Life cycle assessment of coal based direct-reduced iron …kth.diva-portal.org/smash/get/diva2:1507502/FULLTEXT01.pdf · 2020. 12. 8. · SE-100 44 STOCKHOLM Life cycle assessment

Master of Science Thesis

KTH School of Industrial Engineering and Management

Energy Technology EGI-2018-2020

Division of Energy Systems

SE-100 44 STOCKHOLM

Life cycle assessment of coal based

direct-reduced iron production in

India

Sabarish Elango

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Master of Science Thesis EGI 2018:2020

TRITA-ITM-EX 2020:509

Life cycle assessment of coal based direct-

reduced iron production in India

Sabarish Elango

Approved

October 2020

Examiner

Dilip Khatiwada

Supervisor

Dilip Khatiwada

Commissioner

Contact person

Deepak Yadav

Tirtha Biswas

Abstract The production of iron through direct reduction (Direct-Reduced Iron; DRI) involves the use of natural

gas or coal to reduce iron ore to iron through carbothermic reactions at a temperature below its melting

point, negating the need for a blast furnace as otherwise required. In India, around 25% of iron is produced

through direct reduction. However, there is a high reliance on coal (79% of DRI production capacity)

causing significant energy use and emissions from production. Also, a large portion of raw materials

(especially coal) is imported due to low quality of domestic resources. To understand the overall supply

chain impact of DRI or sponge iron production, a life-cycle assessment is carried out on four stages:

mining, transport, beneficiation (i.e. ore pellet-making and coal washing) and DRI production. Around 315

coal-based DRI production plants are mapped using GIS to identify clustering of plants in major iron-

producing regions. Weighted average specific energy use and emissions is calculated for seven such clusters

(using total cluster capacity), based on regional raw material qualities and transport distances from various

mines, ports and beneficiation plants. The results suggest an overall specific (per tonne DRI) energy

consumption of 27.24 GJ with an emission of 2.8 tCO2eq, 2.6 kgNOx, 1.8 kgSOx and 1.4 kgPM2.5. The

specific energy and emission values are used to calculate the total annual emissions by multiplying with the

2019 DRI production amount of 27.8 million tonnes. The annual midpoint and endpoint impacts as per

ReCiPe 2016 (country-wise factors where applicable) are then calculated. The DRI industry causes 77.31

million tCO2eq/year in global warming potential, 59.02 thousand tSO2eq/year in acidification potential and

287.2 thousand tPM2.5eq/year in fine dust formation potential. It is estimated to cause approximately

270,000 years of reduction in overall human life and 230 species years of species loss (mainly in terrestrial

ecosystems). Different sensitivities are carried out to understand the impact of some key influencing

parameters (effect of ore quality and coal quality, effect of imports of ore and coal). Some development

scenarios, such as increasing coal washery capacity, shifting land transport from road to rail, increasing

waste-heat recovery penetration, effect of stricter regulations, etc. are discussed, along with pathways for

fuel-switching from coal to natural gas, and then from natural gas to hydrogen.

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Abstrakt Järnsvamp producerat genom direktreduktion (Direct-Reduced Iron; DRI) innefattar användning av

naturgas eller kol för reduktion av järnmalm till järn genom karboterma reaktioner vid en temperatur under

dess smältpunkt, vilket eliminerar behovet av en masugn. Ca 25% av järnproduktionen i Indien sker genom

direkt reduktion. Energin kommer i 73% av fallen från kol, vilket gör processen är väldigt kolberoende. I

sin tur bidrar detta till en betydande energianvändning och stora koldioxidutsläpp. Låg kvalité på inhemska

resurser (framförallt kol) kräver import av en stor del av processråvarorna. För att förstå den övergripande

effekten av försörjningskedjan av DRI- eller svampjärnproduktionen genomfördes en livscykelbedömning

i fyra steg - gruvdrift, transport, anrikning (dvs tillverkning av malmpellets och koltvätt) och DRI-

produktion. Cirka 315 kolbaserade DRI-produktionsanläggningar kartlades med GIS för att identifiera

kluster av produktionsanläggningar i större järnproducerande regioner. Vägt genomsnittligt specifikt

energianvändning och utsläpp beräknades för sju sådana kluster (med total klusterkapacitet) baserat på

regionala råvarukvaliteter och transportavstånd från olika gruvor, hamnar och förädlingsanläggningar.

Resultaten indikerar en total specifik energiförbrukning på 27,24 GJ/ton DRI med ett utsläpp på 2,8

tCO2eq, 2,6 kg NOx, 1,8 kg SOx och 1,4 kg PM2,5 per ton DRI. De specifika energi- och utsläppsvärdena

användes för att beräkna de totala årliga utsläppen genom att multiplicera med DRI-produktionsmängden

för 2019, vilken motsvarade 27,8 miljoner ton. Den årliga mitt- och slutpunktseffekten enligt ReCiPe 2016

(landsmässiga faktorer där så är tillämpliga) beräknades sedan. DRI-industrin har en global

uppvärmningspotential motsvarande 77,31 miljoner tCO2eq/år, 59,02 tusen tSO2eq/år i

försurningspotential och 287,2 tusen tPM2,5eq/år i potential för bildning av fint damm. Detta beräknas

förkorta livslängden hos människor världen över med total cirka 270 000 år och 230 år av artförlust (främst

i markbundna ekosystem). En känslighetsanalys utfördes för att förstå effekterna av viktiga påverkande

parametrar (effekt av malmkvalitet och kolkvalitet, effekt av import av malm och kol). Vissa

utvecklingsscenarier, såsom ökad koltvättkapacitet, skifte i transport från väg till järnväg, ökad penetration

av spillvärmeåtervinning, effekten av strängare föreskrifter etcetera diskuterades tillsammans med

tillvägagångssätt för bränsleomkoppling från kol till naturgas och sedan från naturgas till väte.

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Acknowledgment I extend my sincere gratitude to my KTH supervisor, Dr. Dilip Khatiwada for the patience and support

extended to me over the period of my work. His guidance regarding the methodology for the life-cycle

assessment and the structuring of the study has been indispensable. He played a major role in ensuring the

timely progress of work and correctness of the data and calculations used.

I convey my heartfelt thanks to my internship supervisors, Dr. Deepak Yadav and Mr. Tirtha Biswas at the

Council on Energy, Environment and Water (CEEW) in New Delhi, India, for making this work possible.

Their joint efforts and support extended to me during my internship at CEEW have provided immeasurable

value to my thesis work. Such extensive data collection would have been difficult without their support.

Their suggestions and comments have helped provide narrative and structure to the work overall. Their

efforts in reading and checking the model have been useful to ensure the validity of the data, assumptions

and calculations used in the study.

I express my gratitude to CEEW, its administrators and staff, for allowing me to develop this work in

parallel with my internship responsibilities, and for providing a warm and welcoming atmosphere over the

course of the six months of my work.

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Table of Contents Abstract ......................................................................................................................................................................... 2

Acknowledgment ......................................................................................................................................................... 4

1. Introduction ......................................................................................................................................................... 7

1.1. Literature review ........................................................................................................................................ 7

1.2. Data collection ........................................................................................................................................... 7

1.3. Research objectives ................................................................................................................................... 8

1.4. Thesis structure .......................................................................................................................................... 8

2. Overview of direct reduction process ............................................................................................................. 9

3. Methodology and data sources ....................................................................................................................... 10

3.1. Goal and scope ........................................................................................................................................ 10

3.2. Life-cycle inventory and impact assessment ....................................................................................... 12

3.3. Application of LCI data .......................................................................................................................... 20

4. Results and sensitivity analysis ........................................................................................................................ 23

4.1. Sensitivity analysis .................................................................................................................................... 26

5. Discussion and outlook ................................................................................................................................... 30

5.1. Study outcomes ........................................................................................................................................ 30

5.2. Limitations ................................................................................................................................................ 31

5.3. Suggested developments ......................................................................................................................... 31

6. Conclusion and policy implications ............................................................................................................... 33

Bibliography ................................................................................................................................................................ 34

Annex ........................................................................................................................................................................... 39

List of Figures Figure 1a: Locations of various DRI plants and facilities in India....................................................................... 8

Figure 1b: Locations of various coal-DRI plants in India ..................................................................................... 8

Figure 2: Rotary kiln (SL/RN process) .................................................................................................................... 9

Figure 3: LCA phases according to ISO14040:2006 ............................................................................................ 11

Figure 4: Process flowchart for LCA ...................................................................................................................... 11

Figure 5: Locations of DRI plant clusters .............................................................................................................. 20

Figure 6: Transport schemes .................................................................................................................................... 22

Figure 7: Specific life cycle midpoint impacts ....................................................................................................... 24

Figure 8: Effect of iron ore grade............................................................................................................................ 26

Figure 9: Effect of coal grade................................................................................................................................... 26

Figure 10: Effect of coal beneficiation share ......................................................................................................... 27

Figure 11: Effect of ore imports .............................................................................................................................. 28

Figure 12: Effect of coal imports ............................................................................................................................ 28

Figure 13: Effect of road vs. rail for land transport ............................................................................................. 29

Figure 14: Effect of WHR share ............................................................................................................................. 29

List of tables Table 1: India-average grid emission factors ......................................................................................................... 13

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Table 2: Energy and emission factors for coal mining – India ........................................................................... 14

Table 3: Energy and emission factors for coal mining – Indonesia .................................................................. 14

Table 4: Energy and emission factors for coal mining – South Africa ............................................................. 14

Table 5: Energy and emission factors for iron ore mining – India .................................................................... 15

Table 6: Energy and emission factors for iron ore mining – Australia ............................................................. 15

Table 7: Energy and emission factors for iron ore mining – South Africa ...................................................... 15

Table 8: Energy and emission factors for dolomite mining ................................................................................ 16

Table 9: Number of trucks in operation ................................................................................................................ 16

Table 10: Truck emission norms ............................................................................................................................. 16

Table 11: Energy and emission factors for trucks ................................................................................................ 17

Table 12: Energy and emission factors for railways ............................................................................................. 17

Table 13: Fuel-specific emission factors and fuel consumption of dry-bulk carriers ..................................... 17

Table 14: Energy and emission factors for ships .................................................................................................. 18

Table 15: Energy and emission factors for coal beneficiation ............................................................................ 18

Table 16: Energy and emission factors for iron ore beneficiation ..................................................................... 18

Table 17: Typical raw material input quantities for DRI production ................................................................ 19

Table 18: Energy and emission factors for DRI production .............................................................................. 19

Table 19: Potential for electricity from WHRB .................................................................................................... 19

Table 20: Potential energy and emissions offset by WHR power plant ............................................................ 20

Table 21: Capacities of different representative plants ........................................................................................ 21

Table 22: Assumptions of iron ore and coal quality ............................................................................................. 21

Table 23: Example of overall transport emissions (for Jamshedpur cluster) ................................................... 22

Table 24: Overall specific energy use and emissions ............................................................................................ 23

Table 25: Midpoint and endpoint life cycle impacts ............................................................................................ 23

Table 26: National annual life-cycle energy use and emissions (with and without WHR) ............................. 25

Table 27: National annual life-cycle impacts (with and without WHR) ........................................................... 25

List of abbreviations DALY disaility-adjusted life years

DRI direct-reduced iron

LCA life cycle assessment

LCI life cycle inventory

LCIA life cycle impact assessment

GWP global warming potential

AP acidification potential

FP formation potential

GCV gross calorific value

VM volatile matter

TPD tonnes per day

WHR waste-heat recovery

WHRB waste-heat recovery boiler

MTPA million tonnes per annum

FC fixed carbon

LNG liquefied natural gas

HFO heavy fuel oil

FO furnace oil

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1. Introduction On a global level, the iron and steel industry is known for its significant energy use and emissions,

amounting to around 8.3% (IEA, 2019) to the total worldwide energy consumption (IEA Statistics, 2019)

and 7-9% of total CO2 emissions (World Steel Association, 2019a) as of 2017. India is the 2nd largest steel

producer in the world as of 2018, after China, producing 106.5 MT (The Economic Times, 2019). India’s

iron and steel industry is highly dependent on coal for its energy needs, thus causing it to be highly GHG

intensive. It emits approximately 2.3 tCO2/tonne of iron (Pal, Gupta and Kapur, 2016), well above the

global average of 1.83 tCO2/tonne of steel (Word Steel Association, 2019a).

India is the world's largest producer of Direct-Reduced Iron (DRI) or sponge iron, with the coal-based

production method accounting for 79% of the total DRI production in the country (the remainder is gas-

based) in 2018 (Indian Bureau of Mines, 2019). The DRI industry in India has seen enormous growth, with

installed capacity increasing from 1.52 million tonnes per annum (MTPA) in 1990-91 to around 49.62

MTPA in 2017-18. Production of sponge iron grew from 0.9 million tonnes in 1990-91 to 30.51 million

tonnes in 2017-18 (Indian Bureau of Mines, 2019). The National Steel Policy of 2017 projects an increase

in domestic demand for sponge iron to 80 million tonnes by 2030-31; sponge iron capacity may increase to

114 million tonnes by 2030-31 with nearly 30% share of gas-based plants (Ministry of Steel, 2017). Coal use

in India is projected to grow by nearly 3% per annum from 2015-2040 due to iron and steel industry growth

and relatively intensive coal use in DRI (U.S. EIA, 2017). Due to the large percentage of DRI production

for ironmaking in India, there is a great potential for emissions and energy use reductions/offsets in the

future. Considering that the large DRI share is contributed to mainly by small- and medium-scale producers,

upscaling of the DRI process and integration with modern heat recovery, renewable energy and energy

conservation techniques could likely be the way forward. Hence, the direct reduction process for sponge

iron (DRI) production is the focus of the LCA study.

The study analyses the impact of national DRI production – from the mining of coal, iron ore and dolomite;

beneficiation of coal and iron ore; transportation of raw materials through road, rail and sea (a large quantity

of coal and significant amounts of iron ore are imported), and the DRI production process itself. The LCA

estimates the energy use and emissions. GHGs and local pollutants (SOx, NOx and PM2.5) provide

important and interesting insights into the impacts caused to human life and ecosystems through global

warming, acidification and particulate matter pollution.

1.1. Literature review Published research on the iron and steel industry from the LCA perspective exist, such as the work of

Olmez et al, 2016. Also, works such as by Cavaliere, 2019, detail the different GHG reductions possible in

the iron and steelmaking processes. Studies specific to the DRI industry in India are important, since there

are many smaller plants which do not have integrated steelmaking capabilities and are typically more

inefficient than larger integrated steel plants. Recent works on the DRI industry such as those by Chatterjee,

2010, Sarangi and Sarangi, 2011, Dey et al, 2015, Kumar and Khanam, 2012 and Prasad et al, 2011 look at

the DRI production process, its specific parameters and the potential for energy conservation primarily

through waste heat recovery. However, a study quantifying the impact of the industry in terms of overall

energy inputs and emissions (from resource extraction, transformation and consumption) has not been

carried out.

