IISI iron steel database d5710975-d600-11da-a94d ......Worldwide LCI Database for Steel Industry Products 1 CONTENTS EXECUTIVE SUMMARY.....3

Worldwide LCI Database for Steel Industry Products

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CONTENTS

EXECUTIVE SUMMARY...............................................................................................................................3

CHAPTER 1: INTRODUCTION ....................................................................................................................7

CHAPTER 2: GOAL, SCOPE, TERMINOLOGY AND DEFINITIONS .........................................................8

2.1 GOAL ...................................................................................................................................................8

2.2 SCOPE .................................................................................................................................................8

2.2.1 Function and functional unit.........................................................................................................8

2.2.2 System boundaries......................................................................................................................9

2.2.3 Process Routes, Technology Coverage....................................................................................10

2.3 ROUTE/SITE/MODULE .........................................................................................................................10

2.4 DATA CATEGORIES .............................................................................................................................11

2.4.1 Waste/Recovered Material ........................................................................................................11

2.4.2 Air and Water Emissions ...........................................................................................................12

2.4.3 Energy Reminders.....................................................................................................................13

CHAPTER 3: DATA QUALITY, GEOGRAPHICAL COVERAGE AND CRITICAL REVIEW....................15

3.1 DATA SOURCES AND STEEL PLANT QUESTIONNAIRES ...........................................................................15

3.1.1 Site Data ....................................................................................................................................15

3.1.2 Upstream Data ..........................................................................................................................15

3.2 DATA QUALITY ....................................................................................................................................16

3.3 CUT-OFF RULES..................................................................................................................................16

3.4 DATA PRECISION, COMPLETENESS, AND CONSISTENCY ........................................................................17

3.5 CRITICAL REVIEW................................................................................................................................17

3.6 GEOGRAPHICAL COVERAGE AND AVERAGING .......................................................................................18

CHAPTER 4: METHODOLOGICAL ASSUMPTIONS AND ALLOCATION PRINCIPLES .......................21

4.1 UPSTREAM INVENTORIES.....................................................................................................................21

4.1.1 Ferrous Scraps ..........................................................................................................................21

4.1.2 Electricity ...................................................................................................................................21

4.1.3 Iron ore and coal........................................................................................................................22

4.1.4 Intermediate products from external supply ..............................................................................23

4.1.5 Other raw materials ...................................................................................................................23

4.1.6 External transportation ..............................................................................................................24

4.2 ALLOCATION .......................................................................................................................................25

4.2.1 Partitioning.................................................................................................................................25

4.2.2 Multi-function Systems ..............................................................................................................25

4.2.2.1 EAF Route .............................................................................................................................28

4.2.3 Conclusion .................................................................................................................................29


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4.3 WASTE TREATMENT ALLOCATIONS.......................................................................................................29

4.4 FLARES ..............................................................................................................................................29

4.5 PACKAGING MATERIALS AND INTERNAL TRANSPORTATION...........................................................30

CHAPTER 5: INTEPRETATION.................................................................................................................31

5.1 CONTRIBUTION ANALYSIS..........................................................................................................31

5.2 CONTRIBUTION ANALYSIS BY ARTICLES .....................................................................................31

5.2.1 Carbon Dioxide (CO2) ....................................................................................................31

5.2.2 Nitrogen Oxides (NOX) ...................................................................................................31

5.2.3 Total Particulates............................................................................................................31

5.2.4 Sulphur Oxides (SOX).....................................................................................................32

5.2.5 (w) Suspended Matter ....................................................................................................32

5.2.6 Total Waste ....................................................................................................................32

5.2.7 Total Primary Energy......................................................................................................32

5.3 Sensitivity Analysis .................................................................................................................32

5.3.1 System Expansion vs. No Allocation ............................................................................32

5.3.2 Coal and Electricity .................................................................................................................30

CHAPTER 6: DATA SHEETS EXPLANATIONS .......................................................................................34

6.1 DATES......................................................................................................................................34

6.2 STATISTICS...............................................................................................................................34

6.3 LCI FLOWS/ARTICLES ...............................................................................................................34

CHAPTER 7: CONCLUSIONS ...................................................................................................................41

ACRONYMS AND ABBREVIATIONS........................................................................................................42

LIST OF TABLES........................................................................................................................................43

LIST OF FIGURES......................................................................................................................................44

APPENDICES .............................................................................................................................................45


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EXECUTIVE SUMMARY

IISI Life Cycle Inventory Study for Steel Industry Products

Introduction Selecting the most appropriate materials for any application depends on the consideration of a range of technical and economic factors including, for example, functionality, durability and cost. A further and increasingly important factor for material specifiers in a world where sustainable development is a key issue is the associated environmental performance of material applications both from a manufacturing and product performance perspective. Among the tools available to evaluate environmental performance, Life Cycle Assessment (LCA) provides a holistic approach to evaluate environmental performance by considering the potential impacts from all stages of manufacture, product use and end-of-life stages, sometimes called the ‘cradle-to-grave’ approach. LCA generally comprises four major components:

- Goal and scope definition; - Life Cycle Inventory - data collection and calculation of an inventory of materials, energy and

emissions related to the system being studied; - Life Cycle Impact Assessment - analysis of data to evaluate contributions to various

environmental impact categories; and - Interpretation - where data are analysed in the context of the methodology, scope and study

goals and where the quality of any study conclusions is assessed. A life cycle inventory (LCI) study has been carried out by the International Iron and Steel Institute (IISI) to quantify resources use, energy and environmental emissions associated with the processing of fourteen steel industry products from the extraction of raw materials in the ground through to the steel factory gate. The study was carried for 1999-2000 data by the IISI with technical liaison and co-ordination through an IISI LCA Forum, based on data collated within IISI member companies. LCI data were calculated for products derived via the blast furnace/basic oxygen furnace route (based on iron ore and steel scrap) and the electric arc furnace route (mainly based on steel scrap). Downstream processing into manufactured products, their use, end of life and scrap recovery processes have not been included in the inventory, making this a ‘cradle-to-gate’ study. The boundaries of the study can be extended to include downstream activities particularly in collaboration with customers who are applying LCA’s to their product systems. Goals

The primary goals of the study were to develop a unified and rigorous LCI methodology for steel products worldwide in accordance with the IISI Policy Statement on LCA and related ISO14040 set of standards to provide reliable data to meet requests from customers and external studies. Further goals were to promote the environmental credentials of steel and to develop steel industry expertise in the subject. Scope

The fourteen products included in the study are the main finished products of the steel industry. They include hot rolled coil (with and without pickling), cold rolled coil (with and without finishing), hot dip and electrically galvanised sheet, painted sheet, tinplate and tin-free sheet, tubes, sections, plate, rebar/wire rod, and engineering steels. The products are of general relevance to a wide range of downstream applications including those in the construction, automotive and packaging sectors.


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Stainless steel products were not included but a separate study has is being carried out to provide data on these; the results of the global study will be available in the near future, whereas the European study is available now. In total, 50 sites operated by 28 companies, including 34 blast furnace operations, 13 electric arc furnace operations, and 3 direct reduction operations participated in the study. The companies contributing data to the LCI study account for 39.7% of global crude steel production outside of the former USSR and China. Companies in Europe, and Far East Asia were well represented and a typical range of operating configurations included, North America is included in the global averages. This level of coverage maintains the IISI LCI study one the most representative LCI studies ever carried out for a material and this provides a sound basis for LCA studies relating to steel. Methodology

The quality and relevance of LCA/LCI results, and the extent to which they can be applied and interpreted, depends critically upon the methodology used. It is therefore important that methodology is transparent and well documented. ISO standards have been developed to provide guidance on methodological choices and to set down rules for transparency and reporting. To date, the relevant ISO standards have been published are:

i. ISO 14040, which sets down the ‘Principles and Framework’ of LCA, ii. ISO 14041, on ‘Goal and Scope Definition and Inventory Analysis’. iii. ISO 14042, on ‘Life Cycle Impact Assessment’, and iv. ISO 14043, on ‘Life Cycle Interpretation’.

The goal of the IISI project was to produce LCI’s with sufficient scope to facilitate the range of emerging impact assessment methods in future studies. The IISI LCI study has been fully reported in accordance with ISO 14040 and ISO 14041 and has undergone critical review from an independent Critical Review Panel (CRP) of LCA specialists. This approach improved the integrity of the study and can help guide methodology. The full CRP Report is included in the report. The main CRP conclusions were: “The International Iron and Steel Institute has done a commendable job in the planning, design and implementation of the IISI Worldwide LCI Database for Steel Industry Products. This database will be a valuable resource for LCA studies involving steel products. We have found this LCI study well constructed and adhering to the requirements of the International Standards ISO 14040, 14041 and 14043 relating to Life Cycle Inventory Analysis (LCI), with a few reservations:

- We missed a listing of data quality requirements as specified in ISO 14040, clause 5.1.2.3, in spite of data quality generally being well documented, especially for the core steel manufacturing processes.

- We did not find the requirement in ISO 14041, clause 6.4.2 to be fulfilled with respect to excluded upstream processes and the treatment of data gaps in the upstream processes. This also implies that we found the sensitivity analyses inadequate to assess and describe the potential effects of these exclusions on the outcome of the study, as required in ISO 14041, clause 5.3.5.

- The interpretation section of the draft report did not conform to ISO 14043, especially with respect to interpreting the results in relation to the goal and scope (applications) of the study.

Our critical remarks should be seen as suggestions for improvement and does not challenge our overall impression of a very thorough and dedicated study, which contributes significantly to the state-of-the art of practical LCI studies.


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We wish to express our gratitude to IISI for providing the opportunity to review this work in detail, and for the constructive atmosphere in which our comments have been received.” Critical Review Report, 25 June 2002. IISI welcomes the comments made by the critical review panel, and is in full agreement with the majority of the critical review panel report. IISI believes that the critical review process was an essential step in the Worldwide LCI for steel products and considers that the report adds value to the methodology report. In response to the CRP’s report, the IISI accepts that due to the large number of upstream modules in the model, not all will have the same allocation procedure and data quality. The standards required by the database owner for the software model are high, allocation procedure is detailed in the information facility of each module used, and is subsequently listed in full in Appendix 6 of the IISI methodology report. IISI accepts that with respect to ISO14041, clause 5.1.2.3, particularly precision and uncertainty, the information is limited due to the fewer datasets available for input to the study, but otherwise we consider this to be adequate for the purpose of the study, and upstream data (mainly from DEAM) quality information is available. Upstream data sourced directly by IISI (e.g. iron ore) is consistent with the data quality of the primary data for steel processes. In view of the comments made on the completeness of the interpretation, the IISI believes that the whole report contributes to the interpretation phase, and that the report has been enhanced to cover aspects of interpretation covered in ISO14043. Overall, we believe that the results have been analysed, and the limitations of the study explained, to provide the transparency and confidence that these data are adequate for LCA’s using steel. Results

The LCI results provide ‘cradle to gate’ (of the steel factory gate) data on all the major raw materials, energy usage, air and water emissions, and wastes for each of the fourteen steel products included in the study. In total about 450 data categories (flows) have been quantified but for simplification 43 major flow categories are presented in summary tables for communication to third parties. These flows include average values for air and water emissions that were fully accounted in the data collection exercise. Other emissions data were included but site measurements and/or upstream data quality on these emissions were thought to be insufficient to generate reliable averages. Both worldwide and regional averages (Western Europe, Far East Asia and Rest of the World) are available provided that a minimum of three sites contributed data for that product. Additional information includes the number of sites contributing to the average and minimum, maximum and variance for each LCI flow. These data indicate the variation across individual sites and can be used to facilitate sensitivity analyses. The LCI results aggregate the contributions of between 150 and 250 process units depending on the product. IISI intends to pursue continuous improvement of the data quality with time. This will include further updates of the data and expanding the range of reliable data categories as measurement techniques become more widespread. Further efforts will also be made to acquire data for upstream operations directly from suppliers, making the data more regionally relevant and reducing the dependence of results upon generic databases. Availability of Data

LCI data can be obtained via the online request facility on the IISI website, which in turn informs the relevant LCA Managers within member companies and organisations across the world. Contact names can be obtained from IISI. The normal procedure is to complete a questionnaire describing the intended application of the data and to discuss this with the LCA Manager. This will help to ensure that the IISI methodology and results can be applied appropriately and will be compatible with the goals of the study.


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Conclusions

The IISI LCI study has generated one of the largest, most rigorous and representative databases of any material. The results can be used reliably to assist decision-making and for evaluating the performance of steel products in the context of sustainable development. The results also provide the opportunity for steel companies to benchmark and evaluate improvement measures to their processes and product systems. Steel industry expertise has been enhanced by involvement in the study and the industry is now better equipped to provide technical support to customers and users of steel on LCA issues. The program of the IISI LCA Forum (launched after the first study) to keep the database up to date and further enhance the methodology and understanding of the study has been successful in raising the profile of LCA within the steel industry, and to its customers. Recommendations for improvement concerning both the documentation and the data will be highly welcome. For LCA to be used as reliable tool for decision making high quality data, sound methodology and transparent reporting are essential. This study is a major step towards enhancement of these standards and the steel industry intends to continue and encourage this trend in its future programme of work. For further information, please contact: LCA Manager International Iron and Steel Institute Rue Colonel Bourg 120, 1140 Brussels, Belgium Tel. (direct): +32 (0)2 702 89 32 Fax. +32 (0)2 702 88 99 Email: [email protected] See also: http://www.worldsteel.org


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CHAPTER 1: INTRODUCTION

This report presents a summary of the second Worldwide IISI LCI Study. It provides an explanation of the methodology, results and interpretation of the LCI data for steel products. The study was originally carried out for 1994/1995 data and as part of the IISI ongoing commitment to improving data quality has now been updated for 1999/2000 data.

The main goal of the study was to provide high quality LCI data for steel products on a global and regional basis. It is believed that other data sets on steel have been derived with limited accuracy or representation and/or contain out of date information. The IISI data contains data on process operation in 1999/2000 collected at individual sites with a universally applied methodology for data collection and LCI calculation.

Whilst the report aims to describe the details of the LCI methodology, further details on the steel industry processes are available from other publications (available via the IISI website www.worldsteel.org), for example, the IISI/UNEP report “Steel Industry and the Environment, Technical and Management Issues, 1997”, and IISI Committee on Technology reports “Energy Use in the Steel Industry” which provides good technical references and specific information on environmental issues.

Although this report features a comprehensive level of detail, it is intended to serve as a basis of dialogue between steel industry representatives and third parties using the data. Recommendations of improvement concerning both the documentation and the LCI data are highly welcomed. They will be considered as the IISI LCI database is improved in future.

