the NESHAP for the Portland cement manufacturing industry and the NSPS for Portland cement plants. The final 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 m ajor 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.

ISI S

- cement was used to conduct an integrated engi neering and economic analysis for the final Portlan d 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 k ilns , in ISIS

- cement all cement kilns projected to be in the U.S. in 2013 a re modeled to meet the

cement demand.

F igure 5

- 1 Portland Cement Facilities and Kilns Modeled in ISIS

- Cement ISIS Inputs : 2013

Total Cement Facilities = 108

Total Cement Kilns = 177

Total Cement Capacity = 127.6 Million metric tons

Non

- hazardous W aste Facilities = 97

Total Non

- hazardous Waste Kilns = 157

Total Cement Capac ity = 115.2 Million metric tons H azardous W aste 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 Faci lities in 2010

- 2012 = 4

Projected New Kilns = 9

Total Cement Capacity = 11.4 Million

metric tons5

- 2

From the 108 Portland cement facilities modeled in ISI S

- 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 t he NESHAP, 16 kilns are projected to be subject to the limits for

n ew 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

. 7



### 5.2 Emission Limits

Table 5

- 1 shows the emission limits for the final NESHAP analyzed in IS IS

, while Table 5

- 2 shows the emission limits for the final NSPS. Emission limits i n 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' Run s

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' Run s

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 clinke r

0.368 0.368 lb/ton cement

7 Under the NESH AP 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

.5

- 3



### 5.3 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 efficienci es 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 a lso include additional testing and monitoring requirement s 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

, T HC 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 c o st 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 ( D LI) 75% 7 0 %

Low NOx Burner (LNB) + Selecti ve Non

- Catalytic Reduction (SNCR) 65 %
B aghouse with Membrane Bags (BHMB) 99.9%
M embrane B ags Retrofit (MBR) 99.9%
ACI + LWS 90% 80% 99% 99.9% 90%
LWS + RTO 80% 98% 99% 90%



### 5.4 ISIS Runs and Results

EPA’s analysis focuses on the result s of the final NESHAP and final NSPS. W e also present additional information on different combinations of the regulatory programs to help stakeholders better unders tand the size and scope of each

. The three policies analyzed are:

o Final NSPS only,

o Final NES HAP only, and

o Final NSPS and NESHAP.5

- 4

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, o n e 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 1 28 million metric tons. T he economic distress in the U.S during 2008 and 2009 resulted in a decline in cement demand. T he demand for cement compared with 2005 levels decline d 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 e x ogenous 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 suppl y 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 f

r om 2010 to 2013. For the polic y scenarios analy zed 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) th e economic impacts of the policies

.



### 5.4.1 ISIS Results

5.4.1.1 Cement Demand

As mentioned in Chapter 3 , in ISIS

- cement the U.S. cement industry is organized in state

- specific demand

centers. The model simulates each cement kiln ’s ability to compe te in each of the demand center s as a fu nction 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., shipp ing 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 three policies analyzed

on the cement demand in 2013

. As shown in Table 5

- 4 , in 2013 the US cement demand could drop
0. 06 % under the NSPS

, 5.82 % under the NESHAP

, and 8.44 % under the combination of the NSPS and NESHAP policies.

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 123,653,270 123,733,005 79,735

- 0.06

NESHAP 123,653,270 116,460,063 7,193,207

- 5.82

NSPS and NESHAP 123,653,270 113,222,314 10,430,956

- 8.445

- 5

5.4.1.2 Cement Product ion

Table 5

- 5 shows the impact of the three policies analyzed on the US cement production in 2013. As shown in Table 5

- 5, U.S. c ement production could drop 0.12 % under the NSPS, 10.02 % under the
NESHAP

, and 13.46 % under the combination of the NSPS and N ESHAP policies.

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 93,920,258 116,681

- 0.12

NESHAP 94,141,619 84,710,916 9,4 30,703

- 10.02

NSPS and NESHAP 94,141,619 81,473,143 12,668,475

- 13.46

5.4.1.3 Cement Imports

Table 5

- 6 shows the impact of the three policies analyzed on US cement imports in 2013

. As shown in Table 5

- 6, U.S. c ement imports could rise 0.66 % under the NSPS; 7. 58 % under the NESHAP and 7.58%
under the combination of the NSPS and NESHAP policies.