A robust LCA that captures the process variability, its effect on energy use and emissions is required to

understand the true opportunities and challenges over the future years of growth for the sector. A cradle-

to-gate LCA is developed for this purpose, to understand the impacts of DRI production from the energy

use and emissions produced from the supply chain.

1.2. Data collection The direct reduction process of iron production is modelled based mainly the work of Sarangi and Sarangi,

2011. Indian Bureau of Mines, 2019 suggests that the Indian industry predominantly relies on coal based

DRI production using the rotary kiln technology (MIDREX, 2019). Only 3 facilities use the natural gas-

based process. Based on data from the Indian Bureau of Mines, 2019 and online mapping services, a

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database is built for major sponge iron plants, coal mines, iron ore mines, dolomite mines, coal washeries,

pellet plants and import sources (Figures 1a and 1b). These are mapped using the open source geographic

information system software QGIS (QGIS, 2020) to identify clusters and spatial spread across the country.

1.3. Research objectives The objective of the study is to understand the environmental impact of coal-based sponge iron plants

across different locations in India from a life cycle perspective (cradle-to-gate). The inventory of emissions

from mining of raw materials, transportation, beneficiation of raw materials and production of DRI are

developed. Life cycle impact assessment methodology (RIVM, 2018) is used to quantify the environmental

impact of this industry on various parameters, at midpoint and endpoint levels. The midpoint level impacts

show the environmental problems (such as global warming, acidification and PM formation), while the

endpoint level impacts show the impacts on higher levels such as human health and biodiversity. Overall,

the study aims to improve understanding of the impact of the Indian DRI industry on national and global

emissions, provide impetus for stricter policymaking and to point out gaps in public knowledge of the

functional aspects of the industry.

This thesis will answer the following key research questions:

RQ1. What are the energy use and emissions (of GHG, SOx, NOx and PM2.5) from the Indian DRI

industry?

RQ2. What are the environmental impacts of the Indian DRI industry on human life and ecosystems?

RQ3. What are the potential changes and improvements to reduce the impact of DRI production?

The sensitivity analysis considered the variations of different factors such as efficiency, transport modes,

technologies, etc. of the product supply chains of coal, iron ore and limestone, and of the core iron

production process.

1.4. Thesis structure The thesis provides an overview of the direct reduction process (Section 2) using the rotary kiln technology

and the reactions involved in the production of iron. The methodology section (Section 3) defines the goal

and scope of the thesis and details the emission inventory data of various life cycle steps used for the life

cycle impact assessment. Section 3.3 discusses the method used to apply the inventory data to obtain the

Figure 1b: Locations of various coal-DRI plants in India (Source: Mapped by author based on Indian

Bureau of Mines, 2018 and QGIS,2020)

Figure 1a: Locations of various DRI plants and facilities in India (Source: Mapped by author based on

Indian Bureau of Mines, 2018 and QGIS, 2020)

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national contribution of the DRI industry. The results are provided in Section 4 in terms of average

emissions and life cycle impacts per tonne of DRI, and total emissions and life cycle impacts resulting from

one year of national DRI output. A sensitivity analysis is performed in Section 4.1 to account for

uncertainties in some variables considered. Section 5 discusses the results and outcomes of the thesis, and

provides some suggestions to abate the impact of the DRI industry in future.

2. Overview of direct reduction process The basic mechanism behind iron production involves two main pathways,

i. Using a blast furnace (heated using coal or natural gas) for reduction of iron ore (iron oxides) into

pig iron by reaction with coke and fluxes (usually limestone) (SAIL, 2012). The molten pig iron is

then converted to steel (through the steelmaking process, usually with a basic oxygen furnace) or

processed and sold as such. In 2019, 46.7% of India’s steel industry utilised the blast furnace-basic

oxygen furnace (BF-BOF) method (World Steel Association, 2019b).

ii. Using coal (solid or gas) or reformed natural gas to perform a direct reduction of the iron ore into

Direct-Reduced Iron (DRI) or Sponge iron at high heat (but below melting point) (Sarangi and

Sarangi, 2011). The sponge iron is then converted to steel (with an electric arc or electric induction

furnace) or processed and sold. The share of electric induction/arc furnace processes in India

constituted 53.3% in 2019 (World Steel Association, 2019b).

The SL/RN process (developed by Steel Company of Canada, Lurgi Chemie, Republic Steel Company and

National Lead Corporation in 1964) forms the basis of rotary kiln technologies used in India (Sarangi and

Sarangi, 2011); the process uses a rotary kiln into which iron ore pellets, non-coking coal (for reduction)

and limestone/dolomite (flux) is supplied. From the other end, air and coal (for combustion) are supplied.

The resulting high temperatures (900 to 1020 °C) form a reducing atmosphere of CO which reduces the

iron ores to sponge iron. The sponge iron is subsequently separated out of the remaining reaction products

through magnetic separation. The kiln is inclined at an angle of ~2.5° to facilitate movement of the charge

from the feed end to the exit. The rotary motion encourages even reaction of the charge through mixing

with the reducing gases (Dey et al, 2015). The basic process is shown in Figure 2.

Around a third of the kiln length is typically required for preheating the charge consisting of iron ore, coal

and dolomite. The dolomite flux is added to control sulphurisation. The coal supplied along with the ore is

mainly meant to produce reducing gas by reacting with atmospheric oxygen at high temperature. In this

stage, the iron ore (predominantly haematite - Fe2O3) is partially reduced to ferrous oxide. After reaching

the ideal reaction temperature of 900-1100 °C, the ore is reduced to metal in the latter portion of the kiln

through further reduction. The following are the main reactions taking place within the kiln, at a

temperature of 1067 °C (Sarangi and Sarangi, 2011).

3𝐹𝑒2𝑂3 + 𝐶𝑂 → 2𝐹𝑒3𝑂4 + 𝐶𝑂2 − 44.46 𝑘𝐽/𝑚𝑜𝑙 (1)

Figure 2: Rotary kiln (SL/RN process) (Source: Dey et al, 2015)

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𝐹𝑒3𝑂4 + 𝐶𝑂 → 3𝐹𝑒𝑂 + 𝐶𝑂2 + 3.07 𝑘𝐽/𝑚𝑜𝑙 (2)

𝐹𝑒𝑂 + 𝐶𝑂 → 𝐹𝑒 + 𝐶𝑂2 − 11.12 𝑘𝐽/𝑚𝑜𝑙 (3)

The CO required for the above reduction reactions is produced when fixed carbon of the feed-end coal

reacts with CO2 produced by the reductions, in a perpetual, reversible reaction called Boudouard reaction.

𝐶 + 𝐶𝑂2 ⇋ 2𝐶𝑂 + 167.52 𝑘𝐽/𝑚𝑜𝑙 (4)

This reaction is crucial to maintaining the reducing atmosphere and kiln temperature. The ratio of

CO/(CO+CO2) depends on the temperature inside the kiln; ideally a CO concentration of ~50-60% is

maintained (Dey, Prasad and Singh, 2015) to ensure optimum reduction of ore. Since the forward reaction

(4) is highly endothermic, it serves to maintain kiln temperature for a regulated combustion of injection

coal. By combining the above reactions, we get

2𝐹𝑒2𝑂3 + 3𝐶 → 4𝐹𝑒 + 3𝐶𝑂2 + 432.52 𝑘𝐽/𝑚𝑜𝑙 (5)

Note that only one part of CO produced in (4) is used for the reduction, whereas the other part is

combusted into CO2 resulting in a net output of CO2 from the kiln. Various other reactions take place due

to the combustion of injection coal fixed carbon and volatiles, causing the formation of additional CO and

CO2 along with H2O and CH4. The sulphur present in coal is removed by dolomite, as the CaCO3 and

MgCO3 decompose into CaO and MgO to act as desulphurising agents. The addition of dolomite is crucial

to control the sulphur content in the DRI (to prevent embrittlement in steel production), and also to control

SOx emissions (Sarangi and Sarangi, 2011).

After the reduction process, the metal (now known as sponge iron or DRI) is separated from the remaining

slag (consisting of coal char, unreacted coal, sulphurated dolomite) through magnetic separation. The

product CO2 reacts further with incoming/excess coal to produce more CO. Thus, for a low ash coal with

high reactivity, the reduction efficiency will be higher as the quantity of coal input would be reduced. Also,

the retaining time of the ore within the kiln can be lower, thus improving output (Dey et al, 2015).

3. Methodology and data sources A framework is developed for the life-cycle assessment based on the

literature review and data collection. The LCA will be cradle-to-gate

in nature (Haque and Norgate, 2015) since sponge iron is an

intermediate product in steel production. The LCA will be carried

out using Microsoft Excel, as per the guidelines set forth in the ISO

14044 standard. Due to the nature of the study (in associating a part

of environmental impact with the investigated product), availability

of data in averages, consideration of factors such as feasibility,

accuracy, comprehensibility, inspiration and robustness have caused

the chosen LCA type as attributional (Ekvall, 2019). The four stages

of the LCA applied in this work as per ISO 14044 include,

i. determination of the goal and scope

ii. inventory analysis of inputs and outputs

iii. assessment of environmental impact

iv. interpretation of results

3.1. Goal and scope The research objective is to calculate the environmental impact of energy use and emissions from coal-

based sponge iron plants across different locations in India from a life cycle perspective (cradle-to-gate).

Various sensitivity parameters can help address the uncertainties, such as process efficiency, share of

import, etc. Additionally, potential avenues for reductions in energy and emissions intensity through various

policy instruments and technological developments are explored. The study will fill a lacuna in

understanding of the life cycle impacts of the Indian DRI industry. The results of the study will aid

Figure 3: LCA phases according to ISO14040:2006

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policymaking for environmental and emissions controls in the DRI industry and will provide impetus for

transformation of the DRI industry towards decarbonisation. The scope of the LCA is cradle-to-gate in

nature, as DRI is not the final product in the value chain. The functional unit (FU) for the study is taken as

the production of one tonne of sponge iron (DRI).

The study will assess the production of DRI in India at a specific level and at a national level. The product

system (shown in Figure 4) includes the mining of resources (coal, iron ore and dolomite), transportation

(by road, rail and sea), beneficiation of resources (iron ore pellet-making and coal washing) and the

production of DRI through the rotary kiln process.

Possible energy use and emissions reduction techniques are noted, such as waste heat recovery for onsite

power generation (Cavaliere, 2019). Onsite power generation is quite common among larger DRI

production facilities (>200 TPD) (National Productivity Council, 2017), so the offset of emissions from

grid electricity is considered. The impact of introducing/scaling up technologies such as natural gas-based

DRI (which has much lower emissions) (Cavaliere, 2019) and hydrogen-based DRI (which can have zero

emissions) (Hybrit, no date) are also explored.

3.1.1. System boundaries and allocation The product system is restricted to energy use and emissions from direct consumption of resources, except

for the generation of grid electricity. Energy use and emissions from secondary processes such as oil

refining, import of oil for fuel, production of coke for pellet-making, etc. are not considered due to lack of

pertinent data and information, and lack of bandwidth to carry out additional data collection. The effect of

Figure 4: Process flowchart for LCA Note: Red – processes, grey – sub-processes, blue – fuels, orange – electricity

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by-products and wastes of DRI production, such as dust, slag and char are also not considered, due to the

high degree of variability in the processing of such materials and lack of regulations (Bhushan et al., 2011).

In most cases, waste materials are landfilled onsite (Bhushan et al., 2011).

For industrial processes with more than one material output, the impacts are proportionally allocated to

each output as per ISO 14040:2006. However, the only significant by-product of DRI production is char,

which has value as a combustion fuel. However, as mentioned above, the variations in handling of such

materials prevented allocation factors from being applied. Similar is the issue with coal bed methane, as the

status of methane capture in coal mines being considered is unknown.

3.1.2. Data and limitations For carrying out an LCA at the national level, data regarding the DRI production process through rotary

kiln technology is required. The major resources being consumed by the production process include iron

ore, coal, dolomite and electricity. Hence, data on extraction of raw materials (domestic and foreign) is

required, as is the fuel consumption and distance covered in transporting the raw materials (road, rail and

sea). Since a portion of coal is washed and iron ore pelletised, data on beneficiation processes are collected.

Some limitations of the study include the constant grid emissions and vehicular emissions inventory for

mining of raw materials in both India and other countries. For vehicular emissions, the emission norms

have been used as a baseline for the typical emission levels. The prevalence and techniques used for iron-

ore pellet making in India are not well understood, hence, only the effects of furnace oil and electricity use

are considered (KIOCL, 2019). The grade of raw materials being consumed in each region of the country

is assumed – more granular data would improve the accuracy of results. For DRI production, the prevalence

of emissions control technologies is not known; older data of SOx, NOx and PM2.5 have been adapted in

this study (CPCB, 2007). The complexities of the DRI process require real time data from various

production facilities to be represented more accurately.

3.2. Life-cycle inventory and impact assessment To conduct a detailed Life Cycle Assessment, the effects of various processes further up the supply chain

need to be considered and quantified. In this case, the processes being considered are shown in Figure 4.

i. Grid emission factors for electricity consumption are calculated from published LCA studies of

different power generating technologies. For consumption of other fuels (diesel, FO, etc.) the

emissions from point of combustion are considered, using emissions norms (DieselNet, 2018;

CPCB, 2017; Transportpolicy.net, 2019).

ii. In the primary level, impacts from mining/extraction (and import, if applicable) of coal, iron ore

and dolomite are considered for the sponge iron production. In the case of coal, imports from

South Africa and Indonesia are considered (Indian Bureau of Mines, 2019); for iron ore, imports

from South Africa and Australia are considered (Indian Bureau of Mines, 2019). The transport of

extracted resources through road, rail and sea are considered. The mining and transport

technologies are assumed to be constant across different countries for simplicity.

iii. In the secondary level, iron ore processing (pellet-making) and coal beneficiation (washing) are

considered.

iv. At the final use level, the iron production process and its energy use and emissions are included.

For this, details regarding the production facility such as capacity, location and key process metrics

are found from published data. The processing of wastes from production are not addressed, since

there is a large degree of variability involved (such as the combustion of char, local landfilling,

selling of char, etc.) (Bhushan et al., 2011).

Life cycle inventory (LCI) data is derived from literature sources. For the DRI production process, literature

references are used to develop a heat and mass balance model, along with certain reaction parameters to

obtain the emission factors for given inputs. Since the process deals with the production of a single product,

allocation issues are considered predominantly insignificant (Haque and Norgate, 2015).