The IISI LCI study has been undertaken in accordance with the ISO14040 set of standards, and in this respect has undergone critical review from an independent panel of specialists, the critical review panel (CRP), to comment on the methodology and reporting of the study. This approach improved the integrity of the study and helps establish transparency. The final CRP report is included in this report.


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CHAPTER 2: GOAL, SCOPE, TERMINOLOGY AND DEFINITIONS

2.1 Goal

LCA continues to be a topic of growing interest to the steel industry, as well as other industries, and independent LCA studies have been conducted by several steel companies and regional steel associations, mostly relating to packaging, construction and automotive applications. However, as these studies were different in purpose, system boundary and methodology, in 1996 the Board of Directors of IISI initiated the original global ‘LCI on Steel Industry Products’ in order to avoid inconsistency and duplicated effort. The update for 1999/2000 data follows the same criteria,

The goals of the project were to:

- To develop the common worldwide methodology for cradle-to-gate steel product Life Cycle Inventories (LCIs) from the original study for the successful update study.

- Produce worldwide LCI data for steel industry products.

- Support communication with industry stakeholders.

- Assist industry benchmarking and environmental improvement programmes.

This aimed to subsequently form the basis for full LCAs, including life cycle impact assessment, across broader boundaries and complete product life cycles.

The methodology was defined in compliance with the IISI Policy Statement on LCA and with ISO standards relating to LCA as described in the following sections.

2.2 Scope

2.2.1 Function and functional unit

Within the scope of this study, the system function is the production of a steel product at the

factory gate. Further functions relating to the generation of by-products from the steel production system have been eliminated using the allocation procedure recommended in ISO 14041 as documented in Section 4.2.

The functional unit, which enables the system inputs/outputs to be quantified and normalised, is one kilogram of steel product at the factory gate.

Fourteen steel products (see Table 2-1) were included in the study. The detailed specifications of each steel product, such as size range, gauge and coating thickness, vary from site to site and are a function of the technology, equipment and product ranges at the sites involved. The range of specifications within a product category will to some extent influence the regional and global LCI ranges. A more detailed correlation between the LCI results and product specifications is the subject of ongoing work, but is outside the scope of this study. In particular, “Rebar & wire rod” (referred to as “bars”) were grouped together to rationalise the number of spreadsheets and to help accumulate data for products with similar processes.


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Product Category Manufacturing route List of products

Long products Blast furnace route

and

Electric arc furnace route

Sections

Rebar/ Wire Rod

Engineering Steel

Flat products Blast furnace route

Plate

Hot Rolled Coil

Cold Rolled Coil

Pickled Hot Rolled Coil

Finished Cold Rolled Coil

Electrogalvanised

Hot-dip Galvanised

Tin-free Steel

Tinplated Products

Organic Coated Flats

Welded Pipes

Table 2-1: List of Products covered by the Study

The study focused on carbon and low alloy steels (with alloy content lower than 2 %). Notably stainless steels (with at least 12% chromium) were outside the study scope, but again form the basis of another study.

2.2.2 System boundaries

The study is a <cradle-to-gate> LCI study. That is, it covers all of the production steps from raw materials <in the earth> (i.e. the cradle) to finished products ready to be shipped from the steelworks (i.e. the gate). It does not include the manufacture of downstream products, their use, end of life and scrap recovery schemes.

Figure 2-1: System Overview

Raw materialand energyproduction(includingextraction)

Consumablesproduction

Steelworks

Site boundaries

System

Steel productsTransportation

Natural resourcesfrom earth

Emissionsto earth

Savedexternal

operations

By-products

EquivalentBy-productfunctions

minus

Recoveryprocesses

Merchant scrap,other steelworks,

etc.

Scrap

Non allocated

By-products


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As shown in Figure 2-1, the steel product manufacturing system encompasses the activities of the steel sites and all major upstream processes, including the production and transportation of raw materials, energy sources and consumables used on the steelworks. Certain upstream stages, which have a negligible contribution to the resulting LCI, were excluded (see section 3.3).

In addition the recovery and use of steel industry by-products outside of the steelworks are taken into account using in most cases the method of system expansion as described in Section 4.2.

Externally supplied scraps are sourced from merchants, other factories and municipal facilities. As indicated in Figure 2-1, no upstream burdens from scrap recovery or treatment are included except transport from the latter sources. No allocation procedure for recycling situation was applied to the use of scraps but the net input is quantified in the data sheets to accommodate the extension of system boundaries in future studies for individual products. The issue of scrap allocation is further discussed in Appendix 5.

2.2.3 Process Routes, Technology Coverage

Steel is produced predominantly by two process routes; the blast furnace route and the electric arc furnace route (the BF and EAF routes respectively).

The BF route is primary ore based with up to 25 % scrap input and the steelmaking stage of this route is carried out using the basic oxygen furnace. The EAF route is predominantly a 100% scrap based steelmaking process. Both routes continuously cast products that feed into hot and cold rolling processes. Cold rolling together with coating and finishing processes for flat products are termed here the ‘cold rolling route’. Flat products are produced predominantly from the BF route whilst long products are produced from both the EAF and BF routes. Table 2-1 indicates that no flat products from the EAF route were included in the study; however, this is a growth area (e.g., North America), and as such was included in the scope of the study, however these data were not included in the averages due to the low number of sites.

Other emerging process technologies include direct reduction of ores that replace the BF route (such as Midrex), these technologies represent a small, but growing contribution to world production, and for this reason are also included in the scope of the study, but no averages were calculated due to the low number of sites.

Other steel making technologies such as the open-hearth process (being around 4% of world steel production in 1999) and ingot cast steel products (being less than 16% of world steel production in 1999) were not included. Open-hearth steelmaking and ingot casting technologies are declining for both economic and environmental reasons and tend to be used only in the former eastern block and China.

2.3 Route/Site/Module

Terminology has been developed for the various system components as follows. ‘Route’ refers to the full cradle to gate system including upstream supplies, transport and by product credits. ‘Site’ refers to the steelworks boundaries. ‘Modules’ are the component unit processes within the ‘site’ and the ‘route’. A ‘Module’ corresponds either to one of the main process stages and its associated ancillary workshops (e.g. coke oven battery, coke gas scrubbing and by-products plant) or to a common utility to the site (e.g. a power plant producing electricity and steam). The latter formed the basis for the site questionnaire design that identified specific inputs/outputs for each process stage. A representation of the Basic Oxygen Furnace module is given in Appendix 2. Similar flow diagrams were developed for each site module and used in the questionnaire manual.

The modular structure at the site level is required for the LCA calculation because most sites import and/or export intermediate materials (such as coke, hot metal, slabs, etc.). It also


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facilitates data/error analysis and can assist with the potential application of results for benchmarking and environmental improvement.

Overall, the LCI results aggregate the contributions of between 150 and 250 process units depending on the product. Information on the contributions can be made available on request.

Primary data was collected for 20 separate steelmaking process steps (Table 2.3-1 shows the break down), plus water intake, effluents, stockpile emissions, energy, transport, and Fe-C content of flows.

Process Stage Number of

Processes

Process Stage Number of

Processes

Coke making 28 Engineering Steels 6

Sinter making 25 Seamless/Welded Pipe 5

Blast Furnace 29 Pickling Plant 23

Direct Reduced Iron 2 Cold Rolling Mill 26

Basic Oxygen Furnace 29 Annealing & Tempering Mill 26

Electric Arc Furnace 14 Electrogalvanising 11

Hot Strip Mill 24 Hot-dip Galvanising 19

Section Mill 10 Tin-Free Mill (ECCS) 6

Heavy Plate Mill 11 Tinplate Mill 10

Rebar 10 Organic Coating Line 11

Total Processes 325

Table 2.3-1: Number of process stages represented in the study

The steel product manufacturing flow diagrams via Blast Furnace Route, Cold Rolling Route and Electric Arc Furnace Route are shown in Appendix 1.

2.4 Data Categories

The LCI study set out to include all significant inputs and outputs from the steel production route so that any future studies could consider a range of impact categories. Thus all major materials and energy inputs, air and water emissions and solid wastes were included. Notably emissions to soil, which can relate to contaminated land issues, were not included. The methodological aspects for key data categories are discussed below.

2.4.1 Waste/Recovered Material

Material disposed of in landfills, both internal and external to the steel works, and incinerated materials have been classified as waste.

For allocation and material balancing purposes materials recovered within the ‘site’ were identified in the questionnaire and treated in calculation as negative flow inputs at the module where they arise and as positive inputs to the modules where they are recovered/consumed. This rule included the treatment of recovered internal scrap. With the exception of scrap and process gases, the net balance of these internally recycled materials is generally small.


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Finally, materials exported from the site for external applications have been classified as by-products (also terms “by product” or “recovered matter”).

Some materials are partly waste and partly by-products. In such cases, the ratio between by-products and waste was identified in the questionnaire for site data collection, and a summary can be found in section 6.3. Allocation procedures were applied only to the by-products.

2.4.2 Air and Water Emissions

A list of all known air and water emissions was defined and drawn up for each process stage and included in the site questionnaires for data collection. Because techniques of measurement are more advanced for some sites than others, the combined list of ‘known’ emissions was more extensive than the typical emission monitoring data collected routinely at any one site.

Thus because of limited available data, certain emissions had too few data sets to provide reliable average data. To distinguish between emissions that are known to exist and those which have reliable data for global averaging a set of ‘accounted emissions’ has been defined for both air and water. These include emissions for which most sites have data and for which calculations, based on reliable site averages, could be used to insert values in the few questionnaires where data were missing.

The list in Table 2.4-1 has been developed for this study to include the significant emissions for global warming, air acidification, eutrophication indices, a more comprehensive selection of heavy metals, and emissions of minor interest for LCA studies. Further explanation of the air and water articles can be found in section 6.3.

Non-accounted emissions were those with too few data sets to apply reliable averages to sites with missing data, but which comprise of important emission categories. As measurement techniques become wider spread, reliable averages can be derived and applied in future. Another factor was that upstream data modules used in the LCI calculation did not necessarily contain all the air emission flows included in the steel plant questionnaires. Hence, even though steel site data may be complete, the upstream data may not take full account of all the air emission categories. A list of non-accounted emissions with minimum and maximum values can be obtained on request but for the reasons given the ranges are not considered reliable.

Regarding water emissions specifically, when recorded in the questionnaires, the pollutant amounts in the intake were subtracted from the pollutant amounts in the discharged wastewater because they are not attributable to the steelmaking processes. For some sites located downstream of urban and industrial areas, the outflow water is purer than the intake. However, there are many gaps for this category of data for which it is not possible to calculate an estimate. Therefore, the values of waterborne emissions are potentially overestimated in terms of net emissions.


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Accounted

Emission

Original Study

1995

Updated Study

1999-2000

Greenhouse Gases

CO2 CO2, CH4, N2O, HFC’s, PFC’s, SF6

Acidification Gases

NOX, SOX as SO2 NOX, SOX as SO2, HCl, H2S

Organic Emissions

Dioxins

VOC’s (excluding methane)

Metals Cd, Cr, Pb, Zn

Air

Others CO, Particulates (Total) CO, Particulates (Total)

Metals Cr, Fe, Zn, Pb, Ni Cr, Fe, Zn, Pb, Ni, Cd

Water Others Cl-, F-, Phenols, CN-, N (except ammonia), P matter, Phosphates, COD, S2-, NH4+ (as N), Suspended Matter (unspecified)

N (except ammonia), P compounds, Ammonia, COD, and Suspended Matter.

Table 2.4-1: List of accounted air and water emissions

2.4.3 Energy Reminders

The primary components of a Life Cycle Inventory are the material inputs and outputs that are taken from or are emitted to earth. Certain material inputs, particularly fuels such as coal, oil etc constitutes energy as well as mass inputs, which can be calculated based on calorific value. Within the LCI data sheets, these accumulated energy values are indicated in a separate section in order to facilitate energy analysis and to remind analysts that these values are derived from material inputs and are not “in addition” to them. The section is referred to as Energy Reminders. Energy reminders can be considered to be outside the normal scope of an LCI; however, IISI included these categories to assist with data verification and interpretation purposes.

Within Energy Reminders the calculated energy indicators have been based on net (low)

calorific values and included the following:

- Total primary energy: this is the sum of all energy sources which are drawn directly from the earth, such as natural gas, oil, coal, biomass or hydropower energy. The total primary energy contains further categories namely non-renewable and renewable energy, and fuel and feedstock energy. These are described below:

- Non-renewable energy: includes all fossil and mineral primary energy sources, such as natural gas, oil, coal and nuclear energy.

- Renewable energy: includes all other primary energy sources, such as hydropower and biomass.

- Fuel energy: is that part of primary energy entering the system which is consumed.


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- Feedstock energy: is that part of the primary energy entering the system which is not consumed and/or is available as fuel energy and for use outside the system boundary. In the case of steelmaking, this includes the calorific value of energy of the outputs (such as that contained in products, recovered materials and waste) as well as fuel losses. In practice, feedstock energy from waste and fuel losses were omitted.

The sum of fuel and feedstock energy, as well as the sum of renewable and non-renewable energy always equates to the total primary energy.

Practically, the steel product feedstock energy is low compared to primary energy values since the calorific value of steel was assumed zero and the by-products feedstock energies were accounted for by allocation procedures relating these to primary energy.

The definition of fuel energy within the LCI Study covers all the energy that is spent for process purposes, either to produce heat, mechanical energy or to enable endothermic chemical reactions to take place. Thus, that proportion of the coke and blast furnace injectants, such as natural gas, coal and oil, which are used as reducing agent are included in fuel energy.

Within the study, the primary energy calculation is based on the following parameters:

- Net caloric values for fossil, mineral and biomass materials,

- Gravitational energy for hydropower: 1.11 MJ of gravity energy yields 1 MJ of electricity,

- Burn-up rate for uranium ore: 7.92 10-3 g of uranium ore, equivalent to 3.19 MJ of primary energy, yields 1 MJ of electricity.


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CHAPTER 3: DATA QUALITY, GEOGRAPHICAL COVERAGE AND CRITICAL REVIEW

3.1 Data Sources and Steel Plant Questionnaires

3.1.1 Site Data

Site data were collected on custom designed and developed questionnaires that were formulated through meetings between Ecobilan and IISI. Questionnaires were organised into process stages as defined in Appendix 1 and ancillary utilities such as power plants, compressors, common effluent and waste treatment plants, each of which contained lists of material and energy inputs, air and water emissions, wastes, products and by-products and recovered material categories. A pre-screening exercise took place, involving the ranges from the original study, to obtain typical data values in order that the site questionnaires could include algorithms to ‘flag up’ out-of-typical-range data. A training manual was developed to describe the procedures for entering site data in accordance with the methodological rules. Questionnaires also contained an iron and carbon balance facility for further verification at the site level.