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 29,812,747 196,416 0.66

NESHAP 29,511,650 31,749,147 2,237,496 7.58

NSPS and NESHAP 29,511,650 31,749,170 2,237,519 7.58

5.4.1.4 Cement Prices

Table 5

- 7 shows the estimated average national price in 2013 for Portland cement under the policies analyzed. As s hown in Table 5

- 7 , c ement prices may increase 0.7 5 % under the NSPS

, 6.99 % under the
NESHAP

, and 10.74 % under the combination of the NSPS and NESHAP policies

.

Table 5

- 7 Policy Impact on U.S. Cement Prices

Cement Price (S) Policy Run Reference Case Policy C ase Change in Price % Change

NSPS 87.77 88.43 0.66 0.75

NESHAP 85.25 91.21 5.96 6.99

NSPS and NESHAP 85.25 94.17 8.93 10.47

5.4.1.5 Control Technology Installation

Table 5

- 8 shows the projected control technology installation in 2013 for the policies analy zed

.5

- 6 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

- 28 40 26 8 11 9 1 3
NSPS and
NESHAP 7 26 44 20 4 8 9 1 1

Ta ble 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 33,225,564 1,386,821 34,612,385

NESHAP 323,556,215 16,641,847 340,198,062

NSPS and NESHAP 298,504,950 15,453,143 313,958,093

5.4.1.6 Cement Capacity

One important fea ture of the ISIS

- cement model is that the cement capacity is dynamically optimized by endoge nously installing new capacity, replacing existing capacity and retiring non

- competitive existing capacity

. Table 5

- 10 shows the projected capacity in 2013 for the three policies analyzed

.

Table 5

- 10 Cement Capacity

Capacity (metric tons cement) Policy Run Reference Case Policy Case Capacity Change % Change
NSPS 94,036,939 93,920,258 116,681

- 0.12
NESHAP 94,141,619 84,710,916 9,430,703

- 10.02
NSPS and NESHAP 94,141,619 81,473,143 12,668,476

- 13.46

With respect to the reference case in 2013 ISIS identified that 116,681 metric tons of existing capacity

may idle under the NSPS

, 9

. 4 million metric tons of existing capacity under NESHAP and 12.7 million metric tons of existing capacity under the combination of the N SPS and NESHAP policies

.

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

, 1 6 kilns under the NESHAP

, and 22 kilns under the combination of NESHAP and NSPS. The average size of the kilns that may idle under the NESHAP
and the c ombination of NES HAP and NSPS is 5 89

, 419 and 575,840 million metric tons of cement

, respectively.5

- 7 Table 5

- 11 Kiln Population

Kilns Policy Run Reference Case Policy Case Kilns I dling % Change

NSPS 129 128 1

- 0.78

NESHAP 114 98 16

- 14.04

NSPS and N ESHAP 114 92 22

- 19.30

Table 5

- 1 2 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 6 facilities to idle under the NESHAP and
11 under the combin ation of the NESHAP and NSPS. 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 R eference Case Policy Case Facilities I dling % Change

NSPS 85 85 0 0.00

NESHAP 81 75 6

- 7.41

NSPS and NESHAP 81 70 11

- 13.58

5.4.1.7 Employment Impacts

Table 5

- 1 3 shows the total number of employee hours for each policy analyzed

. The difference between the reference and policy case in each scenario give s an estimate of the change in the number of employee
hours due to the policy under analysis . 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

- 1 4 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

- 1 3 Change in Employee Hours

Employee Hours Policy Run Reference Case Policy Case Difference % Change
NSPS 24,586,999 24,586,069 930 0.00
NESHAP 24,069,236 21,558,859 2,510,377

- 10.43
NSPS and NESHAP 24,069,320 20,761,183 3,308,137

- 13.745

- 8 Table 5

- 14 PCA’s Labor Data: 2005 and 2008

Year Process Labor hours: Dire ct N umber of
E mployees N umber of Direct H ours

per E mployee

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
Av erage 19,709,361 9,262 2,082