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The LCI will provide Scope 1 (direct) and Scope 2 (indirect, from grid electricity) energy use in MJ and

emissions in GHG (CO2, CH4) as kgCO2eq; SOx, NOx and PM2.5 in g (respectively). Impacts of the life

cycle will be assessed through the ReCiPe 2016 LCIA methodology. ReCiPe LCIA translates emissions and

resource extractions into environmental impact scores using characterisation factors at two levels –

midpoint level and endpoint level (RIVM, 2018). Of the three time-horizon options within ReCiPe, a

hierarchical (100-year) time horizon is considered for this study. The impacts are assessed in terms of

country-specific global warming potential (GWP) in kgCO2eq, acidification potential (AP) in gSO2eq and

fine dust formation potential (fine dust FP) in gPM2.5eq, in the midpoints; effect on human health (in

disability-adjusted life years or DALY), terrestrial ecosystems and aquatic ecosystems (in time integrated

species loss, i.e., species years) in the endpoints.

3.2.1. Grid electricity Emissions factors are estimated for the electric power grid using the energy mix in generation of India

(Central Electricity Authority, 2019) and specific emission factors from literature for certain exemplary

power generation technologies such as coal, nuclear, hydro, renewables, etc. Table 1 shows the emissions

factors of various pollutants calculated using weighted average of the generation mix.

Table 1: India-average grid emission factors

Generation

share %

GHG

g/kWh

SOx

g/kWh

NOx

g/kWh

PM2.5

g/kWh

Reference(s)

Thermal 74.52% 953.77 4.72 3.22 0.94 (Singh, Sharma and Mahapatra, 2016)

(Guttikunda and Jawahar, 2018)

(Seal and Bose, no date)

Hydro 10.46% 28.40 0.11 - - (Pang et al., 2015)

Nuclear 2.76% 18.50 0.02 0.04 0.01 (Hatch, 2014)

Gas 3.63% 626.00 0.02 0.32 0.04 (GREET, 2019)

Solar 2.86% 69.39 0.57 0.20 - (Yu and Halog, 2015)

Wind 4.52% 10.50 0.03 0.03 0.02 (Hatch, 2014)

Weighted average 748.63 3.55 2.42 0.70

3.2.2. Mining For the mining processes, diesel and electricity are the fuels considered, as typically cited in the given

references. The impact of explosives is not included. For diesel consumption, the emission factors

prescribed for non-road diesel engines (construction) (DieselNet, 2018); CO2 emissions are calculated as

stoichiometric CO2 output from combustion minus carbon going as CO, to reflect the incomplete

combustion. For electricity, the grid emission factors found in Table 1 are applied. In case of fugitive

emissions, data from GREET, 2019 is used.

a. Coal mining

India Energy use and emissions data for coal mining are obtained from Coal India Limited, 2017, which is the

world’s largest single coal producer. In India, Coal India Limited has a market share of 83% in coal

production (Coal India Limited, 2020). The sustainability report published by CIL in 2016-17 provides the

amount of diesel and electricity consumed in mine activities (the two main fuels), for which specific

consumption data is calculated based on the coal production in 2016-17. Fugitive methane emissions data

from coal mines is obtained from the GREET database (Argonne National Laboratory, 2019).

Diesel consumption: 0.79 l/tonne coal (or) 8.12 kWh/tonne coal

Electricity consumption: 8.82 kWh/tonne coal

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Table 2: Energy and emissions factors for coal mining – India (Source: Coal India Limited, 2020; Argonne National Laboratory, 2019; Author’s calculations)

Fugitive Diesel Electricity Total

Energy (MJ/tonne coal) 29.22 31.75 60.96

GHG (g/tonne coal) 32796.75 2045.69 6602.03 41444.47

NOx (g/tonne coal) 32.46 21.36 53.82

SOx (g/tonne coal) 0.55 31.28 31.84

PM2.5 (g/tonne coal) 21.20 1.62 6.19 29.01

Imports of coal from Indonesia and South Africa are considered. The emission factors for diesel and

electricity are kept constant for simplicity, but the specific consumption data is obtained for each country.

Indonesia One of the largest coal mines at Kideco is operated by Indika Energy. Data regarding fuel usage is obtained

from their sustainability report (Indika Energy, 2020). Of note is that 97% of diesel is biodiesel, hence the

biogenic CO2 is not considered. Also, most of the energy requirement is met by onsite diesel generators.



Table 3: Energy and emissions factors for coal mining – Indonesia (Source: Indika Energy, 2020; Author’s calculations)



GHG (g/tonne coal) 32796.75 868.25 217.74 33882.4

NOx (g/tonne coal) 459.28 0.70 459.98

SOx (g/tonne coal) 7.84 1.03 8.87

PM2.5 (g/tonne coal) 21.20 22.96 0.20 44.36

South Africa For South Africa, data is obtained for the largest coal mining company, Exxaro (Exxaro, 2019).



Table 4: Energy and emissions factors for coal mining – South Africa (Source: Exxaro, 2019; Author’s calculations)



GHG (g/tonne coal) 32796.75 5640.05 10331.15 48767.95

NOx (g/tonne coal) 89.50 33.42 122.93

SOx (g/tonne coal) 1.53 48.95 50.48

PM2.5 (g/tonne coal) 21.20 4.48 9.69 35.36

b. Iron ore mining

India Energy use and emissions data for iron ore mining are obtained from National Mineral Development

Corporation, 2018, which is a publicly owned mining company. NMDC is the largest iron ore mining

company in India, with a production of 35.57 million tonnes in 2017-18 (National Mineral Development

Corporation, 2018a). The sustainability report published by NMDC in 2018 specifies the diesel and

electricity consumption in its mines. Note that the same fugitive PM2.5 values are taken as in coal mining.

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Diesel consumption: 0.44 l/tonne ore (or) 4.46 kWh/tonne ore

Electricity consumption: 2.47 kWh/tonne ore

Table 5: Energy and emissions factors for iron ore mining – India (Source: National Mineral Development Corporation, 2018a; Author’s calculations)

Diesel Electricity Total

Energy (MJ/tonne ore) 16.06 8.89 24.95

GHG (g/tonne ore) 1124.13 1849.13 2973.25

NOx (g/tonne ore) 17.84 5.98 23.82

SOx (g/tonne ore) 0.30 8.76 9.07

PM2.5 (g/tonne ore) 0.89 1.73 10.50 (incl. fugitive)

Imports of iron ore from Australia and South Africa are considered. As before, the emission factors are

kept constant, but the energy consumption data is country specific.

Australia For Australia, data is obtained from (Norgate and Haque, 2010).


Electricity consumption: 3 kWh/tonne ore

Table 6: Energy and emissions factors for iron ore mining – Australia (Source: Norgate and Haque, 2010; Author’s calculations)



GHG (g/tonne ore) 6924.86 2245.90 9170.76

NOx (g/tonne ore) 109.89 7.27 117.16

SOx (g/tonne ore) 1.88 10.64 12.52


South Africa For South Africa, data is obtained for Kumba Iron Ore company, operating some of the largest mines in

the world, such as the Sishen mine (Kumba Iron Ore Limited, 2020).



Table 7: Energy and emissions factors for iron ore mining – South Africa (Source: Kumba Iron Ore Limited, 2020; Author’s calculations)



GHG (g/tonne ore) 11823.61 7945.41 19769.02

NOx (g/tonne ore) 187.63 25.71 213.34

SOx (g/tonne ore) 3.20 37.65 40.85


c. Dolomite mining In this study, it is assumed that the DRI production process uses only dolomite and no limestone. Due to

a lack of energy use data specific to dolomite mining, a calculation is made based on data published by

Directorate General of Mine Safety [DGMS], 2015. This database lists the total production of dolomite in

India and the number of pieces of equipment and their average horsepower. Based on this information and

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assumptions regarding the working hours of a dolomite mine (Shruti Seva, 2019) and the load factor of

equipment (Klanfar, Korman and Kujundžić, 2016), specific consumption data is calculated as follows:

𝑆𝑝. 𝑑𝑖𝑒𝑠𝑒𝑙 𝑢𝑠𝑒 = 𝑁𝑜. 𝑜𝑓 𝑑𝑖𝑒𝑠𝑒𝑙 𝑚𝑎𝑐ℎ𝑖𝑛𝑒𝑠 × 𝐴𝑣𝑔. ℎ𝑝 × 𝐴𝑣𝑔. 𝑙𝑜𝑎𝑑 𝑓𝑎𝑐𝑡𝑜𝑟 × 𝑊𝑜𝑟𝑘𝑖𝑛𝑔 ℎ𝑜𝑢𝑟𝑠 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟 × (1

+ 𝑡ℎ𝑒𝑟𝑚𝑎𝑙 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝑜𝑓 𝑑𝑖𝑒𝑠𝑒𝑙 𝑒𝑛𝑔𝑖𝑛𝑒)

𝑆𝑝. 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑖𝑡𝑦 𝑢𝑠𝑒 = 𝑁𝑜. 𝑜𝑓 𝑒𝑙𝑒𝑐𝑡𝑟𝑖𝑐 𝑚𝑎𝑐ℎ𝑖𝑛𝑒𝑠 × 𝐴𝑣𝑔. ℎ𝑝 × 𝐴𝑣𝑔. 𝑙𝑜𝑎𝑑 𝑓𝑎𝑐𝑡𝑜𝑟 × 𝑊𝑜𝑟𝑘𝑖𝑛𝑔 ℎ𝑜𝑢𝑟𝑠 𝑝𝑒𝑟 𝑦𝑒𝑎𝑟

Diesel consumption: 0.32 l/tonne dolomite (or) 3.26 kWh/tonne dolomite

Electricity consumption: 2.42 kWh/tonne dolomite

Table 8: Energy and emissions factors for dolomite mining (Source: DGMS, 2015; Author’s calculations)


Energy (MJ/tonne dolo) 11.75 8.71 20.46

GHG (g/tonne dolo) 815.28 1810.57 2625.85

NOx (g/tonne dolo) 13.06 5.86 18.92

SOx (g/tonne dolo) 0.22 8.58 8.80

PM2.5 (g/tonne dolo) 0.98 1.70 23.87 (incl. fugitive)

3.2.3. Transport Transport is an important part of the supply chain, especially considering that the iron industry, to an extent

relies on import of coal and iron ore. Moreover, due to the generally remote locations of mines and the

plants themselves, a fleet of trucks and/or a nearby railway is relied upon to transport most of the raw

material. Thus, the transport emissions can be broken down into road transport, rail transport and maritime

transport. For all these cases, emission factors have been estimated as elaborated in the sections that follow.

Based on location of a certain cluster, these factors are to obtain the total energy use and emissions.

a. Road transport To estimate the emissions from road transport, the currently operating fleet of trucks in India are considered

based on the lifetime of such vehicles (taken as 20 years) and annual sales data since 2005 till 2018 (MoRTH,

2017). Based on the year of sale, the number of trucks following the then-active emission standards is

found. A weighted average emission factor is then found to estimate for the current fleet based on the

historical emission norms in India (CPCB, 2017; Transportpolicy.net, 2018).

Table 9: Number of trucks in operation (Source: MoRTH, 2017)

Years active No. of vehicles % share

India 2000 2000-2005 847,142 19.50%

BS-II 2005-2010 1,178,737 27.13%

BS-III 2010-2017 1,978,604 45.53%

BS-IV 2017-2018 340,781 7.84%

Total 4,345,264 100.00%

Table 10: Truck emission norms (Source: CPCB, 2017; Transportpolicy,net, 2018)

(Units in g/kWh) CO HC NOx PM SO2

India 2000 (2000) 4.5 1.1 8 0.36 0.395

BS-II (2005) 4 1.1 7 0.15 0.079

BS-III (2010) 2.1 1.6 5 0.1 0.055

BS-IV (2017) 1.5 0.96 3.5 0.02 0.008

Weighted avg. for current vehicle mix

3.036 1.317 6.010 0.158 0.008 (fuel dependent)

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CO2 emissions are estimated by considering the stoichiometric requirement of diesel fuel to produce CO2,

i.e. 2640 gCO2/l diesel. From the CO production given by Table 10, the carbon in CO is subtracted from

that of CO2 to account for the incomplete combustion. SO2 emissions are estimated as a stoichiometric

formation from the sulphur in fuel (TransportPolicy.net, 2018). Fuel consumption data is taken for N3

rigid vehicles tested at 40 kph (International Council on Clean Transportation, 2017), calculated as 0.1516

kWh diesel/tonne-km. Thus, the emission factors for Indian trucks is found, given in Table 11.

Table 11: Energy and emission factors for trucks (Source: Author’s calculations)

Energy (MJ/tonne-km) 0.546

GHG (g/tonne-km) 37.570

NOx (g/tonne-km) 0.911

SOx (g/tonne-km) 0.001

PM2.5 (g/tonne-km) 0.024

b. Rail transport A small percentage of materials is transported through railways (for most of the distance) in instances where

the plant/mine is located near a railway system; railways are typically significantly cheaper and more reliable

(Freightera, 2019). Indian Railways operates a fleet of electric and diesel-electric locomotives (depending

on route), emissions factors for which are identified (Indian Railways, 2019) as being 0.003 litres/net

tonne-km (diesel) and 0.01 kWh/net tonne-km (electric), by applying a conversion from the given gross

tonne-km to net tonne-km values. Average daily running km of electric (387 km) and diesel goods

locomotives (351 km) provided the usage share of electric vs diesel at 52.44% vs 47.56% respectively.

While India does not yet have emission norms for diesel locomotives, an interim report from the Research

Designs and Standards Organisation prescribes some suggested standards for diesel locomotives based on

tests carried out to a certain section of the fleet (Research Designs and Standards Organisation, 2017).

These standards are applied to the specific consumption figures mentioned.

Table 12: Energy and emission factors for railways (Source: Research Designs and Standards Organisation, 2017)

Diesel Electric Weighted avg.

Energy (MJ/tonne-km) 0.122 0.035 0.077

GHG (g/tonne-km) 8.640 10.384 9.554

NOx (g/tonne-km) 0.656 0.019 0.322

SOx (g/tonne-km) 0.000 0.019 0.010

PM2.5 (g/tonne-km) 0.019 0.017 0.018

c. Maritime transport A portion of the Indian sponge iron industry relies on imported coal and iron ore (small %) to obtain better

quality. Shipping predominantly occurs from South Africa, Australia and Indonesia. Such materials are

transported using dry-bulk carriers, running predominantly on heavy fuel oil (HFO). The emissions factors

for shipping are calculated based on engine characteristics detailed by Kristensen, 2015. An average value

of the emissions per kg fuel for different engine configurations is considered as an estimate. Fuel demand

and annual tonne-km of dry-bulk carriers is given by Psaraftis and Kontovas, 2009, under the assumption

that fuel consumed per tonne-km has not changed significantly since 2009. Using the fuel-specific emission

factors of the former, and fuel consumption and tonne-km of the latter, emissions are found in g/tonne-

km; except for SOx emissions, for which recent IMO standards limit it at 0.5% fuel weight (IMO, 2020).