Data sources were defined in three categories F, L and O to be entered in the questionnaires. These were:

- Factory: Site-specific measured or calculated data.

- Literature: value based on literature information.

- Other: data obtained from other site sources e.g. data extrapolated from other steel sites.

Each of these data points is then categorised in terms of data quality, by type (Measured, Calculated, an Average, Estimated, or Unknown), and by date (2001-1999, 1998-1994, 1993-1989, <1989) and scored respectively. Further explanation of data quality is detailed in section 3.2.

Statistics on these data sources and scoring are shown in Appendix 3.

3.1.2 Upstream Data

Data for processes outside of the steel industry, e.g. upstream and by-product recovery operations were generally obtained from professional LCI databases and literature sources. The quality and transparency of methods to derive these data are less well defined than the steel industry specific data in terms of reliability, geographical relevance, methodology and completeness.

In order to avoid potential data quality problems arising from this, the IISI acquired data for those upstream processes that were judged to have a significant contribution to the global LCI results. Data were collected directly from representative sites for iron ore mining, pellet making, limestone quarrying, lime production and sea transport for the original project, and iron ore mining, pelletising (forming additional LCI’s), coal mining, and other steelmaking additions for the updated project.

Appendix 4 summarises the list of upstream modules that have been used. The main assumptions concerning those upstream models are described in section 4.1.


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3.2 Data Quality

The data collection methodology featured extensive data quality requirements, in order that the goal of the study could be satisfied in a quantitative manner, and in accordance with the ISO 14040 standards.

Five data quality categories were specified for each data entry in the steel plant questionnaires as follows:

- Measured: flow where values have obtained from continuous or spot measurements on site. For instance, continuous measurements may include the total electricity consumed, which is readily available from electricity meters, or the coal consumption, measured at the weighbridge or using some other form of stock accounting. Spot measurements may include VOC emissions measured quarterly, over the period of a few hours, from which the annual value has been calculated.

- Calculated: the flow value has been calculated using some form of empirical ratio (e.g. emission factors, etc.), mass balance or other indirect method. For instance, SOX emissions may have been measured over a period of several years and an emission factor determined and used for the subsequent measurements or CO2 emissions may be calculated based on a carbon balance for a process.

- Averaged: flow value was obtained from an average such as the global averages used as part of the IISI LCI study, or averages from internal information sources on site.

- Estimated: the estimated flow value has been established based on approximations. For instance, the transportation distance of a raw material may be estimated because of a lack of better information.

- Unknown: This data type is only available for data from literature sources where there is insufficient information to classify the data into the previous types.

Data collection related to one-year operation and questionnaires indicated the reference year specific to each data point. The majority of data was derived from the most recent records, primarily covering 1998 to 2000, and in some cases the original data was considered valid for inclusion in the update, and therefore the date indication remained the same.

Statistics on data types and age are shown in Appendix 3.

3.3 Cut-off Rules

Criteria were set out in the original study for the recording of material flows and to avoid the need to pursue trivial inputs to the system. These are outlined below:

1. All non-mass inputs to the process stages were recorded, including energy carriers (heating fuels, electricity, steam, compressed air) and water (recorded by volume).

2. At least 99.9% of material inputs to each process stage were included.

3. Wastes representing less than one percent of total waste tonnage for given process stages were not recorded unless treated outside of the site.

Criterion 2 was attainable because site input tonnages are weighed by relatively few inputs such as iron ore, pellets, limestone, scrap, dolomite, olivine, serpentine, metallic additions, refractories, coke, sinter, hot metal, and intermediate steel products which account for >99.9% of material inputs to each process stage.


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Following the contribution analysis carried out on the original study, the list of site inputs was simplified for the update. All site inputs, which cumulated contributions, represent less than 2% of the data categories in the data sheets were excluded from the data collection.

The criteria for inclusion/exclusion of upstream environmental burden are presented in section 4.1.5.

3.4 Data Precision, Completeness, and Consistency

All questionnaires returned from the sites were checked individually by IISI. Suspected out of range data and important missing information were detected both automatically, using a check programme, and manually, following visual inspection of the data. Where data was missing or suspected to be erroneous the site was contacted until all necessary data was received. When completed, the questionnaires were downloaded electronically into TEAMTM using an interface programme specifically developed to avoid typing and miscalculation (e.g. unit conversion) errors. This procedure ensured that a comprehensive and accurate data set was received from each site and that these data were accurately transferred into TEAMTM prior to LCI calculation.

After carrying out the initial LCI calculation, the results were distributed to the sites in order that extreme values, revealed by the statistical analyses, could be checked and verified or corrected and the energy balance could be checked using the energy reminders described above. Based on the experience from the original study, and the interaction with the sites with the results, the LCI became ever more accurate and robust.

3.5 Critical Review

The methodology, results, and interpretation of the LCI study for 2000 were subjected to a critical review, to ensure that the project was consistent with the ISO14040 standards. The critical review panel for the updated study were Dr. Weidema (2. –0 LCA Consultants, Denmark), Dr. Keoleian (University of Michigan, US), and Dr. Inaba (National Institute of Advanced Science & Technology, Japan). The full critical review panel report is included in Appendix 10.

Study critical review

The conclusion of the critical review panel report was that ”The International Iron and Steel Institute have done a commendable job in the planning, design and implementation of the IISI Worldwide LCI Database for Steel Industry Products. This database will be a valuable resource for LCA studies involving steel products.

We have found this LCI study well constructed and adhering to the requirements of the International Standards ISO 14040, 14041 and 14043 relating to Life Cycle Inventory Analysis (LCI), with a few reservations:

We missed a listing of data quality requirements as specified in ISO 14040, clause 5.1.2.3, in spite of data quality generally being well documented, especially for the core steel manufacturing processes.

We did not find the requirement in ISO 14041, clause 6.4.2 to be fulfilled with respect to excluded upstream processes and the treatment of data gaps in the upstream processes. This also implies that we found the sensitivity analyses inadequate to assess and describe the potential effects of these exclusions on the outcome of the study, as required in ISO 14041, clause 5.3.5.

The interpretation section of the draft report did not conform to ISO 14043, especially with respect to interpreting the results in relation to the goal and scope (applications) of the study.

Our critical remarks should be seen as suggestions for improvement and does not challenge our overall impression of a very thorough and dedicated study, which contributes significantly to the state-of-the art of practical LCI studies.”


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Critical Review - IISI Response

IISI welcomes the comments made by the critical review panel, and is in full agreement with the majority of the critical review panel report. IISI believes that the critical review process was an essential step in the Worldwide LCI for steel products and considers that the report adds value to the methodology report.

In the light of the critical review comments, the following opportunity is taken to address some of the issues, on a case-by-case basis (the full response is found in Appendix 10), in the context of the current IISI position, and for the future use of the study.

In view of the comments made on upstream data quality, IISI, as part of its continuous improvement commitment, will work to obtain and use better upstream data for the worldwide LCI study for steel products.

IISI accepts that due to the large number of upstream modules in the model, not all will have the same allocation procedure and data quality. The standards required by the database owner for the software model are high, allocation procedure is detailed in the information facility of each module used, and is subsequently listed in full in Appendix 6 of the IISI methodology report. IISI accepts that with respect to ISO14041, clause 5.1.2.3, particularly precision and uncertainty, the information is limited due to the fewer datasets available for input to the study, but otherwise we consider this to be adequate for the purpose of the study, and upstream data (mainly from DEAM) data quality information is available. Upstream data sourced directly by IISI (e.g. iron ore) is consistent with the data quality of the primary data for steel processes.

As described above, primary data was pursued for important upstream processes such as coal, lime, and cement but only limited response was received, therefore we agree that the quality of the upstream data could be improved and will be an area for continuous improvement. IISI believes that these data are sufficiently transparent and accurate to justify their inclusion in the study, and to expand the database in the future.

In view of the comments made on the completeness of the interpretation, the IISI believes that the whole report contributes to the interpretation phase, and that the report has been enhanced to cover aspects of interpretation covered in ISO14043. Overall, we believe that the results have been analysed, and the limitations of the study explained, to provide the transparency and confidence that these data are adequate for LCA’s using steel.

3.6 Geographical Coverage and Averaging

Geographic Coverage (Original study figures in brackets)

The companies participating in the study produce about 39.7% (38.9%) of global steel production excluding the former USSR and China and the contributing sites are among the largest of the principal producer countries, representing 7 out of the 10 top steel making companies in the world. At regional level the participating companies produce 60% (44%) of European, 72% (69%) of Far East Asian and 13% (24%) of North American steels. The list of participating companies is shown in Appendix 9.

50 (55) sites located in 20 (17) countries participated in the study (see Table 3.6-1). The sites represented account for 16% (17%) of the total worldwide crude steel production and 21% (22%) when excluding former USSR countries and mainland China. The total production of the participating countries exceeds 53% (55%) of the world total crude steel production. The highest level of representation is for Western Europe (EU15) with 36% (45%) of the EU15 crude steel production followed by Far East Asia with 34% (34%).


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Western Europe

EU15

Far East Asia Rest Of The World

(Including N. America)

Blast furnace/rolling and coating/

Integrated sites

20 (29)

8 (7)

6 (2)

Electric arc furnace sites

8 (7)

3 (3)

2 (1)

Direct Reduction 0 (0) 1 (0) 2 (0)

Table 3.6-1: Number of Contributing Sites per Region (Numbers in brackets indicate the original

study).

Western Europe

EU15

Far East Asia

World

Total Crude Steel Production (IISI data)

(Million metric ton)

163 (156)

166 (138)

847 (752)

excl. former USSR and China: 630 (578)

Crude steel production of

participating sites

(Million metric ton)

58.0 or 36%

(63.1or 41%)

55.6 or 34%

(46.4 or 34 %)

132 (126)

Table 3.6-2: Part of Total Crude Steel Production covered by the Study (Numbers in brackets

indicate 1995 data).

The groups defined for reporting of regional statistics include: Western Europe (EU15), Far-east Asia (Japan, Korea, and Taiwan), and Rest-of-the-World (includes Canada, Czech Republic, Mexico, Slovak Republic, South Africa, Turkey, and US). This group includes the North American sites, as there were too few to calculate a separate average. Regional LCI averages are considered as particularly appropriate since (carbon) steel products are traded mainly at the regional level.

Although the study reached a remarkable level of representation, the sampling of sites could be expanded for specific products such as seamless and welded pipe, and for process steps, such as DRI.

Averaging

Averages have been calculated straight (without weighting the contribution according to production tonnage) and vertically (i.e. LCI’s are calculated for each site, and the resulting values averaged across the contributing sites).

It was considered that weighted average according to production tonnage was not appropriate since not all steel plants were represented. Furthermore, straight averages facilitate interpretation and benchmarking exercises. In general, it is believed that the sensitivity of the results to the averaging options is low, especially compared to other sources of uncertainty such as the upstream models.


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Some care is needed in interpreting the data differences associated with products from subsequent and parallel process stages because the sample size of sites become fewer as the products become more complex. Thus averages can be derived from different sample sets.

Horizontal averaging was enabled in the event that the sample of sites for a product was not representative enough, however all products studied in the update allowed averages to be calculated.


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CHAPTER 4: METHODOLOGICAL ASSUMPTIONS AND ALLOCATION PRINCIPLES

The following sections explain the basis of calculation of the LCI’s particularly the allocation rules chosen and the integrity and sensitivity of the upstream data modules.

4.1 Upstream Inventories

This section describes the main assumptions concerning the upstream models that have been used in the study.

The list of upstream modules is shown in Appendix 4 with information on the data sources and relevant information.

4.1.1 Ferrous Scraps

No upstream data for scrap collection, sorting and processing was included in the study. However, for external scraps the environmental burdens associated with the transportation from the scrap merchant, municipal facilities or other factories to the steelworks is included, although generally negligible.

The main reason for omitting scrap burdens was because of the complexity and diversity of scrap recovery and recycling practices for all of the products, which would have extended the boundaries and extended the project time scales. However, this decision accommodates the extension of system boundaries in future studies for individual products.

Similarly, no allocation procedure was applied to the use of scraps for reuse and recycling situations. This was to allow users of the data to apply consistent allocation procedures for scrap inputs and scrap recycling scenarios arising at end-of-life, as recommended in clause 6.5.3 of ISO 14041. The issue of scrap allocation is further discussed in Appendix 5.

4.1.2 Electricity

The grid electricity production associated with individual sites can have a significant effect on the LCI, particularly with regard to CO2 emissions; therefore this was customised for each country. Thus, the proportion of different energy sources used for grid electricity production (e.g. % nuclear, % hydro, % thermal energy by fuel, including coal, natural gas oil, others) was adapted for each country using the reference ‘Energy Statistics of OECD Countries, 1997-1998, 2000 Edition’. Within the United States and Canada, the breakdown of grid electricity production was by region according to the reference ‘Energy Agency Statistics - US Department of Energy, North American Electric Reliability Council (NAERC)’.

The models used for each type of primary energy (e.g. coal extraction, transport and combustion in electricity power plant), however, are standard for the majority of sites (e.g. except Finland, where country specific modifications were sourced) and were sourced from Laboratorium fur Energiesysteme (ETH), Zurich 1996, and Electricité de France 1994. Thus, it is assumed that electricity production from coal, for example, has the same efficiency and environmental burdens worldwide. These approximations must be taken into consideration when interpreting the LCI data.


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Electricity production fromheavy fuel oil

(incl. oil extraction, oil refining inheavy fuel oil, transport andcombustion in power plant)

Electricity production fromcoal

(incl. coal extraction, refining ,transport and combustion in power

plant)

Electricity production fromnuclear energy

(including, uranium ore extraction,transport, refining, nuclear power

plant)

Electricity production fromhydropower

Electricity Breakdownw% of electricty from coal,

x% of electricty from heavy fuel oil,y% of elctricty from nuclear,z% of hydropower electricity)

country specific

Standard for all countries Standard for all countries

Standard for all countriesStandard for all countries

Grid electriicty

Figure 4-1: Grid Electricity Production Model

4.1.3 Iron ore and coal

Iron ore and coal represent the majority of all material inputs to the LCI for all primary steel (Blast Furnace Route) products studied. Therefore, the major iron ore and coal mining companies were contacted to assist in improving the data quality and representation.

Datasheets for iron ore mining, and pelletising operations can be made available by request. Iron ore

Iron ore is delivered to the steelmaking plants either in the form of iron ore fines or in the form of pellet. This depends on the quality of the original ore material and on the operational practices at the steelmaking plants. Pelletising is performed on very fine ores to ensure satisfactory gas permeability in the blast furnace. Similarly, iron ore fines are sintered to obtain an agglomerated product, called “graded sinter”, of suitable size, porosity and strength for charging into a blast furnace.

