Steel Production

Processes, Products, and Residuals

Clifford S. Russell, William J. Vaughn

• Publisher: RFF Press
• Year: 2013
• Print ISBN: 9781617260674
• eBook ISBN: 9781135999254
• Topic: Business
• Subtopic: Business General
• Index: Business

The authors address the problems of determining the implications of different environmental standards and public policies by investigating their effect on industrial costs and resource use within linear-programming framework.

Originally published in 1976

Part I
Overview of the Steel-Making System and the Model

1
Policy Questions and Methodology

As this book goes to press, the United States is in the midst of a reappraisal of the environmental decisions made in the last half-dozen years and embodied in some of the showpiece legislation of the environmental movement, such as the National Environmental Policy Act (NEPA)¹ and the 1972 amendments to the Federal Water Pollution Control Acts of 1965.² Some of this reappraisal has been informal and intensely public, punctuated by advertisements of interested parties, inflammatory statements from political leaders, and lawsuits filed by anxious guardians of the natural world. On the other hand, some of the reappraisal has been institutionalized, as in the National Academy of Sciences studies of auto pollution control regulations;³ the ongoing cluster of academy studies of the entire decision-making process (concentrating on the use of scientific and technical information) within the Environmental Protection Agency (EPA);⁴ and in the National Commission on Water Quality created by the 1972 amendments.

The public debate and the quieter, presumably more scholarly studies tend to focus on the costs, measured in different ways, of meeting the legislated environmental standards or complying with legislated procedures. The measures of cost most widely estimated and quoted are:

1. The direct dollar cost that industries, government units, and private citizens will pay for control equipment, fuel switching, or lost production required or implied by particular laws. Thus, for example, the National Commission on Water Quality is spending a large fraction (19 percent) of its $15 million appropriation on technical, engineering studies to determine the cost to various industries of the treatment standards implied by the 1972 amendments.⁵
2. The short-term disruptive effects implied as the direct costs work themselves through the system: for example, unemployment, relative price changes, general price level increases,
3. The long-term effects on economic growth as determined by the traditional measures of aggregate economic welfare.
4. The increases (or decreases) in energy use per unit of output or, where appropriate, the effect on energy supply.
5. The implications for the use of any number of critical materials, the supply of which shifts because of the actions of various international cartels, or the vagaries of politics in developing countries.⁶

Unfortunately, the actual estimation of costs has not proved to be nearly so easy as the identification of the cost measures of greatest interest. A major reason for this has been the near monopoly on information concerning industrial costs held by industry itself. Data dealing with residuals generation and discharge have, until very recently, been considered trade secrets.⁷ This has allowed industry to control the debate by making its own claims about what it has spent on "pollution control" and to keep from the public knowledge of what residuals are actually being discharged. The tendency has been to take credit for every investment related to residuals, even though a particular action might very well have been taken in response to favorable market prices for, say, some recovered by-product. Further, industry has tended to exaggerate the anticipated costs of new policies, particularly in terms of the publicly sensitive issues of unemployment and growth effects.

But a second reason for our difficulties with cost estimation has to do with the complexity of the task, even given adequate data resources. For example, the level of residuals discharges from a particular plant in a particular industry, in the absence of government-imposed discharge limits or effluent charges, will vary with the relative prices of inputs, outputs, and by-products; with quality standards imposed on products; with available input qualities; and with the types of production processes in use. This raises a pair of symmetrical problems: The first problem lies in determining the cost of a policy that limits the discharge of residuals. This task is difficult because we cannot assume that reductions in discharge levels (and thus the cost of those reductions) can be attributed exclusively to the requirement for constraint. In some cases, part of the discharge reduction is attributable to forces, such as relative prices of by-products, which are exogenous to the constraint policy. The second problem lies in determining the effect of a given set of effluent charges. We cannot predict with any accuracy the level of effluent discharges resulting from the imposition of charges since, in fact, this level will continue to vary significantly with exogenous forces.