Table 13: Fuel specific emission factors and fuel consumption of dry-bulk carriers (Source: Author’s calculations)

(Kristensen, 2015) CO2 SOx NOx PM2.5

g/kg fuel g/kg fuel g/kg fuel g/kg fuel

Average emissions 3116.73 5.00 43.74 2.00

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Dry bulk fuel consumption 52,376 tonnes

(Psaraftis and Kontovas, 2009) Total tonne-km 37,131,605,092 tonne-km

Specific consumption 1.411 g/tonne-km

Table 14: Energy and emission factors for ships (Source: Author’s calculations)

Energy (MJ/tonne-km) 0.059

GHG (g/tonne-km) 37.570

NOx (g/tonne-km) 0.911

SOx (g/tonne-km) 0.001

PM2.5 (g/tonne-km) 0.024

3.2.4. Beneficiation

a. Coal washery Coal beneficiation involves several processes of washing and drying the coal to remove excess ash content,

thus improving the coal quality. Data for the coal beneficiation process is obtained from Chaturvedi, 1997.

The coal washery process involves primarily electrical equipment such as pumps, conveyors, cyclones, etc.

It is assumed that 20% of the coal required for DRI production is washed. The coal washery allows a typical

reduction in ash content of ~28-30% (Zamuda and Sharpe, 2007).

Electricity consumption: 17.67 kWh/tonne raw coal

Table 15: Energy and emission factors for coal beneficiation (Zamuda and Sharpe, 2007; Author’s calculations)

Energy (MJ/tonne coal) 63.60

GHG (g/tonne coal) 13225.56

NOx (g/tonne coal) 42.79

SOx (g/tonne coal) 62.67

PM2.5 (g/tonne coal) 12.41

b. Iron ore pellet making Iron ore is in the form of lumps and fines of various sizes, and as-mined may have some impurities which

reduce the Fe %. Thus, ore is sent to pellet making plants where it is concentrated and impurities are

removed; the output (ore pellets) are of higher Fe % and the uniform size improves reducibility (Cavaliere,

2019). Data for the pellet making process is obtained from KIOCL, 2019, a government owned iron ore

mining company. This company makes calcined pellets using furnace oil, electricity, coke and bentonite

(the effects of production/mining and transport of the latter two are not considered due to lack of data).

Note: Furnace oil emissions calculated using non-road engine emission norms (DieselNet, 2018).

Furnace oil consumption: 16.65 l/tonne ore


Table 16: Energy and emission factors for iron ore beneficiation (KIOCL, 2019; Author’s calculations)

Furnace oil Electricity Total


GHG (g/tonne ore) 40,784.51 70724.852 135462.164

NOx (g/tonne ore) 645.94 161.86 807.80

SOx (g/tonne ore) 11.03 237.07 248.10

PM2.5 (g/tonne ore) 48.45 46.94 95.38

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3.2.5. DRI production For the DRI production step of the life cycle, literature data (Sarangi and Sarangi, 2011) is utilised to develop

a heat and mass balance. The reactions, kiln conditions and DRI quality used by the model provided in this

reference are replicated on Excel. The following are major assumptions as found in the reference:

i. ~5.3% of iron ore is lost in flue dust

ii. ~17% of fixed carbon is lost in flue dust, char and DRI

iii. Fixed carbon of injection coal combusts to increase temperature and produce CO2

iv. Fixed carbon of feed coal produces CO by reacting with CO2 formed by injection coal combustion

v. 67% of injection coal volatile matter (VM) reacts in kiln, remaining VM combusted in the ABC

vi. 1/3rd of feed coal’s volatile hydrogen is reduced to methane - assumed to remain un-combusted in

the ABC. If combusted, GHG emissions would be reduced by nearly 25%.

vii. Kiln temperature is 1340 K, DRI/char is 1325 K, Flue gas/dust is 1100 K

The input parameters (ore quality and coal quality) are varied for different datapoints for the DRI clusters

(see Section 3.3). Using the built-in ‘Goal seek’ tool, the model allowed the calculation of specific energy

consumption and emissions per tonne of DRI production, by estimating the coal and ore requirement based

on quality of raw materials (i.e. ultimate analysis-based composition). Other process parameters (such as

losses) are kept constant. Below are the input parameters for an average case, the cluster of Jamshedpur.

Table 17: Typical input quantities for DRI production (Source: Chatterjee, 2010; Author’s calculations)

Input material Quantity Quality

Iron ore 1483 kg 65% Fe

Coal 1155 kg 35% Fixed carbon, 34% volatiles, 9% moisture, 21% ash

GCV = 5214 kcal/kg

Dolomite 98 kg 28% CaO, 20% MgO

Electricity 70 kWh (Outotec, no date)

For the above inputs, the net heat is balanced using the ‘Goal seek’ function by adjusting coal input, such

that the heat supplied from exothermic reactions is equated to heat consumed for reactions/losses. By

computation for the above inputs, the typical emissions from DRI production are shown in Table 18. Note

that due to the presence of various types of emissions control technologies, it is difficult to estimate NOx,

SOx and PM2.5 from the heat/mass balance. These are adapted from an earlier survey (CPCB, 2007) as

average values per Nm3 of waste gas reported for various DRI plants.

Table 18: Energy and emission factors for DRI production (Author’s calculations, CPCB, 2007))

Energy (MJ/tonne DRI) 25,447.12

GHG (kg/tonne DRI) 2623.77

NOx (g/tonne DRI) 596.15

SOx (g/tonne DRI) 1637.41

PM2.5 (g/tonne DRI) 1197.18

Whilst the prevalence of waste-heat recovery for power generation is unknown, it is common for plants

having more than 200 TPD of DRI capacity to have a waste-heat recovery boiler (WHRB) to generate

steam, for electricity generation using a steam turbine (National Productivity Council, 2017). This would

account for emissions offsets, as it negates a portion of grid-generated electricity. Considering a typical

configuration and steam conditions for a WHRB (BEE, 2011), the following is the potential for power

generation and associated emissions offsets.

Table 19: Potential for electricity from WHRB (Source: Author’s calculations; National Productivity Council, 2017)

Component Value Comments

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Heat in flue gas 6700 MJ/tonne DRI Flue gas from 1 tonne DRI produced

Boiler efficiency 80% Typical value

Heat supplied to steam 5360 MJ/tonne DRI Heat contained in steam

Turbine heat rate 2356 kcal/kWh (IGEN, no date)

Net electricity output 426 kWh/tonne DRI Incl. 70 kWh for DRI plant and 10% parasitic loss

Table 20: Potential energy and emissions offset by WHR power plant (Source: Author’s calculations)

Energy (MJ/tonne DRI) -1534.53

GHG (kg/tonne DRI) -319.11

NOx (g/tonne DRI) -1032.40

SOx (g/tonne DRI) -1512.12

PM2.5 (g/tonne DRI) -299.36

3.3. Application of LCI data A calculation model for the life cycle assessment is developed using Microsoft Excel. As given in Figure 4,

based on the total material consumption of the cluster plants, the supply chain data is applied. For coal

mining, it is assumed that 50% of coal is procured through imports and 50% is domestically sourced (Argus,

2019). For iron ore mining, it is assumed that 85% is domestic and 15% is international, based on the overall

average share of domestic vs imported ore used by the iron and steel industry (Indian Bureau of Mines,

2019). Of the imported materials, the share of different exporting countries is applied based on data from

Chatham House, 2018. It is arbitrarily assumed that 20% of domestic iron ore and 20% of domestic coal

underwent beneficiation into pellets and beneficiated coal respectively.

To obtain a broader picture of the Indian DRI industry, the specific energy and emissions are expanded to

a national level. From the developed database containing 317 sponge iron plants, their locations and

capacities, it is identified that the states of Odisha, Jharkhand, Chhattisgarh, West Bengal and Karnataka

accounted for ~80% of the total installed capacity. Formation of clusters is observed in the GIS mapping

(Figure 1b), such as around the cities of Jamshedpur and Rourkela. Based on these formations,

predominantly observable clusters are selected (Figure 5):

Figure 5: Locations of DRI plant clusters (Source: Author’s creation)

Bellary

Durgapur

Jamshedpur

Rourkela

Angul

Raigarh/

Jharsuguda

Raipur

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i. Angul, Odisha

ii. Rourkela, Odisha

iii. Jamshedpur, Jharkhand

iv. Raigarh/Jharsuguda, Chhattisgarh/Odisha

v. Raipur, Chhattisgarh

vi. Durgapur, West Bengal

vii. Bellary, Karnataka

Once clusters are identified, representative plants are considered in each of the clusters, which are arbitrarily

considered to be an agglomerate of all plants in that cluster. Essentially, each cluster is assumed as one large

plant for the purpose of simplifying the calculation of distances and total energy consumption. Then, based

on the total DRI production capacity, the energy consumption and emissions produced by each cluster are

calculated; the above clusters represent ~77% of the total coal based DRI capacity in India. By summing

up these values, and extrapolating to the total DRI production in India (Statista, 2020), the total energy use

and emissions for DRI production is calculated.

Also, the calculation of mining emissions varies

between clusters – it is assumed that the Southern

cluster of Bellary utilises imported coal from South

Africa, whereas the other clusters in the North are

assumed to use Indonesian coal due to their

proximities to the West and East coasts respectively.

The calculation for energy use and emissions from

the DRI production are varied by considering the

typical qualities of iron ore and coal in the respective

source regions of these materials (Srinivasan et al.,

2018; Indian Bureau of Mines, 2019). For e.g., in the

case of Raipur, ore with 65% Fe is chosen since that

is the most common type mined in the region. The

change in material quality also affects transport and

beneficiation indirectly. Dolomite quality is kept

constant due to the limited quantity considered.

Table 22: Assumptions of iron ore and coal quality

Cluster Iron ore quality (Indian Bureau of Mines, 2019)

Domestic coal source (20% beneficiated) (Srinivasan et al., 2018)

Imported coal source (Srinivasan et al., 2018)

Angul 62% Fe MCL Indonesian

Rourkela 62% Fe MCL Indonesian

Jamshedpur 65% Fe CCL Indonesian

Raigarh/Jharsuguda 65% Fe SECL Indonesian

Raipur 65% Fe WCL Indonesian

Durgapur 65% Fe ECL Indonesian

Bellary 62% Fe SCCL South African

Cluster Capacity (MTPA)

Angul 5.39

Rourkela 3.35

Jamshedpur 2.34

Raigarh/Jharsuguda 6.16

Raipur 5.02

Durgapur 2.14

Bellary 3.01

Total 27.41

Total coal DRI capacity in India (2019)

37.60

% Represented 72.88%

Coal DRI produced in India (2019)

27.81

Table 21: Capacities of different representative plants (Source: Indian Bureau of Mines, 2018)

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Figure 6 shows the transportation scheme considered. From each representative plant, the distances to the

nearest coal port, iron ore port, coal mine region, coal washery, iron ore mine region, iron ore pellet plant

and dolomite mine region are found using QGIS and online mapping services. Such distances are calculated

for both road and rail (from the nearest railway station at each end, last mile by road). For shipping, the sea

distances are calculated from major exporting ports in the exporting countries to major importing ports in

India, then the remaining distance is covered by road. Thus, in this manner, there is the possibility of two

scenarios – transport by road or by rail; the initial results are calculated with an arbitrary 50% road and 50%

rail scenario. Table 23 shows the typical values obtained in the case of Jamshedpur cluster.

Table 23: Example of overall transport emissions (for Jamshedpur cluster) (Source: Author’s calculations)

Coal transport emissions Truck Train Ship Total

Energy (MJ/tonne DRI) 70.22 17.82 577.84 621.86

GHG (g/tonne DRI) 4811.66 1572.14 40788.80 43980.70

NOx (g/tonne DRI) 117.23 55.46 832.75 919.10

SOx (g/tonne DRI) 0.15 2.41 25.81 27.10

PM2.5 (g/tonne DRI) 3.08 2.02 26.15 28.70

Iron ore transport emissions Truck Train Ship Total

Energy (MJ/tonne DRI) 101.54 28.56 225.12 290.17

GHG (g/tonne DRI) 6957.66 2378.49 16133.01 20801.08

NOx (g/tonne DRI) 169.51 78.55 297.68 421.71

SOx (g/tonne DRI) 0.22 2.91 15.03 16.60

PM2.5 (g/tonne DRI) 4.46 2.74 10.35 13.94

Dolomite transport emissions Truck Train Total

Energy (MJ/tonne DRI) 20.13 3.53 11.83

GHG (g/tonne DRI) 1379.27 350.72 864.99

NOx (g/tonne DRI) 33.60 13.87 23.74

SOx (g/tonne DRI) 0.04 0.74 0.39

PM2.5 (g/tonne DRI) 0.88 0.54 0.71

Figure 6: Transport schemes

-23-

4. Results and sensitivity analysis By applying the emissions factors to each of the different stages from cradle to gate (mining > transport >

beneficiation > DRI production), the overall specific emissions per tonne of DRI is obtained.

Table 24: Overall specific energy use and emissions

Category Mining Transport Beneficiation DRI production

Total

Energy (MJ/tonne DRI) 337.80 995.77 79.31 25,828.57 27,241.45

GHG (kg/tonne DRI) 49.80 70.86 23.51 2,635.95 2,780.12

NOx (g/tonne DRI) 360.57 1,500.68 200.58 600.87 2,662.70

SOx (g/tonne DRI) 38.95 46.55 67.28 1,652.79 1,805.57

PM2.5 (g/tonne DRI) 81.91 47.44 24.53 1,209.89 1,363.77

From Table 24, the contributions of different steps along the supply chain to the total energy use and

emissions per tonne of DRI can be compared. In all metrics except NOx emissions, the DRI production

process itself is the largest contributor. Thus, it highlights the need for stricter regulations and better

monitoring to control the emissions from this process. This is especially true since the most efficient coal-

DRI processes can reach a SEC as low as 17.5 GJ/tonne DRI (Chatterjee, 2012) from the above calculated

25.8 GJ/tonne DRI. Note that, as mentioned in Section 3.2.5, the combustion of any methane generated

during DRI production would reduce GHG emissions by 25%.

Efficiency improvements can be possible by enhanced waste-heat recovery systems that allow preheating

the kiln feed materials, leading to 8-9% reduction in coal consumption (Kumar and Khanam, 2012).