Whilst sintering always takes place on the steelmaking plants, the mining companies generally perform pelletising, although some steel companies have their own pelletising facilities. Therefore, in contrast with sintering, pelletising was classified as an upstream component.

The upstream model for both iron ore mining and pelletising used within the LCI Study was previously based on site data from a large European mine supplying the steel industry for which detailed information was available. However, as this mine is underground and most ore is now supplied from open cast mines, it was expected that the energy burden for mining would be overestimated and the particulate emissions to air would be underestimated.

The LCI data collected for the mining and pelletising of iron ore covered, 4 iron ore mines, 3 pelletising operations, and 3 combined mining and pelletising sites (6 open mines, and 1 underground). The geographical coverage included 3 European, 3 North American, 1 Far-east Asian, and 2 from Oceania; representing 146.5 million t iron ore production (15% of World production), and 44 million t pellet production (18% of World production).

The contribution to total primary energy use for iron ore mining was found to be small, 0.27MJ/kg ore (0.235MJ/kg ore in the original study for an underground mine). The data for particulate


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emissions was found to be 0.029g/kg ore of particulates (unspecified), higher than the first study, most likely reflecting the data gathered from predominantly open cast mines.

The contribution of total primary energy use for pelletising operations (gate-to-gate) was found to be 1.25MJ/kg pellet. The contribution of total primary energy for cradle-to-gate pelletising operations (i.e. the combination of iron ore mining and pelletising operations) was found to be 1.87MJ/kg pellet.

Iron ore overburden (i.e. non-iron bearing rock resulting from ore processing at the mine, tailings etc) was recorded in the iron ore-mining inventory, appearing in the LCI results as separate from waste, to be consistent with other upstream processes. No information was available concerning end of life use of the ore overburden and this would need to be clear before classification, for example, as a by-product if used for applications such as road building, or as a waste if accumulated at the mine, or as a ‘neutral flow’ if reintroduced to the mine cavity. However, regardless of destination, the ore overburden material is mostly rock which has a relatively minor potential environmental impact. The same methodological choice was also applied to zinc ore mining.

Coal

Major coal producers around the World were contacted for the use of LCI data, however it was not possible to obtain suitable site data for coal mining given the response of only one company. Therefore the 1991 BUWAL model, which is representative of German coal production (85% underground, 15% open cast), was used. This model is not representative of coal production worldwide where open cast mining dominates. To reflect the lower actual quantity of waste produced, the waste tonnage data for this model was adjusted for all countries except Germany from 0.5 to 0.05 kg of waste per kg of coal, based on the 1994 ETH model representative of world-wide coal supply with open mining dominating. (Note that that the predominance of underground mines in Germany may still warrant the higher value).

4.1.4 Intermediate products from external supply

Semi-finished products (continuously cast products at the steelmaking stage) are sometimes imported to the sites from external supply. Since these imports are generally small quantities the LCI of such imports was assumed to be the same as that for material from within the site.

Coke, graded sinter and hot metal can also be sourced from external supply and since these can be substantial quantities these were assigned the global study LCI average data including appropriate transportation.

4.1.5 Other raw materials

For all energy inputs (electricity and heating fuels), the upstream inventories have been taken into account, with the exception of compressed air, as the contribution was found to be negligible, and thus this input was disregarded.

For the other material inputs (except water and scrap which do not have upstream burdens, see section 4.1.1), Table 4-1 shows the percentage of material inputs to site accounted for by upstream modules from earth (or the cradle) for hot-rolled coil, and hot-dip galvanised by the BF route as at least 98% w/w; and for Rebar by the EAF route at least 97% w/w. The increase in upstream production data was a main contributing factor for this high percentage accountability; however, not included in this analysis is the coverage of the upstream modules, and the effect on the LCI of data gaps (although there is confidence in the quality requirements set by the upstream data provider, and the transparent reporting is clear from Appendix 4).


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BF Route EAF Route

Hot Rolled Coil Hot-dip Galvanised Rebar/Wire Rod

A*: Total quantity of mass inputs to steel processes

2.270 kg/kg 2.455 kg/kg 0.142 kg/kg

B*: Quantity of site mass inputs traced to earth

2.257 kg/kg 2.441 kg/kg 0.139 kg/kg

B/A 99.4 % 99.4 % 98.2 %

B/A x 0.98**: Estimated part of actual total mass input with upstream taken into account

98.4 % 98.4 % 97.2 %

Table 4-1: Percentage of Total Site Input Tonnage with Upstream (production) taken into account

* values exclude heating fuel, scrap and water

**Percentage of mass inputs recorded in the questionnaires (see section 3.3)

In addition sites were asked to indicate any materials that were thought to carry significant environmental burden. Overall, since the materials not accounted for represented less than 0.01 g/kg of product in the LCI results, it is believed that all significant materials were captured.

4.1.6 External transportation

The means of transportation and distances for the shipment of the main raw materials (in terms of tonnage) to the steelworks were recorded in the questionnaires and included:

- Rail (electricity and diesel engines distinguished).

- Road.

- Shipping by barge.

- Shipping by freighter.

The models for rail, road and barge transportation came from BUWAL and the model for freighter transport was built using data from Nippon Steel Corporation, to account for the large dry bulk carries normally used for raw materials.

The functional unit for all transport models, except road transportation, is kg.km. For road transportation, the functional unit is 1 litre of diesel oil burnt in a truck engine and the model includes water, lubricant and tyre consumption.

Transportation was included for iron ore, pellet, coal, scrap, limestone, lime dolomite and olivine. These raw materials represent more than 95% (w/w) of the total tonnage of inputs (except water which is not transported).


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4.2 Allocation

With any multi-product system allocation rules must be defined to relate the system inputs and outputs to each of the products. This is particularly important in the case of the blast furnace route, which generates important quantities of valuable by-products.

Several methods are documented in ISO 14041 and ISO Technical Report 14049 (Illustrative examples on how to apply ISO 14041), the principles of which are discussed below, as well as their application to the steelmaking systems.

4.2.1 Partitioning

Where a system generates material products of similar function, inputs/outputs can be allocated in ratio to mass of products. Where systems generate energetic products, such as fuels, allocation can be based upon the relative energy values of the products. This is termed mass or energy partitioning. Other partitioning parameters than energy and mass are sometimes used. For example, the stoichiometric coefficients are sometimes used for chemicals.

If within the system the product routes diverge, then to avoid unrepresentative allocations, relevant inputs and outputs should be obtained for each of the divergent routes and then allocated to the products accordingly. This is sometimes referred to as the avoidance of allocation or partitioning. The expansion of the system boundaries (see next section) is another way recommended in ISO 14041 to avoid partitioning.

Within the IISI methodology, mass partitioning has been used to allocate for the small sample of BOF plants producing ingots, which were not included in the study. Hence, the common BOF plant flows were allocated between ingots and continuously cast products by mass but those inputs specific to the ingot route were not included in the data collection exercise (in practice, the ingot specific flows tend to be minor and sites may not have distinguished).

Similarly, energy partitioning was used for allocation to the various products (electricity, hot water, compressed air, steam and blast air) at power plants within the steel sites. These data categories were collected through the site questionnaires.

4.2.2 Multi-function Systems

Where a system generates products with different functions, allocation procedures become more complex. One such example relevant to the IISI study is the blast furnace. This process stage consumes mainly iron ore, coke and fuels primarily to produce hot metal (mass) but in addition blast furnace gas (energy) is generated and slags (mass).

Blast Furnace (BF) gas is used as a fuel for upstream and downstream processes and can be exported to systems external to the steel production system generally to produce grid electricity. In some countries, BF gas and coke oven gas represent a significant percentage of the total energy consumption for grid electricity generation. On average, 3.9 MJ of BF gas is generated per kg of hot metal, excluding the small quantities that are flared without energy recovery (see section 4.4).

BF slags are mainly used in cement making and road building aggregates. In particular, granulated BF slag (i.e. slag quickly quenched in water) is characterised by a slower but stronger binding power than regular Portland cement. This latter property makes granulated BF slag a high value product of great demand for special applications such as coastal infrastructure. On average, 0.26 kg of BF slag is generated per kg of hot metal. The generation rate, which depends on the raw materials used, can exceed 0.35 kg in some cases. On the sample of participating sites, more than 90 % of the total amount produced is exported for external applications and 60% for cement making.


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Several methods were considered in order of increasing relevance to the IISI study as follows:

No Allocation - This would allocate all inputs and outputs to hot metal and would not attribute any material flows to the production of blast furnace gas or slags. This places a high burden on hot metal and also makes difficult the allocation of energy to upstream and downstream processes that consume blast furnace gas (such as reheat furnaces) since the consumption of blast furnace gas at these units would have no upstream burdens. This would not be satisfactory because product routes diverge downstream of the blast furnace and each route consumes different levels of BF gas and electricity (generated by on-site BF gas-fired generators). Thus this method would give rise to inaccuracies in allocations across the steel production route.

Physical Partitioning - This technique could be applied by splitting the function of the blast furnace into two (or three) sub-systems, each consuming coke, iron ore and fuels to produce one of each product; hot metal, BF gas (and slag). The blast furnace inputs and outputs are allocated between each product in proportion to the mass, in the case of mass partitioning, or feedstock energy, in the case of energy partitioning, of the products. This method would facilitate allocation of blast furnace gas to downstream processes (and allocation to slags) but because hot metal production and blast furnace gas generation are thermodynamically inseparable, this method would be theoretical and have no practical relevance. For example, using mass as the partitioning parameter would be inappropriate for the blast furnace since the mass of gases and slags is relatively high compared to hot metal and, in the case of slag, would be disproportionate to its functional value. This would therefore under-estimate burdens associated with hot metal. The method would also misrepresent the functionality of blast furnace gas (as a mass product rather than an energy product). The results would therefore misrepresent the real allocations (e.g. coke allocation to hot metal would be under-estimated because of the quantity allocated to BF gas production; in reality the systems are mutually dependent). Other possibilities were reviewed, such as the iron and carbon contents, and it was found that no satisfying physical partitioning parameters could be found to allocate the blast furnace between hot metal, the gas and the slags.

This is also valid for the BOF plant generating BOF gas (energy) and some slags or for the other various by-products arising at the downstream operations. One exception is the coke plant for which energy partitioning is acceptable for the main by products (coke oven gas and tar) although this is less appropriate for the remaining by-products such as BTX, sulphurous and nitrogenous compounds.

Economic Partitioning - This technique would partition the system inputs and outputs according to the relative economic values of the three products; hot metal, slag and process gas. Economic methods are not favoured in ISO standard methodology because of the inevitable fluctuations in market value make questionable the long term viability of the results. In addition, economic value of by-products is dependent on local demand/supply situations. Furthermore the international nature of IISI study would have been further complicated by international exchange rates across the participating countries. The lack of scientific basis and the practical difficulties did not make this a viable option.

The “Deducting” Method – (A Type of Energy Allocation)

This method was applied in former LCA studies of steel products.

It consists of deducting the energy value of blast furnace gas output from the energy inputs and allocating the remainder to hot metal. The method would not attribute material flows effectively and would not allocate to slags. Again the method would cause some difficulty in the allocation of energy to upstream and downstream processes that consume blast furnace gas since the production of blast furnace gas at these units would have no upstream burdens. In addition this method would under-estimate the energetic inputs to hot metal production because in practice all inputs are necessary.


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Another alternative consists of deducting a quantity of coke energetically equivalent to the energy value of blast furnace gas output from the total coke input of the blast furnace and allocating the remainder to hot metal. All other inputs/outputs of the blast furnace than coke input are totally allocated to hot metal. Although this alternative would facilitate allocation of blast furnace gas to downstream processes, its physical relevance was also considered poor.

System Expansion Method - This method provided the most consistent solution to avoiding the problems described above and was adopted for the IISI study. Allocation rules are avoided by attributing all system inputs/outputs to the main system function (to produce hot metal) but credits are given to the production of BF gas and slags because their production replaces alternative production of similar functional products. To some extent, this method can be ranked as an “open loop recycling procedure”. Where BF gas is consumed in modules within the system, the burdens of alternative products are then added to the system, offsetting the credits. Where all generated gases are consumed on-site, values of inputs/outputs and emissions equate to the real site values.

The method is cited in section 6.5.3 of ISO 14041 and illustrated in Annex B2 of the same document. It is described as one of the preferred methods since it 'avoids' allocation. The controversy over its usage, however, is in the choice and functional equivalence of the alternative systems selected and great care has been taken to ensure that those selected are consistent with actual practice. For example, BF gas is a fuel with no equivalent means of generation; therefore an assumption has to be made regarding the fuels potentially replaced. The selection of alternative fuels was the subject of sensitivity analyses described in the original study. The decision was taken to assume that energetic gases generated in steel production such as blast furnace gas, coke oven gas and BOF gas, replace the national grid electricity applicable to

the respective country. This assumption is justified in two ways:

- Excess gas exported beyond the system boundaries is usually supplied to local power stations to generate electricity. Generally, the alternative fuel to these gases would be coal, fuel oil or natural gas with usually coal predominant;

- In terms of fuel quality and air emissions it is unlikely that exported gases would be preferred to the cleaner fuels such as natural gas, nevertheless, in some countries (e.g. in Far East Asia, processes gases are utilised for electricity generation).

The system expansion takes account of both the production and the combustion of the fuels compared to that of coal.

In the same way significant material by-products such as BF slags, which are sold to known destinations, are assumed to replace functionally similar products. The list of those by-products and the assumed functional equivalents is shown in Appendix 6. System expansion methodology as applied here is intrinsically related to the economic value of the by-products because of the functional equivalents being replaced. The method however is superior to economic partitioning since it avoids the dependence on market value and exchange rates.

It has additional advantages over partitioning:

- Beyond the issue of steel by-products, this method allows to discriminate between alternative recycling routes from an environmental perspective as “credits” are given for recycling. This reinforces the environmental value of recycling in the industry. Partitioning scenarios do not integrate the actual use of by-products. For example, partitioning applied to BF slags would not consider the actual proportions of slags used in cement making and aggregates respectively whist the environment benefits of saving cement is much higher, in terms of energy resources and air emissions at least, than those associated to aggregates.

- With system expansion, the initial (actual) inventories of the process units are preserved in the results displaying their individual contribution to the overall LCI at


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the route level. This is not the case with partitioning or deducting methods. Therefore these latest methods affect the traceability of the LCI results and consequently the possibilities of interpretation and benchmarking. Similarly, system expansion preserves the mass and element balances of the system, which is not the case with most alternative methods. It is also true for energy although the concept of energy balance is often ambiguous.