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

Table 5

- 1 5 Employment Impacts

Employment Impacts Policy Run Change in Employee Hours Change in Employment
NSPS 930 0
NESHAP 2,510,377 1,180

- 1,211
NSPS an d NESHAP 3,308,137 1,555

- 1,596

5.4.1.8 Total Social Cost

In ISIS, total social cost is the cost to comply with the policy under analysis. This cost includes
installation of controls and monitoring requirements as well as the cost to society due to the changes in supply, demand, employment and price effects. Table 5

- 16 shows the change in surplus for each policy analyzed in ISIS. As shown in Table 5

- 1 6 the surplus for the cement industry may fall by $33 million under the NSPS; by $ 408 million under the NESHAP and by $ 451 million under the combination of the NSPS and NESHAP policies.

Table 5

- 1 6 Total Social Cost

Surplus Loss (Million Dollars) Policy Run Reference Case Policy Case Change in Surplus % Change
NSPS 10,027 9,994 33

- 0.33
NESHAP 9,343 8,936 408

- 4.37
NSPS and NESHAP 9,341 8,891 451

- 4.82

5.4.1.9 Emissions

Table 5

- 17 shows the estimated emission changes in 2013 for Hg , THC, PM, HCl, SO 2 and NO

x for each policy analyze d

.5

- 9 Table 5

- 1 7 Emissions

Emissions (tpy)

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

Change

NSPS PM 7,645 7,561 83 1.09

Hg 5.78 5.32 0.46 7.94

THC 12,207 12,185 22 0.18

HCl 4,611 3,925 686 14.88

NO

x 126,751 116,043 10,708 8.45

SO 2 122,825 114,481 8,344 6.79

NESHAP PM 6,439 615 5,823 90. 44

Hg 6.22 0.90 5.32 85.55

THC 11,894 1,081 10,813 90.91

HCl 4,502 126 4,376 97.20

NO

x 122,857 109,324 13,533 11.02

SO 2 115,163 34,919 80,245 69.68

NSPS and
NESHAP PM 6,351 620 5,731 90.24

Hg 6.22 0.81 5.41 86.99

THC 11,894 968 10,926 91. 86

HCl 4,502 135 4,367 97.00

NO

x 122,857 95,799 27,058 22.02

SO 2 115,164 35,775 79,389 68.945

- 10



### References for Chapter 5

EPA (2010). August 6, 2010

. "Summary of Environmental and Cost Impacts for Final Portland Cement NESHAP and NSPS" Memoran dum prepared for Keith Barnett by RTI. US Environmental Protection Agency

.

PCA . December 2005

. U.S. and Canadian Labor

- Energy Input Survey 2005 . S kokie, IL: PCA’s Economic Research Department. Portland Cem ent Association

PCA . December 200 8

. U.S. and Canadian Labor

- Energy Input Survey 2008 . S kokie, IL: PCA’s Economic Research Department

. Portland Cement Association

PCA

. 200 9

. Forecast Report: Long

- Term Cemen t Consumption Outlook . By Ed Sullivan

. October 28

, 200 9

. Portland Cement Association

PCA. 2010

. Forecast Report: Capacity Update. By Ed Sullivan

. April 28, 2010

. Portland Cement
Association5

- 11 

## Disclaimer

Reference herein to any specific comme rcial 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 Go v ernment. The views and opinions of authors expressed her ein do not necessarily state or reflect those of the United States Government, and shall not be used for advertising or product endorsement purposes.A

- 1 

## Appendix A Andover Technology Partners and RTI International Memos

Andover Technology Partners

May 7, 2010 Electrical Load for Wet Scrubbers

February 26, 2010 Wet Scrubber Cost Algorithms

July 10, 2009 GHG Mitigation Methods for Cement

March 10, 2009 Costs and Performance of Controls

– revised from comments

March 10, 2009 NO

x , SO 2 and CO 2 Emissions fr om Cement Kilns (Emissions Memo)

– revised

from comments

RTI March 31, 2010 ISIS Cement Production Costs

August 31, 2007 Draft: Documentation for Portland Cement Kiln Cost Functions (2005)