Efficiency improvements can also be had by increasing ore beneficiation, by which the Fe % in ore is

increased, the reducibility is improved and ore losses through dust are reduced (Chatterjee, 2010). Coal

beneficiation can be employed to reduce the ash content of local coals (Zamuda and Sharpe, 2007) to

improve their quality, thus reducing reliance on imports. Making WHR mandatory (currently it is a

recommended guideline) (Bhushan et al., 2011) for power generation for plants with >200 TPD capacity

(National Productivity Council, 2017) could allow emissions offsets (Table 26) by negating the

corresponding amount of electricity produced using the grid (Plant SEC reduces to 24.2 GJ/tonne DRI); a

WHR power plant could produce a net output of 426 kWh/tonne DRI (see Annex).

Of note in Table 24 is the significant contribution of transport systems towards the total energy use and

emissions. Especially in terms of NOx emissions, the transport contribution is even higher than the DRI

production process (the latter is usually reduced through emissions control technologies such as wet

scrubbers, electrostatic precipitators, etc.). The overall energy use and emissions of transport is significantly

influenced by shipping, which contributes 80-90% of the total value, despite being less emissions intensive

than other modes. Thus, while imported coals are of higher quality, a better option would be to deploy

larger capacity of clean raw material beneficiation technologies to improve the quality of domestic ores and

coals (Argus, 2019). This will be a key step to reduce reliance on foreign trade and to still maintain emissions

reductions targets. It remains to be seen how the mining emissions will progress; in India, mining is still

highly labour-intensive (hence the lower fuel consumption than foreign mines, see Section 3.1.2). If

mechanisation increases in the coming years, there is a potential for steady increase in mining emissions.

Thus, mining processes must be improved to ensure accelerated adoption of sustainable fuels and practices.

Table 25: Midpoint and endpoint life cycle impacts

Category Mining Transport Beneficiation DRI production

Total

Midpoint impacts

100 yr GWP (kgCO2eq/tonne DRI)

49.80 70.51 23.51 2,635.95 2,779.78

-24-

AP (gSO2eq/tonne DRI) 151.91 554.44 114.61 1,301.15 2,122.10

100 yr Fine dust FP (gPM2.5eq/tonne DRI)

627.28 996.28 301.43 8,402.28 10,327.26

Endpoint impacts

GWP Human health (DALY/tonne DRI)

4.62E-05 6.54E-05 2.18E-05 2.45E-03 2.58E-03

GWP Terrestrial ecosystems (species years/tonne DRI)

1.39E-07 1.97E-07 6.58E-08 7.38E-06 7.78E-06

GWP Aquatic ecosystems (species years/tonne DRI)

3.81E-12 5.39E-12 1.80E-12 2.02E-10 2.13E-10

AP Terrestrial ecosystems (species years/tonne DRI)

2.41E-08 6.90E-08 2.57E-08 4.66E-07 5.85E-07

Fine dust FP Human health (DALY/tonne DRI)

4.26E-04 6.77E-04 2.03E-04 5.68E-03 6.98E-03

Table 25 provides the midpoint and endpoint life cycle impacts from each stage of the process as per the ReCiPe 2016 methodology. The midpoint impacts are quantified in terms of global warming potential, acidification potential and PM formation potential. Across all three indicators, the DRI production process is the major contributor. However, the transportation process (shipping, mainly) contributes significantly to acidification. These trends can be observed in Figure 7. In terms of endpoint impacts, human health and ecosystem loss are measured in disability-adjusted life years (DALY) and species years respectively. The impact on both human life is highest due to the formation of fine dust, whereas global warming causes most of the species loss. The national annual impacts can be found in Table 27.

49.80 1.8%

70.86 2.5% 23.51

0.9%

2 635.95 94.8%

1

10

100

1 000

10 000

kgC

O2eq

/to

nn

e D

RI

GWP 100 yr

81.91 6%

47.09 9.7% 24.53

3%

1 209.89 81.4%

1

10

100

1 000

10 000

gPM

2.5

eq/t

on

ne

DR

I

Fine dust FP 100 yr

151.91 7.2%

554.44 26.1%

114.61 5.4%

1 301.15 61.3%

1

10

100

1 000

10 000

gSO

2eq

/to

nn

e D

RI

AP

Figure 7: Specific life cycle midpoint impacts

-25-

Table 26: National annual life-cycle energy use and emissions (with and without WHR)

Category Total (national) Total (with WHR credits)

Energy (million GJ) 757.58 703.06

GHG (million tonnes) 77.32 65.98

NOx (million kg) 74.05 37.36

SOx (million kg) 50.21 -3.52

PM2.5 (million kg) 37.93 27.29

Table 26 shows the annual life cycle impacts from the DRI industry. To note, of the total GHG emissions,

~20% is caused by CH4 which is produced mainly by volatile hydrogen of feed coal which reacts with

carbon instead of being converted to H2O (Sarangi and Sarangi, 2011). Thus, the proper selection of feed

coal is crucial, not only for methane but also CO2, since VM of feed coal is usually lost with flue gases and

is combusted in the ABC. While this may be beneficial for WHR, the trade-off between choosing a low

VM coal versus increased power generation from WHR needs to be further analysed. From Table 26 it is

seen that WHR electricity offsets a significant amount of emissions from grid electricity. This will reduce

in the future as more renewable electricity is produced by the grid, thus reducing the grid emissions factor.

Methane is also released in significant quantities from coal mines, showing the need for improving mining

technologies to capture fugitive methane emissions.

Table 27: National annual life-cycle impacts (with and without WHR)

Category Total (national) Total (with WHR credits)

Midpoints

100 yr GWP (million tCO2eq)

77.31 69.72

AP (million kgSO2eq) 59.02 27.21

100 yr Fine dust FP (million kgPM2.5eq)

287.20 197.15

Endpoints

GWP Human health (years)

71,739.57 64,695.92

GWP Terrestrial ecosystems (species years)

216.46 195.20

GWP Aquatic ecosystems (species years)

0.01 0.01

AP Terrestrial ecosystems (species years)

16.26 5.83

Fine dust FP Human health (years)

194,239.80 133,901.90

The midpoint and endpoint impacts from one year of DRI production at a national scale are given in Table

27. The endpoint effects of these pollutants would potentially impact 265,979.37 years of human life (>30-

year olds) collectively, and 232.73 species years of terrestrial and aquatic ecosystem loss for 1 year of DRI

production. The impacts (especially from acidification and fine dust formation) are lower if emissions

offsets are considered from the WHR power generation, but this advantage will be curbed as the grid

emissions factor reduce with the increasing renewables share and improving efficiency standards.

-26-

4.1. Sensitivity analysis Due to the nature of the LCA, there exist several deterministic parameters which are highly variable. The

major parameters are varied to test the data for sensitivity. The following is the behaviour of the output

with changes to the input parameters. Note that if changes in specific variables and parameters are not

mentioned, the baseline values/assumptions given in Section 3.3 are applicable.

4.1.1. Effect of raw material grade

a. Effect of ore grades

The most mined iron ores in India have a % Fe content of >62%. However, depending on region and

availability, and option of import, lower or higher quality ore may be used. For lower quality ores, due to

the higher gangue content, there is a loss of productivity across the supply chain – a larger quantity of ore

has to be mined, transported and beneficiated, and a larger amount of coal is needed to heat up the gangue.

Also, the output of the kiln is reduced due to the high feed-to-output ratio. Figure 8 shows the effect of

ore quality in life cycle energy use and emissions. Each % increase in Fe content causes NOx to reduce by

0.55% and others by 0.25% - 0.3%. The typically recommended range of Fe content is 62% to 68% for

coal DRI production (Sarangi and Sarangi, 2011; Chatterjee, 2012).

Iron ore beneficiation is typically applied to agglomerate iron ore fines, and for concentrating lower grade

ores. The sensitivity of ore beneficiation has not been captured in this study; the set system boundary did

not allow for consideration of the energy use and emissions of bentonite mining and coke

production/imports, both of which are consumed in significant quantities (see Section 3.1.4b).

b. Effect of coal grades

Indian coals are high in ash and have a GCV of 3000-5000 kcal/kg. The high ash content is problematic

due to its role in dust generation and accretion formation in the kiln at higher temperatures. Hence, DRI

manufacturers prefer to use imported coal with lower ash content. Process efficiency also depends on the

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

0.5%

1.0%

1.5%

2.0%

62% 63% 64% 65% 66% 67% 68%

% c

han

ge

% Fe in oreEnergy GHG NOx SOx PM2.5

-

20

40

60

80

100

Energy GHG NOx SOx PM2.5

Ener

gy (

x10

) (G

J/ye

ar)

GH

G (

mill

ion

to

nn

es/y

ear)

NO

x, S

Ox,

PM

2.5

(m

illio

n k

g/ye

ar)

MCL WCL SCCL SECL Indo SA

Figure 8: Effect of iron ore grade

Figure 9: Effect of coal grade

-27-

fixed carbon and volatiles present in the coal (Sarangi and Sarangi, 2011). To understand the effect of coal

grades, the model is tested with different types of coal (Srinivasan et al., 2018) are tested on the model.

Beneficiation share is set to 0% but all other parameters are kept constant.

From Figure 9, it is observed that Indonesian coal causes higher energy use and GHG emissions than some

local coals – this is likely due to the combination of high GCV yet high hydrogen content (leading to higher

CH4 emissions). For South African coal, however, the overall coal requirement is significantly lower and

hence the GHG emissions are lower. This exercise shows the complexity of the kiln processes, such that

selection of coal, blending ratios, etc. are to be highly controlled to have optimal efficiency. Choosing a

higher GCV coal alone may not result in reduced energy use and emissions. Also, VM content of the feed

coal must be low, since it is mostly lost due to the opposite flow direction of the flue gases in the kiln,

which carry out the released volatiles from the kiln entry. Thus, a large amount of VM carbon and hydrogen

is combusted in the ABC, leading to high emissions and wasted energy. Choosing a low VM, high FC coal

for the feed end and high VM, high FC coal for the injection end is suitable.

c. Effect of coal beneficiation

Beneficiation of coal can improve productivity and reduce emissions by removing a significant portion of

the constituent ash in coal by means of ‘washing’ the coal, i.e., the productive solids are separated from the

ash by flotation in a liquid medium. Typical ash reduction is reportedly ~30% (Zamuda and Sharpe, 2007).

Since the share of beneficiation is unknown, an arbitrary 20% is considered for domestic coal. Thus, a

sensitivity is carried out; for this sensitivity, a domestic coal of 40.9% ash (Srinivasan et al., 2018) is

considered, without any imports. A constant ore quality of 65% Fe is taken, and all other parameters

remained unchanged. From Figure 10, it can be observed that an increased share of beneficiation results in

a small net decrease in energy use and emissions (GHG and PM2.5), despite the beneficiation process being

quite energy intensive. However, NOx emissions may increase slightly depending on the capacity limitations

of the emissions control equipment. As coal beneficiation increases the sulphur content per unit of coal,

more dolomite must be introduced to control SOx emissions. Overall, increasing beneficiation is

favourable, if cleaner fuels and specific emission norms are put in place to bring a considerable difference.

4.1.2. Effect of imports

a. Import of ore Iron ore is imported to maintain/improve required ore quality standards when such domestic ore may be

difficult to procure due to shortages and competition. For ores imported in pellet form, quality can be as

high as 70% (Geoscience Australia, 2018), which will improve efficiency as seen in Figure 6. For the case

of import of higher quality ore than is domestically available, the sensitivity is shown in Figure 11. Here,

the 100% domestic ore quality is capped at 60% Fe whereas the 100% imported ore is 70% Fe. But even

with the increase in ore quality, the overall energy use and especially the NOx emissions continue to

increase, with only marginal reductions in other pollutants as seen in Figure 9. This is primarily due to the

transport sector (shipping), whose increase in energy use (~90% from base case) and NOx emissions

-1.0%

-0.5%

0.0%

0.5%

1.0%

0% 20% 40% 60% 80% 100%

% c

han

ge

% Beneficiation share

Energy GHG NOx SOx PM2.5

Figure 10: Effect of coal beneficiation share

-28-

(~78% from base case) outweigh efficiency gains in DRI production. A similar level of GHG and PM2.5

reduction can be achieved by increasing beneficiation. Thus, increasing imports is not feasible in the long

term; it is better to increase domestic ore beneficiation, notwithstanding any mining/supply issues.

b. Import of coal

As mentioned in Sections 4.1.1, a higher grade of coal with a lower ash content and better combustibility is

generally better for process efficiency. Since Indian coal is on average highly ashy, roughly half of the DRI

industry already imports coal (Argus, 2019). Figure 12 shows the life cycle impact of increasing coal imports,

for a constant ore quality of 65% Fe and no beneficiation. The comparison is shown for Indonesian coal

and CCL coal (Srinivasan et al., 2018). Like iron ore, imports of coal cause an increase in energy use and

emissions, but as mentioned in Section 4.1.1, this is highly dependent on the composition and quantity of

VM of the feed-end coal. Using low VM domestic coal for feed-end and high VM imported coal for

injection-end may allow the efficient use of imported coal. However, NOx emissions will still increase due

to the contribution of shipping.

4.1.3. Effect of land transport mode In the analysis presented in Section 4, the land transport is assumed to be 50% road and 50% rail. To

understand the impact of land transport share on overall emissions, a sensitivity is made, shown in Figure

12 for the given shares of imports (20% iron ore and 50% coal imports). Clearly, the major impact is on

NOx emissions; the energy intensity and GHG emissions also increase with increasing share of road

transport. This highlights the need for better road infrastructure and adoption of cleaner fuels (such as

LNG) for trucks. As the rail network gets cleaner with increased renewables share in the grid (Nag, 2020),

railways will have a much lower impact than road transport and can be a preferred choice are rail access is

available and rail traffic is manageable. This will also reverse the trend seen in SOx emissions, where road

-35%

-25%

-15%

-5%

5%

15%

25%

35%

-2.5%

-1.5%

-0.5%

0.5%

1.5%

2.5%

0% 20% 40% 60% 80% 100%

% c

han

ge (

NO

x)

% c

han

ge

% Ore imports

Energy GHG SOx PM2.5 NOx

-50%

-30%

-10%

10%

30%

50%

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

0% 20% 40% 60% 80% 100%%

ch

ange

(N

Ox)

% c

han

ge

% Coal imports


Figure 11: Effect of ore imports

Figure 12: Effect of coal imports

-29-

transport is better due to the low sulphur fuels, compared to grid electricity. However, comparing Figures

11/12 and Figure 13 shows the clear need to focus on shipping reforms if imports are to play a role in the

supply chain. If domestic materials are encouraged along with 100% coal beneficiation, the reduction in

emissions would be significant (even with CCL coal). The effect of land transport increases to +/-10%

(NOx) and up to +/-1% (others) for either extreme in this case.