- With regards to the LCA calculations, partitioning or deducting methods are extremely complex to apply in the case of steelmaking as several types of iterations are required to definitely eliminate the by products. For example, coke oven gas and other coke by-products are used at the blast furnace which itself is a source of by-products. Therefore, the inventory of hot metal (and BF gas), after blast furnace partitioning, contains several inputs of coke by-products. These inputs must be eliminated by iteration because inversely BF gas is used at the coke plants, etc. This further contributes to the lack of traceability characterising steel LCI results with partitioning or deducting methods applied.

Summary

In this updated study, the previous assumption that the process gases (namely coke oven gas, BF gas, and BOF gas) displace coal was refined.

Each plant defined the proportion of the process gas used for electricity generation, and that used for heat generation. Therefore the quantity of process gas used for electricity generation is assumed to displace the national grid electricity; similarly the quantity used for heat production is assumed to displace the appropriate fossil fuel(s) used at the facility.

It could also be considered that the choice of the replaced energy depends on the goal of the study and particularly to its time scale. For example, marginal means of electricity production would be preferentially applied in the case of (short term) descriptive studies. In contrast, average ‘structural’ models would be preferred for (long term) prospective studies.

The construction of the system expansion method in the TEAM IISI LCI model provides flexibility to replace system expansion scenario with alternative functional systems. However, as shown below, the choice of the energy carriers replaced by the process gases has moderate effects, in most cases, on the final steel product LCI since the gas excess generally represents less than 10 % of the total primary energy at the route level.

4.2.2.1 EAF Route

One notable difference between the BF route and the EAF route is that the by-product credit/debit for the latter is insignificant for most articles, as shown in Appendix 7c,d for EAF bars, and engineering steel. For example, the total credit of the by-products represents less than 1 % of the overall total primary energy for each product.

System expansion was applied to some 90 % of the total mass of by-products generated by the EAF rebar route, and 70% for engineering steel. The remainder is registered in the output “non allocated by-products (total)” and represents 0.01 kg per kg of bars, and 0.05kg engineering steel.

As an exception, the zinc ore values are entirely determined by the credit assigned to the EAF dust. No zinc is used for the manufacturing of bars. The EAF process and the treatment of the off fumes generating the EAF dusts enable the recovery of zinc from galvanised scraps, which saves primary zinc for other applications. Virtually all EAF dusts recovered externally are recovered for their zinc content, mainly through the Waelz process. These dusts contain in average 25 % Zn, as well as iron and other materials, which in contrast to zinc were not taken into account. The model used for the Waelz process is rough. However, these approximations should have minor


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effects on the overall LCI results, as the average export rate of EAF dust is lower than 0.01 kg per kg of steel product.

Finally, some waterborne emissions, namely: zinc, and lead, are significantly determined by the credit of primary zinc and iron ore assigned to the EAF dusts and to the scales respectively.

4.2.3 Conclusion

In conclusion the system expansion method was selected for multi-functional systems since it closely represents the real interactions of steel production routes with the environment and avoids unsound theoretical scenarios. Most importantly, it does not result in favourable or misrepresentative LCI results for the steel industry.

The construction of the system expansion method in the IISI LCI model provides flexibility to analyse and, if necessary, switch off each system expansion scenario and/or replace with alternative functional systems. This facility allows sensitivity analysis of different system expansion scenarios and will facilitate future studies into alternative uses of wastes that may in future be treated to replace functionally similar products.

4.3 Waste Treatment Allocations

Some solid waste/by-product treatment processes are dedicated to a single process stage (e.g. the coke by product plant, BF slag processing and BOF slag processing). These treatment processes were, therefore, allocated to the relevant process stage. Other treatments (termed Common Solid Waste Treatment Process CSWTP) combine solid waste/by-products from different process stages.

Ideally, data for each CSWTP would be collected in a disaggregated form allowing identification of the CSWTP operation undergone by each waste flow in order that the CSWTP operations can be partitioned between the relevant process stages. However data were not available in this form. Moreover, the environmental burdens and material and energy consumption associated with CSWTP are small compared to those of the process stages. Therefore, aggregated data was collected for each CSWTP and environmental burdens were allocated directly to the crude steel.

Air emissions generated by oily waste incinerators were recorded and allocated between the different process stages dependant on their oil consumption.

In reality, the environmental burdens of both the CSTP and oily waste incineration made a negligible contribution to the overall LCI of any steel product.

4.4 Flares

Process gases are sometimes sent to flare stacks and combusted rather than being used elsewhere, due to variations in gas supply and demand and to the availability of gas collection facilities. As the combusted gases disperse without containment, measurement of these emissions is difficult.

Estimates of emission data for flares were either supplied by sites in questionnaires or calculated by Ecobilan. Ecobilan based the flare emission calculations on emission ratios of process gas combustion, except carbon dioxide, which was calculated by carbon balance. The process gas emission ratios were identical to those used for the calculation of missing accounted emission in the process stage modules.

Air emissions due to flaring are usually negligible compared to the process stage air emissions. No manufacturing or economic benefits are realised from flaring, and the associated emissions were allocated entirely to the functional unit of the respective gas source module.


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4.5 Packaging materials and Internal Transportation

Data on the packaging of steel products were requested and supplied in the questionnaires but it was showed that the associated impacts were negligible. For this reason, both the packaging of materials supplied to the steelworks, and the packaging of steel products were excluded from the study. This was justified in the initial IISI study.

The environmental burden of internal transportation was neglected, as a study on a sample of sites in the original study showed an average of 0.001litre/kg crude steel was used, corresponding to about 0.03MJ fuel energy/kg of product.


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CHAPTER 5: INTEPRETATION

The function of this chapter is to evaluate, in accordance with ISO14041, the significance of methodological choices, major upstream modules, and process stages of the LCI results, in terms of a contribution analysis for some identified articles, and sensitivity analyses where appropriate.

5.1 Contribution Analysis

Contribution analysis was performed on BF route Hot Rolled Coil, Hot-dip Galvanised; EAF Route Section, and Bloom/Billet for all the sites represented in the study. The contribution analysis was carried out in conjunction with statistical analysis on the LCI results to verify data ranges, extreme values and significant differences between regions (i.e. the t-test) – this forms part of the on going work program, and is not reported in detail in this report.

5.2 Contribution Analysis By Articles

The following summary outlines the results by article for each for the steel products under analysis.

5.2.1 Carbon Dioxide (CO2)

The blast furnace, coke and sinter plants dominate the CO2 emissions for the BF Route HRC. The largest non-site process to contribute is Electricity, followed by coking coal, and Lime. Much of the analysis of the CO2 is being reported through IISI working groups on climate change emissions, and is such the subject of ongoing work. Flaring is included in the process stage boundaries, as are by-products, and as such the CO2 related to the export of process gases is not included.

5.2.2 Nitrogen Oxides (NOX)

Transportation dominates the NOX emissions for the BF Route HRC, with sea transport the largest contribution, fluvial transport seventh highest, and rail (diesel) 10th. Sea transport NOX emissions are the lowest of the transport modes used in the study per functional unit, by a factor of 10 per 1000kg.km over fluvial and rail, and a factor of 1000 lower than road transport. With the vast majority of the dry bulk raw materials sourced for use in the steel works transported by sea, the results show the importance of transportation in route contributions. There is a corresponding issue here with emissions of SOX, described below. Appendix 4 further describes the module for transportation, the sea transport used in the model uses heavy fuel oil as the fuel.

Coke and Sinter operations represent the 2nd and 3rd highest contributions of NOX, followed by electricity production for the upstream modules. The allocation of BF Slag for cement production represents the major credit to the system of around 15% - one illustration of the significant choice of using system expansion over other allocation procedures.

5.2.3 Total Particulates

Particulate emissions (including all particulates reported) are greatest for iron pelletising operation, representing around 40% of route emissions. Particulate emissions are reported in many upstream modules, and are too numerous to report below a 5% cut-off. Sinter and BOF steelmaking contribute the highest particulate levels for HRC at the site level, with electricity production and iron ore mining the largest upstream modules. On-site stockpile emissions show an average over the sites of around 3.5% of the total contribution, although few sites report this data, however there is significance here in the impact at a local level. The allocation of BF Slag for cement production again shows a significant credit to the system for particulates.


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5.2.4 Sulphur Oxides (SOX)

Electricity production is the largest contribution to BF Route HRC, followed by Sinter production, and sea transport as the largest three. Allocations of the by-products represent a greater credit to the system than other articles under study, of which BF Slag is the greatest, followed closely by the use of process gases (BF Gas & CO Gas) for onsite electricity and heat, as well as export (BF Gas) for electricity production – again this is an example of the significance associated with using system expansion. The export of process gases for electricity generation replaces the respective national electricity grid in the model; this is an improvement on the previous study, whereby coal was replaced.

5.2.5 (w) Suspended Matter

Hot Rolling operations represent the greatest water emission of suspended matter, due to the extensive use for cooling, and de-scaling. This water is therefore contaminated with suspended matter, oil etc, and is routed to the treatment processes. The other steelmaking processes have significance, apart from sintering, where there is not a large water requirement.

5.2.6 Total Waste

The BOF Steelmaking represents the greatest contribution on site, however the upstream modules totalled together far outweigh this proportion. The contribution from the BOF process will be due to the non-allocated slags. The mining operations (e.g. coal, iron ore, Lime) correspond to the majority of the contribution, and will mainly result from waste rocks, and tailings. Overburden is a separated from the waste reporting, as described in the data sheet explanation (Chapter 6).

5.2.7 Total Primary Energy

The greatest contribution for the total primary energy to the BF HRC route is the coal production, separated for reporting into coking coal, and coal. Electricity production and other types of coal (e.g. for pulverised coal injection) follow, making coal the dominating contribution. Fuel use, in terms of heavy fuel oil and natural gas, represent about 3% respectively. The allocation of by-products such as BF Gas & CO Gas for electricity and heat production; BF Slag for cement; and tar, credit the system for primary energy of approximately 20%.

Observations of the EAF Route show the effect of hydroelectricity use on CO2 emissions. The Rebar operation shows a larger contribution over electricity, whereas for engineering steel, where there is little hydroelectric based electricity for the sites, the electricity supply to site is by far the greatest contribution.

5.3 Sensitivity Analysis

Sensitivity Analyses were performed on the allocation procedure methodology choice of system expansion, and the major contributions to the system of coal, and electricity for blast furnace, and electric arc furnace routes.

5.3.1 System Expansion vs. No Allocation

Blast Furnace Route

The global results for hot rolled coil, (an average over 24 sites), and hot-dip galvanised sheet (an average over 19 sites), with ‘system expansion’ as calculated in the study and with ‘no allocation’, are shown in Appendix 7a,b. The end column is the difference between ‘no allocation’ and ‘system expansion’. It is therefore the cumulated inventory of all the “added systems” listed in Appendix 6.

Sensitivity analyses of the LCI indicate that the system expansion method generally leads to lower burdens being attributed to product routes when compared to no allocation. This is due to


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credits being given to by-products that can replace alternative production of similar functional products.

In general, the sensitivity of the results is dominated by blast furnace by-products (BF gas and BF slag), and their resulting inclusion or exclusion from the boundary (given the choice of using system expansion vs. no allocation), and the effect on the raw materials saved.

Electric Arc Furnace Route

The EAF route analysis is shown in Appendix 7c,d and shows little sensitivity to the choice of system expansion over no allocation, although as with the BF route, the by-products of the system (mainly EAF Slag) dominate the results.

5.3.2 Coal and Electricity

Blast Furnace Route

Coal is the major contribution to the blast furnace route, representing approximately 85% of primary energy contribution for global hot rolled coil. Electricity represents 12% of the contribution. The other upstream processes represent less than 5% of the primary energy contributions, including iron ore mining, fuels, and transport.

The request for coal mining data directly from the suppliers/sites yielded no response, resulting in the sensitivity analysis being performed for an arbitrarily chosen increase and decrease of 25% for all input and output articles of the coal data used in the study. The exercise was carried out to assess the sensitivity of the coal mining process, and not the calorific value of the coal. Similarly, due to the lack of responses for electricity generation process data, the input articles for the electricity generation modules were arbitrarily increased and decreased by 25% to assess the sensitivity of the generation methods.

Appendix 7e-f show the results of the sensitivity analysis on coal and electricity respectively for BF route hot rolled coil.

The results for the sensitivity of coal mining show that the articles for hot rolled coil most affected are the raw material inputs of coal, natural gas, and oil; the air emission of Nitrous Oxide; and waste (total). Articles not appearing in the datasheets that show sensitivity to coal mining include the air emissions of aldehydes, organic matter (unspecified), with changes of +/-22% for each article for the respective increase and decrease in coal articles of 25%.

The results for the sensitivity of electricity generation show that the articles for hot rolled coil most affected are the raw material inputs of lignite, natural gas, oil, and uranium; the air emission of methane; and the water emission of iron. There are small changes in primary energy, due to the allocation of by-products (including electricity). Those articles not reported in the datasheets, that also show sensitivity to electricity generation include the raw material inputs of iron sulphate, lead, pyrite (FeS2), and silver; numerous air emissions including Fluorine, Benzene; and numerous water emissions including sodium.

Electric Arc Furnace Route

Analysis of the sensitivity of electricity to EAF Engineering Steel is shown in Appendix 7g for the same arbitrarily chosen 25% increase and decrease in electricity generation (all articles). Coal is not a significant contribution to EAF Steelmaking, apart from that already contained in the electricity generation; therefore no separate sensitivity for coal was carried out.

The results for the sensitivity of electricity generation show that the articles for engineering steel most affected are the raw materials inputs of lignite, natural gas, oil, uranium; the air emissions of hydrogen chloride, hydrogen sulphide, SOX, methane, NOX, and cadmium; and the water emissions of iron and lead.


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CHAPTER 6: DATA SHEETS EXPLANATIONS

The function of this chapter is to explain some of the main features of the data sheets and clarify potential ambiguities. Data sheets were produced for all products both globally and regionally (E.g., Europe, Far East Asia and Rest Of World, see chapter 3.6) whenever more than three sites contributed. This was necessary to maintain confidentiality between companies and to ensure a minimum level of representivity.

The data sheets indicate the number of contributing sites, the process route and the geographical identity.

6.1 Dates

Two dates are indicated on the data sheets.

Date of issue is administrative information indicating when the data sheet was calculated and issued. Due to the vast scale of data contained in the LCI results, the data sheets are updated regularly following corrections or adjustments to improve methodology. Users who are applying the data should check the latest data issue.

Date of data (1999-2000) relates to the on-site data specifically (see section 3.2). The age of non-site data, namely upstream and system expansion components generally ranges from 1995 to 2000 (see Appendix 4).