The Goals

This study has two complementary goals which grew out of our assessment of the difficulties and requirements just discussed. First, we aim to present a methodology for investigating the implications that alternative public policies, especially but not exclusively environmental policies, will have for industrial costs and resource use. Second, we produce some information, which we believe to be of value in its own right, about the environmental quality and resource use problems of the steel industry. The model structure, which we describe somewhat more fully below, was developed at Resources for the Future (RFF) in connection with a study of industrial water use funded by the National Water Commission.⁸ It builds on the earlier conceptual work of Blair T. Bower,⁹ and has since been applied to residuals management problems in petroleum refining.¹⁰ We chose the steel industry for a second application of this model type primarily because a major regional modeling project at RFF was using the Delaware Estuary Region, which has five steel mills, as a case study area.¹¹

We were impressed with the magnitude of the steel industry's residuals problems. For example, iron and steel production has been ranked fourth out of eighteen major industrial categories in its annual particulate emissions, which amounted to approximately 10 percent of the 1966 national total of particulate emissions. This is just slightly below fuel combustion; crushed stone, sand, and gravel; and operations related to agriculture.¹² In addition, approximately 33 percent of the volume of national industrial waste-water discharge emanates from the basic metals industrial category,¹³ with gross water use in iron and steelmaking amounting to as much as 40,000 gallons per ton of finished steel.¹⁴

The steel industry's residuals problems are highly interconnected with its choice of raw materials—particularly its choice between hot iron (from blast furnaces using coke and ore) and steel scrap. This choice, in turn, depends primarily on the relative costs of the two inputs. Since steel scrap, particularly from junked cars, is a consumption residual with serious environmental amenity effects, pollution at. the mill and dispersed, visual pollution are linked through the scrap-hot metal tradeoff. This connection seemed well worth further study.¹⁵

We also wish to address problems raised by studies appearing from the federal government purporting to establish a basis for effluent guidelines. In these studies, we were disturbed by:¹⁶

1. The faulty conceptual basis of many of the studies (for example, their frequent insistence on setting effluent standards by process unit and ignoring the possibility that the millwide totals could vary widely under this system).
2. Their relative inability to deal with changes in initial residuals loads and the costs of meeting effluent standards implied by changes in exogenous conditions.
3. The wide variation in reported costs per unit of output.

These problems are exactly the sort with which the linear programming method can deal effectively, so we believe that our study promises policy relevance as well as intellectual rewards.

We are not, of course, the first to propose a study of one facet or another of the steel industry from the outside. On the one hand, the patterns of technological change in the industry have fascinated economists, and any number have made efforts to explain, attack, or defend the industry's actions in the past.¹⁷ A separate set of research efforts has dealt with steel mill residuals problems, but this research has never been done in the context of a programming model, and more important, has never involved simultaneous consideration of process and input-mix changes. In addition, each study has focused on a single environmental medium (air or water) and often on a single residual as well.¹⁸

Thus, our specific goals in setting up the steel industry model were the following.

1. As far as possible, the model should include the major air- and waterborne residuals and solid wastes discharged from steel mills.
2. It should also reflect the available options for changing residual type and discharge medium.
3. It should allow us to explore the effect of energy cost on energy use and on residuals discharge, and conversely should show us how environmental policies and other exogenous factors affect energy use in steelmaking.
4. Similarly, the model ought to allow us to investigate steel scrap use in new steel production, both to find out how quantities and qualities of scrap demanded respond to scrap prices, available technology, and other influences, and, on the other hand, to see how variations in scrap use affect residuals discharges and resource use (especially iron ore and coal).
5. The model should be set up to allow the representation of specific mill types, as differentiated by steel furnaces [that is, basic oxygen furnace (BOF), open hearth (OH), electric arc furnace (EA), and a duplex shop consisting of both BOF and EA capacity],
6. The model should include some version of the finishing section, which is a large source of residuals, but was commonly ignored in the studies available to us when we began.

A further aim is implied in the a...

Table of contents

• Cover Page
• Half Title page
• Full list of titles in the set
• Title Page
• Copyright Page
• Original Half Title page
• Original Title Page
• Original Copyright Page
• Resources for The Future, Inc.
• Contents
• List of Tables
• List of Figures
• Preface
• Acknowledgments
• Part I Overview of the Steel-Making System and the Model
• 1 Policy Questions and Methodology
• 2 An Overview of Steel Technology
• 3 By-product Coking
• 4 Sinter Production
• 5 Iron Production: The Blast Furnace
• 6 Principal Steel-Making Practices
• 7 The Finishing Section
• 8 Particulate Emission Control
• Part II Analytical Application of the Model
• 9 The Bench Mark Case: Some Results and Comparisons
• 10 Indirect Influences on Residuals Generation and Discharge
• 11 Direct Influences on Residuals Generation and Discharge
• 12 Policy Applications and New Directions
• Abbreviations
• Glossary of Steel-Making Terms
• Table References
• Index