4.1.4. Effect of waste-heat recovery power generation The WHR credits counted in Table 25 consider that 100% of plants having a capacity >200 TPD are

presently operating a waste-heat recovery power plant. Of the 37.60 MTPA installed coal DRI capacity,

35.53 MTPA are >200 TPD plants. This is considered as 100% WHR coverage (as it is not feasible to install

WHR in smaller plants) (Bhushan et al., 2011). However, since this remains a recommendation, there is an

uncertainty regarding the true penetration of such systems. Hence, a sensitivity analysis is performed on the

share of WHR. As seen in Figure 14, the widespread implementation of WHR power generation can lead

to a significant indirect emissions offset, considering the current grid energy mix and emissions factors.

Figure 13: Effect of road vs. rail bias for land transport

650

670

690

710

730

750

770

40

50

60

70

80

90

0% 50% 100%

Ener

gy (

10

6G

J/ye

ar)

CO

2(1

06

ton

nes

/yea

r)N

Ox,

SO

x, P

M (

10

6

kg/y

ear)

Road:Rail ratio (100% domestic case)

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

-0.5%

-0.3%

-0.1%

0.1%

0.3%

0.5%

0% 20% 40% 60% 80% 100%

% c

han

ge (

NO

x)

% c

han

ge

Road:Rail ratio (baseline case)


650

670

690

710

730

750

770

40

50

60

70

80

90

0% 50% 100%

Ener

gy (

10

6G

J/ye

ar)

CO

2(1

06

ton

nes

/yea

r)N

Ox,

SO

x, P

M (

10

6

kg/y

ear)

Road:Rail ratio (baseline case)

Figure 14: Effect of WHR share

0%

20%

40%

60%

80%

100%

120%

140%

0%

20%

40%

60%

80%

100%

0% 20% 40% 60% 80% 100%

% c

han

ge x

10

0 (

SOx)

% c

han

ge

% WHR power generation for >200 TPD plants

Energy GHG NOx PM2.5 SOx

0% road

100% rail

100% road

0% rail

-30-

This is especially true for SOx emissions which see a x100 reduction, due to the large contribution of SOx

from grid electricity. While NOx contribution is mainly from shipping, it too can be offset by ~90%. GHG

emissions can be offset by <20% due to the large impact of the DRI production process.

5. Discussion and outlook The growing iron and steel industry in India is one of the key sectors to reform in order to meet the

country’s nationally determined contributions (NDCs) to the Paris Agreement (Del Bello, 2020).

Considering the export potential of a manufacturing hub such as India, due importance must be provided

to sustainable decarbonisation of core industries such as the DRI industry. With potential export markets

in developed regions looking to increase carbon taxes on imports (European Commission, 2020), the

embedded carbon in a variety of goods along the life cycle of iron and steel industry must be brought down.

In the case of local effects, the DRI industry in its current state is causing a great amount of damage to

human life and sensitive ecosystems.

5.1. Study outcomes In 2017, India emitted 2162 million tonnes of CO2, of which 563 million tonnes (~26%) is the industrial

contribution (International Energy Agency [IEA], 2017). From the baseline calculations given in Table 25,

CO2 emissions from the DRI industry is estimated to be 61.85 million tonnes (taking 20% of GHG as

CH4). Thus, it is understood that the contribution of the DRI life cycle is ~2.9% of national CO2 and ~11%

of industrial sector CO2 emissions respectively. Similarly, India consumed 24,753.62 million GJ of energy,

of which 8582.94 million GJ (~35%) is industrial use. From Table 25, the consumption of the DRI life

cycle is 757.68 million GJ, or 3% of the national and 8.8% of industrial use.

The ReCiPe 2016 life cycle impact assessment shows the level of effect caused by the DRI industry. At the

midpoint level, national annual emissions include 77.31 million tCO2eq potential for global warming over

100 years (from CO2 and CH4 emissions), 59.02 thousand tSO2eq potential for acidification (from SOx and

NOx emissions) and 287.20 thousand tPM2.5eq potential for fine dust formation over 100 years (from PM2.5,

SOx and NOx emissions). The endpoint effects of these pollutants would potentially impact 265,979.37

years of human life (>30-year olds) collectively, and 232.73 species years of terrestrial and aquatic ecosystem

loss for 1 year of DRI production.

Chatterjee, 2011 mentions that the SEC for a coal-based rotary kiln process (SL/RN, TDR, Jindal

technologies) are ideally between 16 and 20 GJ/tonne DRI. However, this analysis finds that the SEC of

DRI production is around 25 GJ/tonne DRI. These values are likely higher for smaller manufacturers

relying on domestic raw materials. As the industry expands towards 80 MTPA coal-based capacity by 2030

(Ministry of Steel, 2017), improving the overall efficiency of DRI production is crucial. This can be achieved

through a comprehensive effort regarding the proper management and use of available resources, import

planning, refining process parameters and taking innovative measures to improve efficiency. Of key

importance in remedying the inefficiencies in the overall DRI supply chain is to analyse and understand the

main drivers of inefficiencies – such as cost-competitiveness, cheap imports, lax monitoring, low demand

for high quality products, etc.

5.1.1. Raw material beneficiation India had an iron ore pellet making capacity of only 65.13 MTPA in India in total as of 2018 (Indian Bureau

of Mines, 2019), for both DRI and pig iron production. Expanding the pellet making capacity (especially

closer to the mining regions) will increase mine productivity (by utilisation of fines), reduce the burden on

transport and production, and improve the output and operating conditions of the DRI plant. However,

the life-cycle impact of bentonite and coke consumption in pellet plants needs to be further analysed.

Coal washery capacity (non-coking coal) in India is 105.24 MTPA as of 2018 (Indian Bureau of Mines,

2019). Overall, whilst only 8.51 million tonnes of coal are consumed by the sponge iron, 576.19 million

tonnes are consumed by the thermal power industry. Thus, capacity allocation specific to the DRI industry

needs to be increased to obtain benefits of ~0.4% to 0.6% in energy use and emissions, rising to ~5% if

imports are reduced.

-31-

Increasing beneficiation capacity can also pave way to reducing import-dependency. Considering Figures 9

and 11, a maximum total benefit of 5% for GHG and 6% for energy can be had by reducing imports and

increasing beneficiation. A larger % benefit may be obtained by adopting more efficient technologies and

best practices in beneficiation. Reducing imports may also be beneficial to economic security by reducing

trade deficits and increasing stability. However, the economic benefit of such measures needs to be verified

by further analyses (Powell and Sati, 2017).

5.1.2. Transport sector reforms The large percentage of raw material imports causes a significant increase in transport emissions, due to the

large distances covered by marine shipping. Reducing imports is key to reducing transport emissions, as

shipping contributes 80-90% of transport energy use and emissions in the current baseline scenario.

Improving ship design and technologies could be explored wherein imports are to remain crucial for the

DRI industry; shipping emissions standards could be stricter to encourage the use of MGO, LNG and

other cleaner fuels (compared to HFO) in shipping (European Commission, 2019).

Shifting land transport to use more rail-based freight can reduce overall life-cycle impact by up to 10% for

NOx and 1% for GHG and PM2.5. With the increasing electrification share and renewable capacity, the

railways aims to reach net-zero carbon emissions in 2030 (Nag, 2020). Thus, the local effect pollution from

NOx and PM2.5 will be further reduced.

For road transport, the introduction of low-sulphur fuels has already been beneficial, as is inferred from

the low SOx emissions. With the introduction of BS6 standards for vehicles in 2020, the impact of transport

will reduce in the coming years (International Council on Clean Transportation [ICCT], 2016).

5.1.3. Other improvements Efficiency improvements by preheating the kiln feed materials lead to 8-9% reduction in coal consumption

(Kumar and Khanam, 2012). Increasing ore beneficiation improves the reducibility and minimises ore losses

through dust (Chatterjee, 2010). Coal beneficiation can be employed to improve the quality of local coals

(Zamuda and Sharpe, 2007) to reduce import dependency. Mandatory WHR for power generation for

plants with >200 TPD capacity could allow grid emissions offsets given in Table 26; a WHR power plant

could produce a net output of 426 kWh/tonne DRI (see Annex).

5.2. Limitations There are some limitations to this study, primarily due to lack of relevant, comprehensive data. Crucially,

literature on the rotary kiln DRI production process is limited in terms of kiln emissions modelling,

reactions modelling and typical assumptions. For e.g., the assumption regarding methane generation

mentioned in Section 3.2.5 affects the GHG emissions value by up to 25%. When it comes to non-GHG

pollutants such as NOx, data is even more limited. There is no recent study on NOx or SOx emissions

from DRI plants, and no CEMS data is available. Further, due to lack of national standards, the market

penetration and efficacy of emissions control equipment such as electrostatic precipitators, wet scrubbers,

etc. is unknown. The onset of Covid-19 regulations also impacted the study since no site visits to DRI

plants could be arranged for real data collection. Overall, most limitations of this study can be mitigated

once more original research and data on DRI plant emissions is available.

5.3. Suggested developments

5.3.1. Stricter regulations and monitoring Many of the key environmental issues surrounding Indian industry are due to inadequate regulations and

leniency in monitoring (Bhushan et al., 2011). Current national environmental regulations specific to the

DRI industry do not enforce any norms on GHG, SOx or NOx pollution, and the limitations for PM2.5

have not been revised since 2008 (Bhushan et al., 2011). Instead, any emission regulations enforced upon

individual DRI plants is by the local pollution control board, which advises and requisites certain equipment

on a case-by-case basis. The emission norms can be revised to include limits on local effect pollutants such

as SOx and NOx, and advocate for minimal GHG emissions. However, in a study conducted by the Central

Pollution Control Board in 2007, it is found that a large number of DRI plants are in violation of even the

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existing norms (CPCB, 2007), a trend which has continued for many years (Juneja, 2015) thus showing the

lack of monitoring and accountability.

To effectively manage and regulate the industry, several key steps have been suggested, such as making

mandatory waste-heat recovery systems, improved emissions control technologies, use of process gas for

heating, recycling of water, etc. (Bhushan et al., 2011). The complexities of the DRI process require real

time data from various production facilities to be represented more accurately. So, a key development could

be the introduction of real time monitoring of stack emissions in sponge iron plants, such as a recent

initiative in the state of Haryana (Sura, 2020). Continuous Emissions Monitoring Systems (CEMS) have

been adopted by several countries such as the USA (Environment Protection Agency [EPA], 2016) and are

critical for ensuring compliance with emission norms. Whilst the CPCB has encouraged industries to adopt

CEMS for self-regulation and has mandated it for several highly polluting industries (Aggarwal, 2018), it

has not been made mandatory for sponge iron plants. CEMS can, for example, help bring down PM2.5

emissions from ~200 mg/Nm3 (CPCB, 2007) to the current limit of 100 mg/Nm3 (Bhushan et al., 2011).

5.3.2. Incentivising emissions reduction Establishing policies aimed at incentivising voluntary steps to reduce energy use and emissions may be a

possible way forward. Such trading schemes have had success in the past, such as the Perform-Achieve-

Trade (PAT) scheme introduced by the Bureau of Energy Efficiency in 2012. Over a 3-year period, PAT

cycle I contributed to the reduction of 8.67 million Mtoe of energy, avoiding 31 million tonnes of CO2

emissions (Bureau of Energy Efficiency and GIZ, 2018). Successive PAT schemes up to PAT cycle V have

been introduced with broader sector coverage and revised targets (BEE, 2020). Proper policy design can

contribute to significant emissions reductions due to the financial incentivisation, which will improve the

economics of installing waste-heat recovery, fuel switching, etc.

5.3.3. Introducing gas-based process Gas based processes such as COREX and MIDREX are well established in international markets (Cavaliere,

2019), but are not prevalent in India due to the relative lack of natural gas networks. Although several gas

pipelines have been constructed, the cost of natural gas is high compared to coal and hence is discouraging

industries from switching. Several large gas-based plants such as Essar Steel, JSW Dolvi Works and JSW

Vijayanagar works are facing difficulties due to the economics of natural gas. Essar Steel has recently been

taken over by ArcelorMittal after insolvency proceedings (FE Bureau, 2019); JSW has been using gas from

onsite coke ovens to cut costs of procuring natural gas (SIMA, 2015) The Government plans to have a gas

based capacity of ~35 MTPA by 2030 (Ministry of Steel, 2017). Use of natural gas has the potential to halve

the SEC and greatly reduce emissions from DRI production, and so is the next step in transition (Chatterjee,

2012). The development of a stable, reliable and cost-competitive natural gas infrastructure is important to

accelerate the adoption by the DRI industry. Such development would also aid in transitioning to hydrogen,

as the systems and technologies are fairly analogous (Gunasekara, 2020).

5.3.4. Introducing hydrogen-based process The use of hydrogen as a reductant instead of CO can potentially allow net-zero emissions steel production.

By replacing fossil fuels with clean hydrogen generated through electrolysis, the MIDREX direct reduction

process can be carried out with only water as the emission. The hydrogen steel concept is also capable of

being fully powered by renewable energy. Additionally, such systems could support multiple end uses, such

as hydrogen fuel-cell vehicles, stationary hydrogen power plants, etc. Several major iron and steel

manufacturers such as SSAB and ArcelorMittal have begun research and testing on the hydrogen steel

process, with the Hybrit Steel project of SSAB, LKAB and Vattenfall expected to be commercially viable

by 2035 (Homann, 2019). With growing international interest in hydrogen economies, fast-developing

nations such as India pose potentially lucrative markets for such technology, and could stand to greatly

benefit from early research, development and adoption. Barriers for hydrogen steel are mainly surrounding

the highly cost-conscious steel industry. Also, the need for large quantities of water for electrolysis is cause

for concern in drought-prone regions; proper water resource management will be key to successful

adoption. If the clean hydrogen based DRI industry is successful, it could potentially negate ~300 million

tonnes of GHG emissions from DRI production per year (as per this analysis).

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6. Conclusion and policy implications The Indian DRI industry consumes 8.8% of national annual industrial energy use and emits 11% of national

annual CO2 emissions. This represents a significant portion of the national contribution in terms of

emissions and energy use. Also, considering the sizeable contribution of CH4 (to the overall GHG), NOx,

SOx and PM2.5, it is crucial to carefully examine the DRI industry for energy use and emissions abatement

measures. The growing iron and steel industry in India is one of the key sectors to reform in order to meet

the country’s NDCs to the Paris Agreement, and the anticipated doubling of DRI capacity from 50 MTPA

in 2018-19 to 114 MTPA by 2030-31 is further indication of the importance of this sector.