6.2 Statistics

Averages are supported by maximum and minimum data ranges and by variance using a coefficient of variation calculation, (standard deviation/ average value ).

These ranges result mainly from the variability across sites and from national grid electricity production. With the exception of the waste quantities from coal mining (see section 4.1.3), all other upstream system components are assumed the same every site and country. The main upstream process units such as iron ore mining, pelletising and coal mining representivity have been improved with data direct from the companies.

Min and max values have been included to allow sensitivity analysis. However, the column min (max) should not be considered as a best (worst) scenario since the set of min (max) values pertains to different sites. For example, the site with the minimum value for iron ore is among the maximum values for scrap. Similarly, the energy reminders in the min and max columns are not consistent with the raw materials values in the same column.

Extreme values and regional variations beyond the extensive troubleshooting phases of the study form the basis for further analysis. In general, the LCI data across sites indicate the balance between cost and operational controls based on the availability of process technology and raw materials but also on local infrastructure and economic factors.

6.3 LCI Flows/Articles

This section gives additional clarification about some of the articles included in the data sheets.

Only major articles are shown in the data sheets, namely the major raw materials, the “accounted” emissions (see section 2.4.2), total waste, total non-allocated by-products and the energy reminders. Information on other articles can be obtained on request (see also below the paragraph on other articles not reported).


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(r) Iron (ore)

The mass of iron ore in ground is reported in kg of iron oxides (mainly FeO, Fe3O4, Fe2O3) and excludes the mass of overburden. The definition of overburden can be found in section 4.1.3, and the definition of wastes is below.

(r) Zinc (ore)

The mass of zinc ore in ground is reported using the LCI carried out by the International Zinc Association for European Zinc production, and represents 82% of European production in 1995.

Ferrous Scraps (net)

This describes the net quantity of ferrous scraps taking account of imports and exports from the system. It includes both steel and iron scraps (although iron scrap usage is generally small).

The data includes:

- Scraps from external supply (scraps merchants, municipal facilities or other factories),

- “Circulating” scraps from within the steelworks but outside the manufacturing system for the steel product route. Thus for intermediate stages (e.g., hot rolled coil) net scrap input may be elevated owing to inputs from downstream stages (e.g., cold rolling). This scrap component tends to decrease with additional process stages. Scrap generated and reused within the manufacturing system is not included as this flow is internal to the system.

As explained in section 4.1.1, because of the study scope, no upstream data for scrap collection, sorting and processing was included in the study, except the transportation of external scraps from the various sources to the steelworks. Similarly, no allocation procedure was applied to the use of scraps, as those described in ISO 14041 for reuse and recycling situations. However, these decisions accommodate the extension of system boundaries in studies for individual products.

The issue of scrap allocation is further discussed in Appendix 5.

Water Used (total)

The update study included an improved water data collection method, by automatically calculating the net water consumption of the plant, as to avoid the double accounting and allocation caused by the circulation of water within the plant. Therefore the results show only the net water consumption, instead of the water intake and wastewater separately.

In addition to the steel plants reporting the water as ‘water used (total)’, the article includes all the upstream processes that have a significant contribution, except for coal mining. The coal mining data availability remains an area for improvement, and even following extensive data gathering requests; no update was possible from the original study. This article therefore assumes that the upstream processes define the water used (total) as net water used.

The quantities of salted water, i.e. sea and brackish water, used by the steel plants were recorded in the original study, but are not reported in the data sheets. Salted water is an abundant resource on coastal area where many blast furnace sites are located. It is mainly used for indirect cooling and therefore it is not contaminated with pollutants coming from the processes.

Fresh water used by the steel plants has several origins: namely surface water (river and lake), deep water (e.g. mine water) or “technosphere” sources (other industrial plants, waste water treatment plants, etc.).


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General remark concerning water usage and water emission data

The study highlighted the difficulty to obtain accurate LCI results for water usage and water emissions. This is due to the lack of metering and complexity of the water networks within industrial plants where effluents from different process units are mixed, intensively recycled between process units to minimise the intake from the environment, and finally often treated in common wastewater treatment utility. This makes difficult the allocation of water usage and water emissions between individual process units.

Regarding water emissions specifically, when recorded in the questionnaires, the pollutant amounts in the intake were subtracted from the pollutant amounts in the discharged wastewater because they are not attributable to the steelmaking processes. For some sites located downstream of urban and industrial areas, the outflow water is purer than the intake. However, there are many gaps for this category of data for which it is not possible to calculate an estimate. Therefore, the values of waterborne emissions are potentially overestimated in terms of net emissions.

These aspects account for the variability of data regarding water usage and water emissions. Better metering and monitoring will help to reduce this in future.

(a) Carbon Dioxide

This article indicates both fossil and mineral sources of CO2 (e.g. combustion of natural gas, oil, lime calcinations, and the oxidation of coal).

(a) Particulates (total)

This article includes all types of airborne particulate emission, including PM 10 and PM 2.5.

The treatment of this article was improved from the original study; namely,

- Including the provision for fugitive emissions to be separated from stack emissions for the completion of missing results, in order to increase the accuracy of the results in this important category of emission,

- Including the provision for fugitive emissions from stockpiles; this was added into the data collection phase.

Fugitive emissions from stockpiles, e.g. coal and iron ore are highly variable due to local parameters such as wind, humidity, and management practices. It is estimated that this source of particulates can represent about 5% of the total site emission but because some are coarse size particulates these can fall to ground in the locality of the site. It should also be noted that only a small number of sites reported fugitive emissions from stockpiles.

Non Allocated By-products

Various by-products recovered externally are not allocated using system expansion or other methods. Their cumulated mass is reported under the category “non-allocated by-products” which exclude the quantities recycled internally or dumped. These by-products were not allocated because of a lack of accurate information on their actual applications and on their possible treatment before usage, although the general trends are known. This situation made difficult to apply the system expansion method in a rigorous way. However, due to the small tonnages involved and considering the nature of the raw materials saved, it is believed that the allocation of these by-products would have minor effects on the overall LCI results. This will be analysed in future work programmes.

For the EAF route, the quantity of non-allocated by-products is up to 0.054 kg per kg of the engineering steel route, 63% of which are EAF slags. EAF slags are mainly recycled as aggregates for road building and other civil works. Applying a credit of stone aggregates (using a generic quarrying model) to the EAF slags has no effect on the EAF route LCI results. However,


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the saving of natural aggregates has other advantages, particularly concerning land use and landscapes.

For the BF route, the total quantity of non-allocated by-products is up to 0.10kg/kg tinplated coil, and covers a wider diversity than for the EAF route, as showed in Appendix 8 for hot rolled coil and tinplated coil. The variation in quantity between products is mainly attributable to the differences of site sampling and slag recovery practices.

Waste (total)

85 different types of waste were identified for the BF route and a further 19 for the EAF route, plus those arising in the upstream phase for which the information has generally improved from the original study. For simplification, the data sheets report only the total quantity of waste. Moreover, because of regulation differences between countries concerning waste characterisation, wastes could not be classified in categories (inert, industrial, hazardous, toxic, etc.) according to their level of potential hazard. The quantities per individual waste are kept in the database.

Table 6.3-1 shows the main waste materials for hot rolled coil, BF route, based on the global averages.

Other overburden materials were recorded separately (i.e. not as waste) as also explained in section 4.1.3.

In steelmaking, process metallurgy (BF, BOF, Metallurgy) slags are used as the steel makers tool for the important roles of separating iron from the other constituents in the ore, and to remove any unwanted elements from the steel and incorporate them in a stable slag structure. When the liquid iron or steel is removed from the process, the slag accompanies it. By carefully controlling the separation and treatment of this slag, the steel maker generates a slag product that can be sold in certain markets, of which the main ones are aggregate and cement. Other smaller markets exist, such as sandblasting, and agriculture. Due to lack of demand in these markets, it may arise that the steel maker might not process the slag in this way, and therefore it must go to landfill. Only in these circumstances does slag become waste.

Material type Origin Generation rate kg/kg of products

Waste (mining) Iron ore mining rocks etc.

1.33646

Waste (unspecified) Mining and fuel operations

0.15586

Minerals (inert) Mining, acid production

0.0328

BOF Slag BOF 0.02419 Metallurgy Slag Steel Plant 0.01038 Tailings Pellet Making 0.007055 Refractories BF, BOF 0.0065 BF Slag BF 0.0044 Others, including sludges, dust, fines etc

Various 0.0175

Total 1.59523

Table 6.3-1: Main Waste Materials, BF Route (hot rolled coil, global average)

Similarly, Table 6.3-2 shows the main waste materials for rebar/wire rod, EAF route, based on the global averages.


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Material type Origin Generation rate kg/kg of products

Unspecified Various 0.0107 EAF Dust EAF 0.0046 Minerals (inert) Mining 0.0024 Slags and Ash Mining & Fuel 0.0006 Refractories Steel plants 0.0003 Other Wastes Various 0.0011 Total 0.0197

Table 6.3-2: Main Waste Materials, EAF Route (bars, global average)

Energy reminders

The primary components of a Life Cycle Inventory are the material inputs and outputs that are taken from or are emitted to earth. Certain material inputs, (e.g. coal, oil etc.) constitute energy as well as mass inputs, which can be calculated based on calorific value. Within the LCI data sheets, these accumulated energy values are indicated in a separate section in order to facilitate energy analysis and to remind analysts that these values are derived from material inputs and are not “in addition” to them. The section is referred to as Energy Reminders. Energy reminders are normally considered to be outside the scope of an LCI but are useful for data verification and interpretation purposes.

Within Energy Reminders the calculated energy indicators have been based on net (low)

calorific values and included the following:

- Total primary energy: this is the sum of all energy sources which are drawn directly from the earth, such as natural gas, oil, coal, biomass or hydropower energy. The total primary energy contains further categories namely non-renewable and renewable energy, and fuel and feedstock energy. These are described below:

- Non-renewable energy: includes all fossil and mineral primary energy sources, such as natural gas, oil, coal and nuclear energy.

- Renewable energy: includes all other primary energy sources, such as hydropower and biomass.

- Fuel energy: is that part of primary energy entering the system that is consumed.

- Feedstock energy: is that part of the primary energy entering the system that is not consumed and/or is available as fuel energy and for use outside the system boundary. In the case of steelmaking, this includes the calorific value of energy of the outputs (such as that contained in products, recovered materials and waste) as well as fuel losses. In practice, feedstock energy from waste and fuel losses were omitted.

The sum of fuel and feedstock energy, as well as the sum of renewable and non-renewable energy always equates to the total primary energy.

Practically, the steel product feedstock energy at route level is close to zero since the calorific value of steel was assumed zero and the by-products feedstock energies were accounted for by allocation procedures relating these to primary energy.

The definition of fuel energy within the LCI Study covers all the energy that is spent for process purposes, either to produce heat, mechanical energy or to enable endothermic chemical reactions to take place. Thus, that proportion of the coke and blast furnace injectants, such as natural gas, coal and oil, which are used as reducing agent are included in fuel energy.


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Within the study, the primary energy calculation is based on the following parameters:

- Net caloric values for fossil, mineral and biomass materials,

- Gravitational energy for hydropower: 1.11 MJ of gravity energy yields 1 MJ of electricity,

- Burn-up rate for uranium ore: 7.92 10-3 g of uranium ore, equivalent to 3.19 MJ of primary energy, yields 1 MJ of electricity.

Other articles not reported

As mentioned at the bottom of the data sheets, only the major raw materials are shown for simplification reasons.

Further details concerning waste and non-allocated by-products are given above.

Concerning the air and water emissions, all ‘accounted’ emissions (see section 2.4.2) are reported in the data sheets. As explained in the same section, because of limited available data, other emissions had too few data sets to provide reliable average data. However, ranges are available for some of these non-accounted emissions, with however some inherent uncertainties due to the data gaps and approximations at the upstream phase.

Concerning natural resources, several other materials may be of interest in the production of crude steel, although the quantities used are very small as indicated in

Table 6.3-3. Depending on the product, a wide variety of other alloy metals such as copper, boron, molybdenum, niobium and strontium can also be used but always in lowly quantity (always less than 0.1 g/kg of crude steel). Lead can be incorporated in higher quantity in some special products called “free cutting” steels used in tools. This was not included in the study. Other natural resources used for the production of crude steel are abundant materials such as gravel, sand, sodium chloride and clay.

Other Natural

Resources (kg/kg

of products)

Hot Rolled

Coil

BF route

Bars

EAF Route

(r) Bauxite

(Al2O3, ore)

0.00617 0.00336

(r) Chromium (Cr)

(Cr, ore)

0.000682 6.16E-09 (and up to 0.0069 for engineering steels)

(r) Ilmenite

(FeO.TiO2, ore)

0.000298 NR

(r) Manganese

(Mn, ore)

0.009878 0.015

(r) Uranium

(U, ore)

1.8E-06 3.53E-07

Table 6.3-3: Other Natural Resources (global averages) used for crude steel making


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In addition tin is used as the coating material for tinplated coil (an average of 3.75 g of tin per kg of tinplated coil according to the study). Some chromium compounds, mainly chromic acid, are also used either for precoating treatments (passivation) and /or as a coating material for ECCS (an average of 0.13 g of (r) chromium per kg of ECCS). Finally, the quantity of uranium used by the BF route can be doubled for more sophisticated products such as Electrogalvanised, or organic coated sheets, due to the consumption of electricity for rolling and coating operations.


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CHAPTER 7: CONCLUSIONS

The IISI has updated one of the largest, and representative databases of any material, in accordance with the ISO14040 set of standards. The results can be used reliably to assist decision-making and for evaluating the performance of steel products in the context of sustainable development.

The study has defined methodology for steel process routes and many of the choices have been based upon representing the real interactions of these processes with the global environment. The focus of the update has also been to improve the study, examples include:

- An expanded list of accounted emissions, reflecting the improved measuring and recording techniques of the industry

- Improved data for upstream processes, such as data sourced from iron ore mining, pelletising sites, and use of other industries inventories based on site data

- The refinement of the methodology so that processes gases displace the marginal energy sources relevant to the site, rather than displacing only coal.

- Increasing the number of countries represented from 17 to 20, with 50 sites and 325 separate processes.

- Increasing the number of averages from 12 to 14 products under study.

- Improving the proportion of material inputs to the steel sites traced back to the earth (98.4% sourced to earth for the BF route, and 97.2% for the EAF route)

- Improving the assessment of quality for the data collected from the steel sites. Just fewer than 14,000 (~4000 more than the original study) data points were collected, with a quality scores by process.

- Improving the interpretation stage of the LCI in terms of contribution analysis, and sensitivity analysis for those flows of high significance, or methodological choices that reflect the industry practice.