This study has examined the life-cycle energy use, emissions and impact of the DRI industry from mining,

transport, beneficiation and DRI production. The ironmaking process is of key focus for reducing energy

use, GHG, SOx and PM2.5 emissions. There is a large contribution of NOx emissions from transport,

chiefly due to the import burden of the industry at present. The penetration of raw material beneficiation

is expectedly low at present, with existing capacity utilising polluting fuels such as furnace oil. Domestic

mining is still apparently labour-intensive, with comparatively low mechanisation.

The ReCiPe 2016 life cycle impact assessment shows the level of effect caused by the DRI industry. At the

midpoint level, national annual emissions include 77.31 million tCO2eq potential for global warming over

100 years (from CO2 and CH4 emissions), 59.02 thousand tSO2eq potential for acidification (from SOx and

NOx emissions) and 287.20 thousand tPM2.5eq potential for fine dust formation over 100 years (from PM2.5,

SOx and NOx emissions). The endpoint effects of these pollutants would potentially impact 265,979.37

years of human life (>30-year olds) collectively, and 232.73 species years of terrestrial and aquatic ecosystem

loss for 1 year of DRI production.

The DRI process metrics suggest that in terms of efficiency, there is a potential for 20-30% improvement

on average when considering the best technologies available (Chatterjee, 2012). This can be brought about

by improving the raw material quality, proper selection of materials and process parameters and waste-heat

recovery, among others. To improve raw material quality, it is suggested to explore the expansion of

domestic beneficiation capacity (particularly for coal) and reduce the import share to bring a gross benefit

of up to 5% in GHG emissions and 6% in energy use. Newer and more efficient beneficiation technologies

could be adopted to ensure sustainable growth. Land transport using trucks can be reduced in favour of

railways to improve transport efficiency and reduce overall emissions by 1-2%. Improving regulations by

revising the 12-year old emissions norms and bettering the monitoring framework by inducting CEMS can

go a long way in preventing plants from flouting norms without detection and reprehension. Extending the

PAT scheme with stricter targets and encouragement of adopting higher productivity, WHR systems and

also for fuel switching could be greatly beneficial in accelerating development.

Over the next decade, however, considering the broad limitations of raw material quality/availability,

technoeconomic uncertainties, etc., the development of a robust and affordable natural gas network may

be of significant potential for decarbonising the DRI industry. A natural gas economy could also go some

way in reducing upstream emissions from mining and transport. In addition, capacity building must be

taken up early on for accelerated hydrogen steel adoption. By enhancing research and development and

deploying pilot production facilities, the overall infrastructure for a hydrogen economy can be stably built

for ensured introduction of hydrogen-based steel in the coming decades. The hydrogen economy can

revolutionise the industry by reducing GHG emissions by up to 94% (Homann, 2019).

In conclusion, short-term measures (beneficiation, revised regulations, WHR, monitoring, etc.) can be taken

to increase coal-DRI performance to BAT standards. Over the medium term, natural gas adoption can be

explored, whilst a suitable long-term goal is to introduce hydrogen and negate 300 million tonnes of GHG

emissions, to enable truly sustainable development. A robust policy must be developed, and relevant

stakeholders must be engaged in a timely manner to accelerate the decarbonisation of this important

industry and thus sustaining the economy over the long term.

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Bibliography Aggarwal, M. (2018) ‘CPCB pushes for broader adoption of emissions monitoring systems’, Mongabay, 11

September. Available at: https://india.mongabay.com/2018/09/cpcb-pushes-for-broader-adoption-of-

emissions-monitoring-systems/.

Argus (2019) Indian sponge iron makers eye SA coal alternatives. Available at:

https://www.argusmedia.com/en/news/2040592-indian-sponge-iron-makers-eye-sa-coal-alternatives

(Accessed: 20 July 2020).

BEE (2011) Waste Heat Recovery Power Plant (4MW)– 200TPD. New Delhi. Available at: http://sameeeksha.org/pdf/dpr/Orissa_sponge_iron_cluster/Waste_heat_recovery_power_plant_4mw_200tpd_Orissa.pdf.

Bhushan, C. et al. (2011) Sponge Iron Industry: The Regulatory Challenge. New Delhi.

Cavaliere, Pasquale (2019) “Clean Ironmaking and Steelmaking Processes”. 1st ed. 2019

https://link.springer.com/book/10.1007/978-3-030-21209-4

Central Electricity Authority (2019) Growth of Electricity Sector in India from 1947-2019. New Delhi.

Central Pollution Control Board (CPCB) (2017) ‘Vehicular Exhaust’. Available at:

https://cpcb.nic.in/vehicular-exhaust/

Chatham House (2018) Resourcetrade.earth. Available at: https://resourcetrade.earth/data?year=2018&importer=699&units=weight (Accessed: 1 May 2020).

Chatterjee, A. (2012) Sponge Iron Production by Direct Reduction of Iron Oxide. Second. Delhi: PHI Learning Pvt. Ltd.

Chaturvedi, P. (1997) Sustainable Energy Supply in Asia. 1st edn. New Delhi: Concept Publishing Company.

Choudury, Abhijit (2014) Energy Audit Methodology of Sponge Iron Manufacturing Units Using DRI Process.

Open Journal of Energy Efficiency 2014, Vol. 3, pp. 41-49

http://dx.doi.org/10.4236/ojee.2014.33005

Coal India Limited (2017) Sustainability Report 2016-17. Kolkata. Available at: https://www.coalindia.in/DesktopModules/DocumentList/documents/Sustainability_Report_of_CIL_for_2016-17_of_Coal_India_Limited_20092017.pdf.

Coal India Limited (2020) About Us. Available at: http://beta.coalindia.in/about-us/ (Accessed: 20 May 2020).

CPCB (2007) Sponge Iron Industry. Delhi.

Del Bello, L. (2020) ‘India’s steel sector is getting in the way of its climate action goals’, Quartz India, 12 February. Available at: https://qz.com/india/1801368/indias-steel-sector-is-getting-in-the-way-of-its-climate-goals/.

Dey, N. R., Prasad, A. K. and Singh, S. K. (2015) ‘Energy survey of the coal based sponge iron industry’, Case Studies in Thermal Engineering, 6, pp. 1–15. Available at: http://dx.doi.org/10.1016/j.csite.2015.04.001.

DieselNet (2018) India: Nonroad Diesel Engines. Available at: https://dieselnet.com/standards/in/nonroad.php (Accessed: 20 May 2020).

Directorate General of Mine Safety (2015) Statistics of Mines in India Volume - II (Non-coal). Available at: http://www.dgms.gov.in/writereaddata/UploadFile/VOLUME_II_NON_COAL_2015.pdf.

Ekvall, Tomas (2019) Attributional and Consequential Life Cycle Assessment. IntechOpen, DOI:

10.5772/intechopen.89202.

https://www.intechopen.com/online-first/attributional-and-consequential-life-cycle-assessment

https://link.springer.com/book/10.1007/978-3-030-21209-4

https://cpcb.nic.in/vehicular-exhaust/

http://dx.doi.org/10.4236/ojee.2014.33005

https://dieselnet.com/standards/in/nonroad.php

http://www.dgms.gov.in/writereaddata/UploadFile/VOLUME_II_NON_COAL_2015.pdf

https://www.intechopen.com/online-first/attributional-and-consequential-life-cycle-assessment

-35-

EPA (2016) EMC: Continuous Emission Monitoring Systems. Available at: https://www.epa.gov/emc/emc-

continuous-emission-monitoring-systems (Accessed: 20 July 1BC).

Essar Steel (2017) ‘Operations – Hazira: Steel Complex’. Available at:

http://www.essarsteel.com/section_level2.aspx?cont_id=FENytf1sB94=&path=Operations_%3E_India

_%3E_Hazira:_Steel_complex

European Commission (2019) Reducing emissions from the shipping sector, Climate Action. Available at: https://ec.europa.eu/clima/policies/transport/shipping_en#tab-0-0 (Accessed: 2 August 2020).

European Commission (2020) Commission launches public consultations on energy taxation and a carbon border adjustment mechanism. Available at: https://ec.europa.eu/taxation_customs/news/commission-launches-public-consultations-energy-taxation-and-carbon-border-adjustment-mechanism_en (Accessed: 27 August 2020).

Exxaro (2019) Environmental, social and governance report 2019. Available at: https://www.exxaro.com/assets/files/Exxaro-ESG-Report-2019.pdf.

FE Bureau (2019) ‘Deal done! ArcelorMittal takes charge at Essar Steel’, Financial Express, 17 December. Available at: https://www.financialexpress.com/industry/deal-done-arcelormittal-takes-charge-at-essar-steel/1795917/.

Geoscience Australia (2018) Iron Ore. Available at: https://www.ga.gov.au/scientific-topics/minerals/mineral-resources-and-advice/australian-resource-reviews/iron-ore (Accessed: 20 July 2020).

Gunasekara, S. (2020) Natural gas: A gateway for hydrogen in India. Available at: https://www.9dashline.com/article/natural-gas-a-gateway-for-hydrogen-in-india (Accessed: 20 August 1BC).

Guttikunda, S. K. and Jawahar, P. (2018) ‘Evaluation of particulate pollution and health impacts from planned expansion of coal-fired thermal power plants in India using WRF-CAMx modeling system’, Aerosol and Air Quality Research. doi: 10.4209/aaqr.2018.04.0134.

Hatch (2014) Lifecycle Assessment Literature Review of Nuclear, Wind and Natural Gas Power Generation. Available at: https://cna.ca/wp-content/uploads/2014/05/Hatch-CNA-Report-RevE.pdf.

Haque N.; Norgate, T., (2015) Life cycle assessment of iron ore mining and processing. Iron Ore 2015, pp 615-

630. Available at:

https://www.sciencedirect.com/science/article/pii/B9781782421566000204?via%3Dihub

Homann, Q. (2019) Hydrogen as a Clean Alternative in the Iron and Steel Industry, Fuel Cell & Hydrogen Energy

Association. Available at: http://www.fchea.org/in-transition/2019/11/25/hydrogen-in-the-iron-and-

steel-industry (Accessed: 29 July 2020).

IEA (2019) ‘Energy demand and intensity in iron and steel, 2000-2017’. Available at:

https://www.iea.org/data-and-statistics/charts/energy-demand-and-intensity-in-iron-and-steel-2000-2017

IEA Statistics (2019) ‘Total final consumption (TFC) by sector, World 1990-2017’. Available at:

https://www.iea.org/data-and-

statistics?country=WORLD&fuel=Energy%20consumption&indicator=Total%20final%20consumption

%20(TFC)%20by%20sector

IEA (2020) India 2020, India 2020. Paris. doi: 10.1787/9faa9816-en.

IGEN (no date) Mapping of 85 pulverized coal fired thermal power generating units in different states. Available at: http://cea.nic.in/reports/others/thermal/tpece/report_85_pul_coal.pdf.

Indian Bureau of Mines (2019) Indian Minerals Yearbook 2018. Nagpur. Available at: https://ibm.gov.in/index.php?c=pages&m=index&id=1363

Indian Railways (2019) Indian Railways Yearbook 2018-19. Available at:

http://www.essarsteel.com/section_level2.aspx?cont_id=FENytf1sB94=&path=Operations_%3E_India_%3E_Hazira:_Steel_complex

http://www.essarsteel.com/section_level2.aspx?cont_id=FENytf1sB94=&path=Operations_%3E_India_%3E_Hazira:_Steel_complex

https://www.sciencedirect.com/science/article/pii/B9781782421566000204?via%3Dihub

https://www.iea.org/data-and-statistics/charts/energy-demand-and-intensity-in-iron-and-steel-2000-2017

https://www.iea.org/data-and-statistics?country=WORLD&fuel=Energy%20consumption&indicator=Total%20final%20consumption%20(TFC)%20by%20sector



-36-

http://www.indianrailways.gov.in/railwayboard/uploads/directorate/stat_econ/Year_Book/Year%20Bo

ok%202018-19-English.pdf

Indika Energy (2020) 2019 Sustainability Report. Jakarta. Available at: https://www.indikaenergy.co.id/wp-content/uploads/2020/06/2019_Indika-Energy-Sustainability-Report_English-3.pdf.

International Council on Clean Transportation (2016) India Bharat Stage VI Emission Standards. Available at: https://theicct.org/sites/default/files/publications/India BS VI Policy Update vF.pdf.

International Council on Clean Transportation (2017) Fuel consumption standards for heavy duty vehicles in India. Available at: https://theicct.org/sites/default/files/publications/ICCT_India-HDV-fuel-consumption_policy-update_20171207.pdf.

International Energy Agency (2017) Data and statistics - CO2 emissions by sector, India 1990-2017. Available at: https://www.iea.org/data-and-statistics?country=INDIA&fuel=CO2 emissions&indicator=CO2 emissions by sector (Accessed: 30 July 2020).

International Maritime Organisation (IMO) (2020) Sulphur 2020 – cutting sulphur dioxide emissions.

Available at: http://www.imo.org/en/MediaCentre/HotTopics/Pages/Sulphur-2020.aspx

IPCC (2014) Fifth Assessment Report. Available at: https://www.ipcc.ch/assessment-report/ar5/.

Jindal Steel and Power (2020) ‘Sponge Iron’. Available at:

https://www.jindalsteelpower.com/products/sponge-iron.html

Jindal Steel and Power (2020a) ‘Businesses – Odisha – Angul’. Available at:

https://www.jindalsteelpower.com/businesses/angul.html

Juneja, S. (2015) Sponge iron’s dirty growth, DownToEarth.

Kajol, Jairaj, B. and Krishnan, D. (2018) ‘PAT’s path to energy efficiency’, The Hindu BusinessLine, 2 May. Available at: https://www.thehindubusinessline.com/opinion/pats-path-to-energy-efficiency/article23751019.ece.

KIOCL (2019) Annual Report 2018-19. Available at: https://www.kioclltd.in/user/download_file_page_content/YXNzZXRzL3VwbG9hZHMvcGFnZV9jb250ZW50LzM2XzM5LzQzbmRfQW5udWFsX1JlcG9ydF8yMDE4LTE5Xy1fQ29sb3VyLnBkZg==.

Klanfar, M., Korman, T. and Kujundžić, T. (2016) ‘Fuel consumption and engine load factors of equipment in quarrying of crushed stone’, Tehnicki Vjesnik-technical Gazette, 23, pp. 163–169. doi: 10.17559/TV-20141027115647.

Kristensen, Hans Otto (2015) Energy demand and exhaust gas emissions of marine engines. Available at:

https://www.danishshipping.dk/services/beregningsvaerktoejer/download/Basic_Model_Linkarea_Link

/806/energy-demand-and-emissions-of-marine-engines-september-2015.pdf

Kumar, V. and Khanam, S. (2012) ‘Recovery and utilization of waste heat in a coal based sponge iron process’, Chemical Engineering and Processing: Process Intensification. doi: 10.1016/j.cep.2012.03.002.