- The sensitivity analysis for allocation was performed and shown to be of low influence apart from the following articles:

Improvement opportunities remain in the coal mining data, and electricity generation data for each country, despite the considerable effort employed for this study, only a limited response was received.

In conclusion, the steel industry has carried out a comprehensive, and technically rigorous LCI study, employing consistent data collection, methodology, and processing, in order to provide a sound foundation to further LCA’s involving steel. These data are available for third party use, along with support from the industry on any methodological or technical issues. This study continues to reflect the industry’s commitment to transparent reporting, and enhancement of its performance, and intends to continue this trend in its future work program.


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ACRONYMS AND ABBREVIATIONS

BF Blast Furnace

BF Gas: process gas generated in the blast furnaces BOD Biological Oxygen Demand

BOD should be measured over a five day period BOF Basic Oxygen Furnace

BOF Dust: Dust collected by the basic oxygen furnace smoke filteration BUWAL Swiss Federal Office of Environment, Forests and Landscape (FOEFL or BUWAL)

BWTP Biological Waste Water Treatment Plant

COD Chemical Oxygen Demand

COG Coke Oven (Gas)

EAF Electric Arc Furnace

ETH Ökoinventare für Energiesystem, Laboratorium für Energiesystem, Zurich

IISI International Iron and Steel Institute


43

LIST OF TABLES

Table 2-1: List of products covered in the study

Table 2.3-1: Number of process stages represented in the study

Table 2.4-1: List of accounted air and water emissions

Table 3.6-1: Number of contributing sites per region

Table 3.6-2: Part of total crude steel production covered by the study

Table 4.1: Percentage of total site input tonnage traced to upstream production

Table 6.3-1: Main waste material, BF Route, hot rolled coil

Table 6.3-2: Main waste materials, EAF Route, bars

Table 6.3-3 Other natural resources used for crude steel making

Table A3-1: Table of data quality scoring system

Table A3-2: Number of data points in top data quality category

Table A4: List of Upstream modules used in the study

Table A6: List of system expansion assumptions

Table A7a-d: Sensitivity of system expansion vs. no allocation

Table A7e-g: Sensitivity analysis of coal and electricity on the study

Table A8: Main non-allocated by-products, BF Route

Table A9: List of participating companies in the study

Table A11-1: Contribution analysis for selected articles, hot rolled coil

Table A11-2: Contribution analysis for selected articles, hot-dip galvanised

Table A11-3: Contribution analysis for selected articles, EAF bloom/billet

Table A11-4: Contribution analysis for selected articles, EAF Sections


44

LIST OF FIGURES

Figure 2.1: System overview

Figure 4.1: Grid electricity production model

Figure A1: Steel product manufacturing Flow Diagrams

Figure A2: Example of module representation

Figure A3: Data quality figures for air, water and total data points


45

APPENDICES

APPENDIX 1: Steel Product Manufacturing Flow Diagrams.................................................................46

APPENDIX 2: Example of Module Representation.................................................................................49

APPENDIX 3: Steel Plant Data Quality Statistic .....................................................................................50

APPENDIX 4: Upstream Model References ............................................................................................53

APPENDIX 5: Application of the IISI LCI Data to Recycling Scenarios ...............................................59

APPENDIX 6: System Expansion Assumptions.....................................................................................64

APPENDIX 7 A, B: System Expansion vs. No Allocation, BF Route for HRC and HDG.....................66

APPENDIX 7 C, D: System Expansion vs. No Allocation, EAF Route for Bars and Eng Steel..........70

APPENDIX 7 E: Sensitivity Analysis for Coal on BF Route Hot Rolled Coil .......................................74

APPENDIX 7 F, G: Sensitivity Analysis for Electricity on BF Route HRC & EAF Route Eng Steel...76

APPENDIX 8: Main Non Allocated By-Products on BF Route .............................................................80

APPENDIX 9: List of Participating Companies.......................................................................................81

APPENDIX 10: Critical Review Report.....................................................................................................82

APPENDIX 11: Selected LCI Articles Contribution Analysis ................................................................88


46

APPENDIX 1: Steel Product Manufacturing Flow Diagrams

BLAST FURNACE ROUTE

(11) Sections (7a) Hot Rolled

Coil(12) Rebar

(13) Engineering Steel

(14) Wire Rod

D C

F

G1

I K H J

to Pickling

Sinter Coke making

Blast Furnace

BOF Steel Making

Section RollingRod & Bar

RollingHot Strip Mill

Heavy Plate

Rolling

Graded SinterCoke

Hot Metal

Slabs-Blooms-Billets

(8b) UO Pipe

UO Pipe

Making

(10) Plate

M2


47

COLD ROLLING ROUTE

(4) Tinplated

Products(5) Tin-free

Steel

(7a) Hot Rolled

Coil

(9) Cold Formed

Sections

(7b) Pickled Hot Rolled Coil

(3) Organic Coated Flats

(2) Electrogalvanized(1) Hot-dip

Galvanized

L

O

P

T S Q R

U

V

From Hot Strip Mill

(6) Finished Cold Rolled Coil

Annealing

and Tempering

Cold Rolling

Pickling

Tinplating ECCS Electrogalvanising Hot-dip Galvanising

Organic Coating

Cold Formed Sections

Cold Rolled Coil


48

ELECTRIC ARC FURNACE ROUTE

(11) Sections

G2

KRod & Bar

Rolling

EAF Steel Making

Section Rolling

(12) Rebar

(13) Engineering Steel

(14) Wire Rod

I

Blooms-Billets


49

APPENDIX 2: Example of Module Representation

G1 BOF STEEL MAKING

Basic Oxygen

Furnace

Blast Furnace

Wet Gas De-

Dusting (tertiary)

Slabs-Blooms-Billets

Metallurgy

Continuous Casting

Hot Strip Mill

Rod and Bar Rolling

Section Rolling

Heavy Plate Rolling

Clarification

Desulf. - Dephosph.- Desilic. Slags

BOF Slag

BOF Dust

Iron Ore

Scraps

Continuous Casting ScalesScales

Continuous Casting Sludge

Steam

Desulphurisation

Dephosphoration

Desiliconisation

Sludge

Water

BOF Gas Fine Sludge

Aluminium

Copper

Lead

Nickel

Ferro Manganese

Ferro Boron

Ferro Calcium

Ferro Chromium

Ferro Molybdenium

Ferro Niobium

Ferro Phosphorous

Ferro Silicium

Ferro Titanium

Silico manganese

Unspecified

Power Plant

Flare

Coke Making

Blast Furnace

Other process stages

External

Oxygen Making

Power Plant

GasCleaned Gas

Nitrogen (N2)

Blast Furnace BF Gas

Coke Making COG Gas

SteamPower Plant

Power Plant

Air

Emissions

(a) Carbon Dioxide (CO2, fossil)

(a) Carbon Monoxide (CO)

(a) Nitrous Oxides (N2O)

(a) Dust

(a) Sulphur Oxides (SOx as SO

2)

(a) Nitrogen Oxid (NOx as NO

2)

(a) Zn

(a) Lead

(a) VOC

PredecantationDecantation &

Purge Treatment

BOF Gas Coarse Sludge

Scraps

Waste WaterWaste Water

Treatment Plant

Pressure Air

Electricity (site)

Calcium Carbide (CaC2)

Magnesium (Mg)

Manganese Ore (Mn, ore)

Dolomite (CaCO3-MgCO3, crude)

Demineralised WaterWater Demineralising

WaterReleased Water

Oxygen (O2)

Hot MetalPig Iron

Metallic Additives

Ingot Casting

Ingots

Moltered Iron Sintered Iron

Electrodes

Other BF Gas

De-Dustings

(primary, dry)

BOF Gas (1)

BOF Gas (2)

BOF Gas Dust

Recirculated Water

Waste Refractories

Scraps

De-Dustings

Hot Metal Treatment Gas

Hot Metal Treatment Dust

BOF Slag Processing

Metallurgy Sludge

Metallurgy Dust

Metallurgy Slag

Water Softening Soft Water

Water

Light Fuel Oil

Cutting

Crylene (C20

H33

NO6)

Acetylene (C2H

2)

Propane

Oil

Nitrogen (N2), Argon (Ar)

Sulphur (S)

Fluorspar (CaF2, fluorite)

Carbone Dioxide (CO2)

Sodium Carbonate (Na2CO3)

Sodium Bicarbonate (NaHCO3)

Sodium Hydroxide (NaOH)

Calcium Chloride (CaCl2)

Calcium Sulphate (CaSO4)

Lime (CaO)

Scales

Iron Scraps

Synthetic Slag

Recarbiding Agent

Petroleum Coke

Liquified Petroleum Gas

Casting Powder

Refractories

Coke

Coke Making

Other Fuels

Secondary BOF

DustSecondary Dedusting

Recirculated Water


50

APPENDIX 3: Steel Plant Data Quality Statistics

The data collection phase of the IISI LCI study made provision for the quality of each data point entered into the questionnaire in terms of Source, Type, and Date. This allowed a score (maximum 15) per data point to be calculated, and therefore an assessment of the overall data collection for the steel works.

Table A3-1 shows the scoring system with the weighting scores to give a maximum possible data quality score of 15;

Source Type Date

Factory Literature Other Measured Calculated Average Estimated Unknown 01-99 98-94 93-89 <88

5 3 1 5 4 3 2 1 5 4 3 1

The same exercise was carried out for the air emissions and effluents data points, with the results shown graphically below.

LCI Data Collection Quality (All Steel Site Data)

Average Data Point Score by Process Stage (Max Score = 15)

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

EAF Ste

el M

aking

Engine

ering

Ste

el Roll

ing

Sectio

n Roll

ing

Heavy

Plat

e Roll

ing

Weld

ed P

ipe M

aking

Wire

Rod

& R

ebar

Roll

ing

Annea

ling

& Tem

perin

g

Tinplat

ing

Sinter

Blast F

urna

ce

Hot S

trip

Mill

BOF Ste

el M

aking

Cokem

aking

Organ

ic Coa

ting

Hot D

ip Galv

anisi

ng

Altern

ative

Iron

mak

ing

ECCS

Cold R

olling

Picklin

g

Electro

galva

nising


51

The data quality statistics are performed only on the steel plant primary data (no upstream processes are included) that are non-zero, and have the data quality indication completed (or partially completed).

Effluent Data Quality


0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

EAF

non-

alloc

ated

UO Pipe

mak

ing

Sectio

n Roll

ing

ECCS

Cokem

aking

Engine

ering

Ste

el Roll

ing

Heavy

Plat

e Roll

ing

Picklin

g

Hot D

ip

Blast F

urna

ce

Sinter

Tinplat

ing

Annea

ling

Hot S

trip

BOF

Power

Plan

t

Organ

ic co

ating

Pellet

mak

ing

Electro

galva

nising

Cold R

olling

Altern

ative

Iron

mak

ing

Wire

Rod

& R

ebar

Roll

ing

Air Emissions Data Quality


0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

EAF Ste

el M

aking

Engine

ering

Ste

el Roll

ing

Altern

ative

Iron

mak

ing

Sectio

n Roll

ing

Wire

Rod

& R

ebar

Roll

ing

Pellet

Mak

ing

Sinter

Cokem

aking

Blast F

urna

ce

Hot S

trip

Mill

BOF Ste

el M

aking

Annea

ling

& Tem

perin

g

Power

Plan

t

Hot D

ip Galv

anisi

ng

Heavy

Plat

e Roll

ing

Tinplat

ing

Cold R

olling

Electro

galva

nising

Organ

ic Coa

ting

Picklin

g

Weld

ed P

ipe M

aking

ECCS


52

Data Quality Summary

In terms of the quality of the data collection, the updated study represents just less than 14,000 data points for which the quality can be scored up to a maximum of 15 - the original study represented just over 10,000 data points. The quality values show good results even including the carrying forward of those sites for which 1995 data is still valid (Japanese sites).

The air emissions data collection represents 3156 data points, with a quality range from 11.0 to 14.2; the effluents data collection represents 5131 data points, with a quality range from 10.3 to 14.2.

Table A3-2 shows the percentage of data points in each of the top categories of data quality.

Percentage Of Data Points In Top Data Quality Category

Factory Measured 2001-1998

All Data Points 98 64 55

Air Emission Data Points 92 32 67

Effluent Data Points 84 34 43

Table A3-2 Data Points in Top Data Quality Category

The EAF process stage has the highest data quality in overall, air and effluent categories, and in terms of products in the study, engineering steel, and sections have best data quality in the study. With respect to the blast furnace route, the coke, sinter, and blast furnace process stages all score highly in the assessment, due to the necessity of reporting to regulatory bodies for strict environmental controls and regulations applied to these units. As the processing route develops to rolling and finishing operations, then the data quality reduces to a minimum of 10.3 for effluents, 11.0 for air emissions – still a relatively high quality rating.

These data from the steel works, and iron ore mining are considered reliable, as they are source directly from the sites. Generally the range and variance of these data are small, especially given the magnitude of this study. The consistency of data collection, and processing was ensured through the development of electronic tools, and common formats, together with the training and experience gained from almost a decade of life cycle work at IISI. The transparency of study ensures that it can be reproduced, using the extensive documenting and recording of the progress during data collection, checking, and generating results.

Given the increase of data points collected for the updated study, the IISI LCI can be shown to be of high quality, and with a defined data quality methodology established, can track and identify areas for improvement in the future, measure the success of the exercise, meet the goal of the study, and the ISO14040 standard for data quality requirements.


53

APPENDIX 5: Application of the IISI LCI Data to Recycling Scenarios

This appendix has been updated in 2005 and can be found in the document “Recycling methodology 2005”

Scrap Quality and Recycling

Steel is 100% recyclable and scrap can be converted to the same (or higher or lower) quality steels depending upon the metallurgy and processing of the recycling route. Some recycled products such as rebar require minimal processing whilst the higher value engineering steels require more metallurgical and process controls to meet tighter specifications.

Some steel products are currently sourced mainly via the primary route mainly because the steel specifications require low residual elements and this can be most cost effectively achieved using primary material. Low residual scrap commands a higher market price owing to the ease of processing through the recycling routes.

As demonstrated above the life cycle inventory of materials in closed loop systems is not dependent upon the source of the material but on the levels and yields of recycling and the relative impact of the recycling versus the primary route.


54

APPENDIX 9: List of Participating Companies

Europe Far East Asia

Arbed (Arcelor Group) Aichi Steel Works Ltd. Corus Group plc China Steel Corp. Cockerill Sambre (Arcelor Group) Daido Steel Co. Ltd Fundia AB Kawasaki Steel Corp. Rautaruukki Oyj Kobe Steel Corp. Sidmar NV (Arcelor Group) Nippon Steel Corp. Thyssen Krupp AG Nisshin Steel Co. Ltd Usinor (Arcelor Group) NKK Corp. Voestalpine POSCO SSAB Sanyo Special Steel Co. Ltd Erdemir Sumitomo Metal Industries Trinecke Zelezarny a.s.