Kumba Iron Ore Limited (2020) Sustainability Report 2019. Centurion. Available at: https://www.angloamericankumba.com/~/media/Files/A/Anglo-American-Group/Kumba/report-library/2019/sustainability-report-2019.pdf.

MIDREX (2019) “2018 World Direct Reduction Statistics”

https://www.midrex.com/wp-content/uploads/Midrex_STATSbookprint_2018Final-1.pdf

Ministry of Road Transport and Highways (MoRTH) (2016-17) Road Transport Yearbook 2016-17

https://morth.nic.in/sites/default/files/Road%20Transport%20Year%20Book%202016-17.pdf

Ministry of Steel (2017) ‘National Steel Policy 2017’. New Delhi: The Gazette of India. Available at: https://steel.gov.in/sites/default/files/draft-national-steel-policy-2017.pdf

Nag, D. (2020) ‘Wonderful! Indian Railways to be net-zero carbon emission mass transportation

http://www.indianrailways.gov.in/railwayboard/uploads/directorate/stat_econ/Year_Book/Year%20Book%202018-19-English.pdf

http://www.indianrailways.gov.in/railwayboard/uploads/directorate/stat_econ/Year_Book/Year%20Book%202018-19-English.pdf

https://theicct.org/sites/default/files/publications/ICCT_India-HDV-fuel-consumption_policy-update_20171207.pdf

https://theicct.org/sites/default/files/publications/ICCT_India-HDV-fuel-consumption_policy-update_20171207.pdf

http://www.imo.org/en/MediaCentre/HotTopics/Pages/Sulphur-2020.aspx

https://www.jindalsteelpower.com/products/sponge-iron.html

https://www.jindalsteelpower.com/businesses/angul.html

https://www.kioclltd.in/user/download_file_page_content/YXNzZXRzL3VwbG9hZHMvcGFnZV9jb250ZW50LzM2XzM5LzQzbmRfQW5udWFsX1JlcG9ydF8yMDE4LTE5Xy1fQ29sb3VyLnBkZg==

https://www.kioclltd.in/user/download_file_page_content/YXNzZXRzL3VwbG9hZHMvcGFnZV9jb250ZW50LzM2XzM5LzQzbmRfQW5udWFsX1JlcG9ydF8yMDE4LTE5Xy1fQ29sb3VyLnBkZg==

https://www.danishshipping.dk/services/beregningsvaerktoejer/download/Basic_Model_Linkarea_Link/806/energy-demand-and-emissions-of-marine-engines-september-2015.pdf

https://www.danishshipping.dk/services/beregningsvaerktoejer/download/Basic_Model_Linkarea_Link/806/energy-demand-and-emissions-of-marine-engines-september-2015.pdf

https://www.midrex.com/wp-content/uploads/Midrex_STATSbookprint_2018Final-1.pdf

https://morth.nic.in/sites/default/files/Road%20Transport%20Year%20Book%202016-17.pdf

https://steel.gov.in/sites/default/files/draft-national-steel-policy-2017.pdf

-37-

network; what that means’, Financial Express, 7 July. Available at: https://www.financialexpress.com/infrastructure/railways/wonderful-indian-railways-to-be-net-zero-carbon-emission-mass-transportation-network-what-that-means/2015745/.

National Mineral Development Corporation (2018a) NMDC at a glance. Available at: https://www.nmdc.co.in/Introduction.aspx (Accessed: 14 May 2020).

National Mineral Development Corporation (2018b) NMDC Sustainability Report 2018. Hyderabad. Available at: https://www.nmdc.co.in/Docs/NMDC-Sustainability/NMDC_Sustainability_Report_2018 Final.pdf.

National Productivity Council (2017) Best Practices Manual for Reducing GHG Emissions in Iron & Steel (Sponge Iron) Sector. Available at: http://npcindia.info/NPC/Files/delhiOFC/GHG-Manual-Iron-Steel-Final.pdf.

Olmez, Gulnur Maden; Dilek, Filiz B.; Karanfil, Tanju; Yetis, Ulku (2016) The environmental impacts of

iron and steel industry: a life cycle assessment study. Journal of Cleaner Production, Vol. 130, pp 195-201

http://www.sciencedirect.com/science/article/pii/S0959652615013797

Outotec (no date) Outotec® SL/RN Process. Available at: https://www.outotec.com/products-and-services/technologies/direct-and-smelting-reduction/slrn-process/ (Accessed: 20 July 2020).

Pal, P., Gupta, H. and Kapur, D. (2016) ‘Carbon mitigation potential of Indian steel industry’, Mitigation and Adaptation Strategies for Global Change. doi: 10.1007/s11027-014-9605-0.

Pang, M. et al. (2015) ‘Environmental life cycle assessment of a small hydropower plant in China’, International Journal of Life Cycle Assessment. doi: 10.1007/s11367-015-0878-7.

Powell, L. and Sati, A. (2017) Coal beneficiation: Policy priorities for India, Observer Research Foundation. Available at: https://www.orfonline.org/research/coal-beneficiation-policy-priorities-for-india/ (Accessed: 20 July 1BC).

Psaraftis, Harilaos N; Kontovas, Christos A. (2009) CO2 emission statistics for the world commercial fleet.

WMU Journal of Maritime Affairs, Vol. 8, pp 1-25

https://doi.org/10.1007/BF03195150

Ramanaiah, Venkata; Khanam, Shabina (2018) Analyses of different modifications proposed for the sponge iron

process for best utilization of waste heat. Process Integration and Optimization for Sustainability (2018), Vol. 2,

pp 365-381 https://doi.org/10.1007/s41660-018-0057-y

Research Designs and Standards Organisation (2017) Exhaust Emission Benchmarks for Diesel Locomotives on Indian Railways. Lucknow.

Sarangi, A. and Sarangi, B. (2011) Sponge Iron Production in a Rotary Kiln. New Delhi: PHI Learning Pvt. Ltd.

Seal, T. K. and Bose, D. (no date) Indian Scenario of Supercritical Power Plants. Available at: https://www.eecpowerindia.com/codelibrary/ckeditor/ckfinder/userfiles/files/Indian Scenario of Super Critical Power Plants Issues and Challenges By NTPC_Part 1.pdf (Accessed: 20 July 2020).

Shruti Seva (2019) Environmental impact assessment of Bahilampur limestone & dolomite mine. Available at: http://environmentclearance.nic.in/writereaddata/EIA/05022019SY0QGT1SFinalEIAEMPText.pdf.

Singh, U., Sharma, N. and Mahapatra, S. S. (2016) ‘Environmental life cycle assessment of Indian coal-fired power plants’, International Journal of Coal Science and Technology. doi: 10.1007/s40789-016-0136-z.

Srinivasan, S. et al. (2018) Benefit Cost Analysis of Emission Standards for Coal-based Thermal Power Plants in India. Bangalore.

Statista (2020) Production volume of coal based sponge iron in India from financial year 2012 to 2020. Available at: https://www.statista.com/statistics/714429/india-production-volume-of-coal-based-sponge-iron/ (Accessed: 20 July 2020).

Steel Authority of India Limited (SAIL) (2012) ‘Coke Ovens-Sinter-BF-BOF Route’

https://www.nmdc.co.in/Introduction.aspx

https://www.nmdc.co.in/Docs/NMDC-Sustainability/NMDC_Sustainability_Report_2018%20Final.pdf

https://www.nmdc.co.in/Docs/NMDC-Sustainability/NMDC_Sustainability_Report_2018%20Final.pdf

http://npcindia.info/NPC/Files/delhiOFC/GHG-Manual-Iron-Steel-Final.pdf

http://npcindia.info/NPC/Files/delhiOFC/GHG-Manual-Iron-Steel-Final.pdf

http://www.sciencedirect.com/science/article/pii/S0959652615013797

https://doi.org/10.1007/BF03195150

https://doi.org/10.1007/s41660-018-0057-y

http://environmentclearance.nic.in/writereaddata/EIA/05022019SY0QGT1SFinalEIAEMPText.pdf

-38-

https://sail.co.in/learning-center/coke-ovens-sinter-bf-bof-route

Sponge Iron Manufacturers Association (SIMA) (2020) ‘Statistics’

http://spongeironindia.com/statistics.php

Sura, A. (2020) ‘Haryana to install devices on industries to monitor pollution electronically’, The Times of India, 16 July. Available at: https://timesofindia.indiatimes.com/city/chandigarh/haryana-to-install-devices-on-industries-to-monitor-pollution-electronically/articleshow/77004244.cms.

Tata Steel (2020) ‘Tata Steel Group Companies – Tata Sponge Iron Limited’. Available at:

https://www.tatasteel.com/corporate/our-organisation/tata-steel-group-companies/

Tata Steel Long Products (LP) (2020) ‘Technology’. Available at:

https://www.tatasteellp.com/technology/

The Economic Times (2019) ‘India replaces Japan as second top steel producer: worldsteel’. Available at:

https://economictimes.indiatimes.com/industry/indl-goods/svs/steel/india-replaces-japan-as-second-

top-steel-producer-worldsteel/articleshow/67721395.cms?from=mdr

Transportpolicy.net (2018) ‘India: Fuels: Diesel and Gasoline’. Available at:

https://www.transportpolicy.net/standard/india-fuels-diesel-and-gasoline/

U.S. Energy Information Administration (U.S. EIA) (2017) ‘India’s steel industry, like America’s, is

dominated by electric-based processes’. Available at: https://www.eia.gov/todayinenergy/detail.php?id=34052

World Steel Association (2019a) ‘Steel's contribution to a low carbon future’. Available at:

https://www.worldsteel.org/publications/position-papers/steel-s-contribution-to-a-low-carbon-future.h

World Steel Association (2019b) ‘World Steel in Figures 2019’. Available at:

https://www.worldsteel.org/en/dam/jcr:96d7a585-e6b2-4d63-b943-

4cd9ab621a91/World%2520Steel%2520in%2520Figures%25202019.pdf

Yu, M. and Halog, A. (2015) ‘Solar photovoltaic development in Australia-a life cycle sustainability assessment study’, Sustainability (Switzerland). doi: 10.3390/su7021213.

Zamuda, C. D. and Sharpe, M. A. (2007) ‘A Case for Enhanced Use of Clean Coal in India: An

Essential Step towards Energy Security and Environmental Protection’, in Workshop on Coal Beneficiation

and Utilization of Rejects. Ranchi. Available at:

https://fossil.energy.gov/international/Publications/Coal_Beneficiation_Workshop/coal_beneficiation_

paper_zamuda.pdf.

https://sail.co.in/learning-center/coke-ovens-sinter-bf-bof-route

http://spongeironindia.com/statistics.php

https://www.tatasteel.com/corporate/our-organisation/tata-steel-group-companies/

https://www.tatasteellp.com/technology/

https://economictimes.indiatimes.com/industry/indl-goods/svs/steel/india-replaces-japan-as-second-top-steel-producer-worldsteel/articleshow/67721395.cms?from=mdr

https://economictimes.indiatimes.com/industry/indl-goods/svs/steel/india-replaces-japan-as-second-top-steel-producer-worldsteel/articleshow/67721395.cms?from=mdr

https://www.transportpolicy.net/standard/india-fuels-diesel-and-gasoline/

https://www.eia.gov/todayinenergy/detail.php?id=34052

https://www.worldsteel.org/publications/position-papers/steel-s-contribution-to-a-low-carbon-future.h

https://www.worldsteel.org/en/dam/jcr:96d7a585-e6b2-4d63-b943-4cd9ab621a91/World%2520Steel%2520in%2520Figures%25202019.pdf

https://www.worldsteel.org/en/dam/jcr:96d7a585-e6b2-4d63-b943-4cd9ab621a91/World%2520Steel%2520in%2520Figures%25202019.pdf

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Annex

SOx, NOx and PM2.5 in DRI plant

Injection coal combustion produces SOx

38.00 lb/tonne coal, per wt. percent S EPA 261.14 mg/Nm3 CPCB 2007

23423.34 g/tonne coal 1389.09 g/tonne DRI 16283.49 g/tonne DRI 11.72 times reduced

Injection coal combustion produces NOx

15 lb/tonne coal EPA 80.2 mg/Nm3 CPCB 2007

6800.00 g/tonne coal 426.61 g/tonne DRI 4727.24 g/tonne DRI 11.08 times reduced

PM from stack is PM2.5 Source

5319.28 m3 waste gas

CPCB 2007

100.00 mg/Nm3 215.82 mg/Nm3

531.93 g/tonne DRI 1148.02 g/tonne DRI

WHR Temperature and enthalpy of flue gas components, assuming flue gas input temperature to heat exchanger

of 1323 K and output temperature of 473 K.

1323 K to 473 K

kJ/mol kcal/kg N2 27.08 231.19 CO2 43.25 234.93 H2O 34.07 452.42 CO 27.36 233.50 O2 28.51 212.93 CH4 54.44 813.17

H2 16.63 1987.09 Heat of flue gas DRI production 1.00 tonne

Sensible heat in flue gas 6,699.27 MJ/tonne DRI

Boiler efficiency 80.00%

Heat supplied to water 5,359.41 MJ/tonne DRI

Mass of steam Steam conditions

h at 126C 531.37 kJ/kg (or) MJ/tonne

h at 65bar, 490C 3,347.20 kJ/kg (or) MJ/tonne

m = Q/delH 1.90 tonne steam/tonne DRI

Electricity produced 543.62 kWh/tonne DRI Heat rate of 2356.3 kcal/kWh

Specific electricity consumed 70.00 kWh/tonne DRI Parasitic losses 10% Net electricity output 426.26 kWh/tonne DRI

Electricity export credits 1,534.53 MJ/tonne DRI

(based on grid emissions) 202.59 kgCO2/tonne DRI

https://www3.epa.gov/ttnchie1/ap42/ch01/final/c01s01.pdf

http://164.100.107.13/upload/NewItems/NewItem_102_SPONGE_IRON.pdf


https://www3.epa.gov/ttnchie1/ap42/ch01/final/c01s01.pdf



https://www.sciencedirect.com/science/article/pii/S0032591017304667



http://sameeeksha.org/pdf/dpr/Orissa_sponge_iron_cluster/Waste_heat_recovery_power_plant_4mw_200tpd_Orissa.pdf

http://cea.nic.in/reports/others/thermal/tpece/report_85_pul_coal.pdf

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638.44 gNOx/tonne DRI

947.75 gSOx/tonne DRI

182.23 gPM2.5/tonne DRI

DRI capacity with WHRB 100%

of plants with >200 TPD

35.53 *106 tonnes

Total credits (millions) 54,527.90 MJ

7,199.00 kgCO2

22.69 kgNOx

33.68 kgSOx

6.48 kgPM2.5

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