VCZ (US Steel Kosice) N.America

Dofasco Inc Others US Steel ISCOR Hylsamex


55

APPENDIX 10: Critical Review Report

Critical review of the 2002 update of the IISI Worldwide LCI Database for Steel Industry Products Bo P. Weidema, Atsushi Inaba, Gregory A. Keoleian The following review has been performed as a critical review according to ISO 14040, clause 7.3.3. 1. The review process The composition of the review panel was proposed by IISI and accepted by the panel chairman. The review was performed in the period from 2002-04-17 to 2002-06-10 based on the draft written report and the data sheets with the LCI results. Bo Weidema and Atsushi Inaba had the opportunity to inspect the underlying data and models at a 5 hours meeting with IISI on the 10th of May 2002. Greg Keoleian was not able to participate in this meeting but reviewed the written report and prepared questions for IISI. 2. Consistency with the International Standard The study was checked for consistency with all requirements of the International Standards ISO 14040, 14041 and 14043 relating to Life Cycle Inventory Analysis (LCI). For the most part, we have found the IISI Worldwide LCI Database for Steel Industry Products (hereafter called “the study”) well constructed and adhering to the requirements of the said standards. It is noteworthy that the study generally applies the ISO system expansion procedure to avoid by-product allocation (except for a few upstream processes). The original study from 1997 was among the first to seek to apply this procedure throughout a study and the report gives a good description and justification of the applied procedure. The results (for 4 production routes) are presented before and after system expansion, which allows the reader to understand the importance of these procedures. Some specific concerns regarding data quality for the upstream processes and the completeness of the interpretation are elaborated in sections 4 and 5 of this review report. 3. Scientific and technical validity The system expansion procedure to avoid by-product allocation implies an attempt to reflect the actual consequences of changes in steel production volume, i.e. to include those processes that are expected to change their production volume in response to changes in steel production volume. To be consistent, the procedure used to identify the processes included in system expansion should also be applied generally when deciding which processes to include in the systems studied. For electricity supply, the processes that can be expected to change are the long-term marginal suppliers to the regionally delimited electricity markets, rather than the average suppliers to the national electricity grids that have been applied in the updated study. The use of average data for the national grids leads to the inclusion of processes that cannot be expected to change their production volume in response to changes in steel production volume, such as hydropower and nuclear power. This procedural inconsistency mainly affects the data for the EAF route, since its electricity consumption is significant. In order to allow the user to correct for this, we recommend that IISI publish the amount of electricity used in each system (e.g. as an energy reminder), as well as the proportion of different energy sources in the aggregated national grids data used in each system. An even better option would be to publish the steel data in a modular form, with and without the electricity system included.


56

In the same line, we do not find the justifications for the processes included in the system expansions sufficiently specific, as there are no references to market data that could support the identification. Besides electricity, we are particularly concerned about the assumptions re. sulphur (in many regions, sulphur is now supplied from desulphurisation processes), tar (typically demand changes in refinery inputs), and waste oil (how contaminations in the waste oil are treated). Also, appendix 6 appears to imply that displacement of alternative production processes has been assumed for materials identified as partly waste and partly by-products, although in reality only the volume for waste treatment is affected, since for such materials the amount of displacement is not dependent on the steel production volume. However, due to the generally small amount of these by-products, the influence on the result is likely to be minor. While system expansion was applied consistently to all inputs to and outputs from the core steel manufacturing processes, the study includes data for a number of upstream processes where the allocations made in the original source are carried over into the IISI study. The significance of this inconsistency has not been assessed. Again, we recommend that IISI publish data for the core steel manufacturing processes as a separate data module, to allow users to add or substitute upstream processes and system expansions as appropriate. Especially, it should be noted that the well-described (appendix 5) and commendable procedure for recycling, where the scrap displaces hot metal, requires the use of data for hot metal, which is currently only available upon special request. 4. Appraisal of the data used It should be noted that emissions to soil are explicitly excluded as a data category. This implies that issues of land contamination and local effects on biodiversity, which are especially significant for the upstream mining operations, are not covered by the IISI study. The data categories may also fall short with regard to a few air pollutant emissions (and soil emissions). Significant air pollutant emissions from steel production such as manganese and arsenic were not reported in the final results. Polycyclic aromatic compounds and benzene are significant emissions for the coke oven operation, and although included in the so-called “non-accounted emissions” (see appendix 12) it should be considered to provide these data together with the accounted emissions, albeit with a specific warning on their limited representativity. This also applies to the size classified particulates, e.g. PM2.5 and PM10. It should be noted that the emissions of Zinc and its presence in the EAF dust is caused by its presence in recycled products and not to steel recycling as such.

The data collection for the core steel manufacturing processes has been performed very rigorously, with appropriate checks on consistency and completeness. For the core steel manufacturing processes, there is a good documentation with respect to data validity and the treatment of missing data. Data quality is generally well documented, especially for the core steel manufacturing processes. However, the draft report does not discuss data quality goals, in terms of what data quality is regarded as necessary and/or sufficient in relation to the goal of the study, nor any data quality requirements as specified in ISO 14040, clause 5.1.2.3. We do not find it meaningful to aggregate data quality indicators for data source, type, and date (appendix 3 of the draft report). The same indicators could better be presented separately, which would also increase the possibility to detect the causes for changes in data quality. To validate data and aggregation procedures and/or to provide data for different operating conditions, we would recommend applying a model of the steel making processes. The development of such a model (or the expansion of an existing model to cover environmental aspects) would probably be more worthwhile than continuing the isolated collection of the already well-documented primary data for the


57

core steel manufacturing processes. The advantage of such a model would also be its ability to consistently analyse different improvement options and to predict data for future operating conditions. The use of straight averages (i.e. without weighting the contributions according to production volume) is rather unusual, and the justification provided does not appear convincing. However, the resulting overrepresentation of smaller plants is not expected to affect the results significantly, as there is no clear correlation between plant size, efficiency, and environmental impacts. The data quality assessment does not include the upstream processes. For these processes the treatment of data gaps is also less documented and can hardly satisfy the requirement in ISO 14041, clause 6.4.2. The study obtained an impressive completeness in terms of the proportion of mass inputs to the steel manufacturing processes, which have been recorded. A contribution analysis was carried out on the original study, to the effect that the inputs excluded from the data collection represent less than a cumulated 2% of the mass input to the steel manufacturing processes. However, the exclusive focus on mass inputs implies an exclusion of service inputs (which do not have any mass), such as wholesale trade, advertising and (waste) water treatment, which together represent e.g. 4% of upstream CO2 in American I/O tables. Furthermore, due to the cumulative nature of upstream processes, a 2% cut-off for inputs to the steel manufacturing processes implies an exclusion of 10-20% of all emissions. For those upstream processes that were found to be significant, a large effort has been expended to obtain adequate data. Nevertheless, the obtained data quality for production of coal, cement and lime is inadequate to ensure the integrity of the cumulated results. The documentation of excluded upstream processes and data gaps in the included upstream processes is inadequate. This also implies that the sensitivity analyses are inadequate to assess and describe the potential effects of these exclusions on the outcome of the study, as required in ISO 14041, clause 5.3.5.

5. Interpretation The interpretation section of the draft report did not conform to ISO 14043. We missed especially an interpretation of the results in relation to the goal and scope (applications) of the study, discussing questions such as: Are the data representative (also geographically e.g. for Japan and the U.S)? What is the possible impact on the result of the upstream allocations (not always using system expansion) and the choice of electricity production processes? What are the major limitations (e.g. importance of missing upstream data; the exclusion of soil emissions and land use issues)? How could the user compensate in different situations? Also, we recommend that the results of this study be placed in the context of previous LCI studies of steel, highlighting any significant differences and their causes. It should especially be noted that the IISI data sheets are not representing 100% virgin productions of steel, since all production routes include a specified amount of scrap input that is not carrying any upstream burden. The user that requires data for 100% virgin production would thus have to add the upstream burden of a supply of virgin raw materials (hot metal) equivalent to the amount of scrap input reported in the IISI data. In view of the frequency with which data for 100% virgin steel are required in LCA’s of steel products (cf. appendix 5), we recommend that IISI provides specific warning and guidelines with respect to the use of the data in this context. Due to the composition of the IISI membership, the IISI data can generally be said to be representative of modern plants. In the present update, average data for North America was not provided, but instead included in the category “Rest-Of-the-World.” Since this category is very diverse, the regionally specific data from the 1997 study must still be regarded as more representative for North America, than the “Rest-Of-the-World” data of the updated study. 6. Transparency and consistency of the study report The report gives a fair, complete and accurate description of the study and its results. We understand from our discussions with IISI that individual users may request more disaggregated data, such as separate data for hot metal (pig iron) and datasets excluding the electricity supply, which


58

will increase applicability of the data in a specific LCA context (and thus reduce misuse). We do recommend that IISI make such data available in a standard form, thus avoiding the need for specifications, calculations and estimates by individual users. In the same line, we would recommend that non-accounted emissions be included in the standard data sheets, with adequate warnings on the lacking representativity of the data for these emissions. In general, we recommend that IISI revisit its publication strategy, so that as much of this high quality information can find its way to the prospective users, including the integration into generic, public databases, preferably in a modular form that allows the users to add or substitute upstream processes and system expansions as appropriate.

7. Conclusion The International Iron and Steel Institute has done a commendable job in the planning, design and implementation of the IISI Worldwide LCI Database for Steel Industry Products. This database will be a valuable resource for LCA studies involving steel products. We have found this LCI study well constructed and adhering to the requirements of the International Standards ISO 14040, 14041 and 14043 relating to Life Cycle Inventory Analysis (LCI), with a few reservations:

- We missed a listing of data quality requirements as specified in ISO 14040, clause 5.1.2.3, in spite of data quality generally being well documented, especially for the core steel manufacturing processes.

- We did not find the requirement in ISO 14041, clause 6.4.2 to be fulfilled with respect to excluded upstream processes and the treatment of data gaps in the upstream processes. This also implies that we found the sensitivity analyses inadequate to assess and describe the potential effects of these exclusions on the outcome of the study, as required in ISO 14041, clause 5.3.5.

- The interpretation section of the draft report did not conform to ISO 14043, especially with respect to interpreting the results in relation to the goal and scope (applications) of the study.

Our critical remarks should be seen as suggestions for improvement and does not challenge our overall impression of a very thorough and dedicated study, which contributes significantly to the state-of-the art of practical LCI studies. We wish to express our gratitude to IISI for providing the opportunity to review this work in detail, and for the constructive atmosphere in which our comments have been received. Copenhagen, 25th of June 2002 Bo Weidema Atsushi Inaba Gregory A. Keoleian Chairman


59

IISI Response to Critical Review Panel Report IISI welcomes the comments made by the critical review panel, and is in full agreement with the majority of the critical review panel report. IISI believes that the critical review process was an essential step in the Worldwide LCI for steel products and considers that the report adds value to the methodology report. In the light of the critical review comments, the following opportunity is taken to address some of the issues, on a case-by-case basis, in the context of the current IISI position, and for the future use of the study. In view of the comments made on upstream data quality, IISI, as part of it’s continuous improvement commitment, will work to obtain and use better upstream data for the worldwide LCI study for steel products. IISI accepts that due to the large number of upstream modules in the model, not all will have the same allocation procedure and data quality. The standards required by the database owner for the software model are high, allocation procedure is detailed in the information facility of each module used, and is subsequently listed in full in Appendix 6 of the IISI methodology report. IISI accepts that with respect to ISO14041, clause 5.1.2.3, particularly precision and uncertainty, the information is limited due to the fewer datasets available for input to the study, but otherwise we consider this to be adequate for the purpose of the study, and upstream data (mainly from DEAM) data quality information is available. Upstream data sourced directly by IISI (e.g. iron ore) is consistent with the data quality of the primary data for steel processes. As described above, primary data was pursued for important upstream processes such as coal, lime, and cement but only limited response was received, therefore we agree that the quality of the upstream data could be improved and will be an area for continuous improvement. IISI believes that these data are sufficiently transparent and accurate to justify their inclusion in the study, and to expand the database in the future. In view of the comments made on the completeness of the interpretation, the IISI believes that the whole report contributes to the interpretation phase, and that the report has been enhanced to cover aspects of interpretation covered in ISO14043. Overall, we believe that the results have been analysed, and the limitations of the study explained, to provide the transparency and confidence that these data are adequate for LCA’s using steel.

1. In response to the comments on electricity supply. This question relates to our study assuming that exported energy from the sites would substitute the energy mix of the national grid. The critical review panel thought it would be more appropriate to substitute for regional grid suppliers, as this may reflect the more common cross border electricity markets. IISI can facilitate this on request.

2. In response to the comments on reporting core steel process data. The reporting of separate data for separate component processes was not an original goal of the study, but this can be facilitated by IISI on request.

3. In response to the comments on ‘non-accounted emissions’. Data on non-accounted emissions (those not appearing on the datasheets) are available on request. The reason that these data are not included on the standard datasheets is that data quality for these articles is low and potentially unreliable.

4. In response to the comments on a steel plant model for validation. The option of a model was not included in the original goal of the study, but can be considered by the member companies for future developments.

5. In response to the comments on the use of straight averages. It was considered that weighted average according to production tonnage was not appropriate since not all steel plants were represented. Furthermore straight averages facilitate interpretation and benchmarking exercises. In general, it is believed that the sensitivity of the results to the averaging options is low, especially compared to other sources of uncertainty such as the upstream models.

6. In response to the comments on completeness, & I/O tables. It is clear that the study does not include administration, services, marketing and sales. In view of the structure of the industry, we believe that this will be a small contribution to the LCI for steel products.


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7. In response to the comments on a comparison to the original study, IISI have formed a separate work group to analyse the results of the IISI Worldwide LCI’s for steel products.

8. In response to the comments on 100% virgin hot metal, 100% virgin hot metal is a theoretical scenario that has been used in Appendix 5 of the report to provide methodological guidance for dealing with steel and recycling.

9. In response to the comments on disaggregated data. IISI member companies can currently supply customised data dealing with specific requests, but to standardise data to cover the range of potential requests is a large resource implication. We believe it is more practical to deal with the requests case by case.

10. In response to the comments on IISI publication strategy. The IISI will revisit its publication strategy, and in view of the comments will be more flexible in providing, and offering a range of data.

Documents

IISI iron steel database d5710975-d600-11da-a94d ......Worldwide LCI Database for Steel Industry Products 1 CONTENTS EXECUTIVE SUMMARY.....3