Guy Cunliffe Energy Benchmarks for Sector Reporting
1. Energy Footprint and Energy Savings Potential
of Domestic Iron & Steel – A Baseline Study
Energy Research Centre, University of Cape Town
Industrial Energy Efficiency Conference
National Cleaner Production Centre South Africa (NCPC-SA)
Century City Convention Centre, Cape Town; Friday, 15 September July 2017
2. Contents
Background to the project
Methodology (and Limitations)
Background: SA Steel Sector Challenges
Steel Production in South Africa
Current Average Energy Estimates
International Benchmark Estimates
Energy Management Systems
Guidelines for Implementation
Going forward: Detailed study of four sectors
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3. Background to the project
Larger detailed study: Energy Footprint and Energy Savings Potential of Heavy Industry
Scope includes Iron & Steel, Non-Ferrous Metals, Non-Metallic Minerals & Chemicals
Objective to develop baseline and potential savings scenarios for industrial energy use
Model to be used by DOE to inform the iterative Integrated Energy Plan process
In response to initial obstacles, this desktop study was proposed as a preliminary step
High level snapshot of energy intensity of the sector
Comparison of South African energy intensity with global benchmarks
Guidelines for implementation (DoE reporting requirements)
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4. Methodology (and Limitations)
Study relied exclusively on data, information and literature available in the public domain
In addition to preliminary data collection for the larger study, we relied upon:
DOE & ERC internal energy balances
South African Iron and Steel Institute (‘SAISI’) data
Company reporting and website data
Other South African public studies (e.g. DEA Mitigations Potential Analysis 2014)
Case studies on projects supported by the NCPC-SA
International literature (e.g. studies by US EPA, India and China)
Data and reporting from the World Steel Association
Very limited direct stakeholder engagement - data was (and is) a key challenge
4
5. 9.4 9.4 9.4 9.6
9.0
8.2
7.5 7.6 7.5
6.9 7.2
6.4 6.4
,0.0
,2.0
,4.0
,6.0
,8.0
,10.0
,12.0
Milliontonnes[Mt]
South Africa Annual Domestic Production Salient notes:
33% production decline 2006 – 2015
Global steel oversupply – downward
pressure on export prices
Rising imports to South Africa
Financial losses for companies
Rising energy (electricity!) prices
Reinforces the importance of Energy
Management System implementation
(SAISI, 2016; TIPS; 2016)
Background: SA Steel Sector Challenges
6. Steel Production Processes in SA
Primary steelmaking ‘routes’:
Blast Furnace – Basic Oxygen
Furnace
Direct Reduced Iron – Electric Arc
Furnace
COREX/MIDREX – CONARC
Furnace (Saldanha Works)
Secondary steelmaking: Scrap-EAF
Final energy carriers:
Electricity
Bituminous coal
LPG
Steam
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Blast
Furnace
64%
Electric
Furnace
5%
Other
(COREX)
11%
Direct
Reduced Iron
20%
Iron Production (SAISI: 2015)
Basic Oxygen
Furnace
61%
Electric Arc
Furnace (incl.
CONARC)
39%
Steel Production (SAISI: 2015)
7. Steel Production Processes – BOF
7
Raw
Iron
Sintering
Blast Furnace
Iron Making
BOF
Steel Making
FluxesSteam Elec
Coke batteries
Coking
Coal
CO
Gas
Coke
Bit
coal
Elec
BF
Gas
Liquid
Iron
Scrap
(<10%) O2
Steam
Liquid
Steel
BOF
Gas
Sinter
mix
BOF Steel Production – High Level Energy and Material Flows
Mate-
rial
Energy
8. Steel Production Processes
8
Raw
Iron
Sintering
Blast Furnace
Iron Making
BOF
Steel Making
FluxesSteam Elec
Coke batteries
Coking
Coal
CO
Gas
Coke
Bit
coal
Elec
BF
Gas
Liquid
Iron
Scrap
(<10%) O2
Steam
Liquid
Steel
BOF
Gas
Sinter
mix
BOF Steel Production – High Level Energy and Material Flows
Mate-
rial
Energy
Majority
energy use
9. Current Average Energy Estimates
Estimate, based on aggregate data
High degree of uncertainty
Actual performance likely to vary
High-level findings
Iron making the most energy intensive step
Process heating the largest end use of energy
Sector average estimated ~ 26.5 GJ/tsteel
India estimate (2014) 27 GJ/t
China (average) ~ 22 GJ/t
US (best practice) 15 – 18 GJ/t
Scrap reduces energy – but limited by availability
9
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0
BF-BOF
DR-EAF
Scrap-EAF
Other-EAF
GJ / t (steel product)
Current Average Energy Intensity (per tech route)
Coal Coke Electricity Off Gas Steam LPG Other
10. International Benchmark Estimates
Benchmark energy intensity averages compiled from
World Steel Association reporting (2014)
Data collected from 42 steel works (representing
~9% of global production)
Global average of 17.5 GJ/tsteel
NB: Limiting factors not accounted for:
Variance in feedstock quality, availability
Difference in quantities of scrap
Variance in energy carriers (US mostly gas-based)
Physical plant limitations, variance in configuration
Driving factors e.g. costs of energy, materials
Effects of under utilisation
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0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0
BF-BOF
DR-EAF
Scrap-EAF
GJ / t (steel product)
CAEC vs Best (Int’l) Available Energy Intensity
BAEC [GJ / t] CAEC [GJ / t]
11. Guidelines for Implementation
Energy Management System implementation has had proven,
demonstrable success at steel plants in South Africa
Capacity building (NCPC-IEE project)
Energy review and strategy development
Management buy-in
Modelling and regression analysis
Action Plan formulation, implementation
Ongoing monitoring
System review
Case studies: Vanderbijlpark Works, Saldanha Works
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12. Guidelines for Implementation Plans
Guidelines for the deployment of implementation plans, requirements directed by
Department of Energy regulations
Reporting in accordance with SANS (ISO) 50001
Development of an EE implementation plan template
Development of guidelines for completion of the template
Main items to be included and reviewed on an ongoing basis:
Scope and boundaries of target systems/area of effort
Management signatory
Energy review documentation (current consumption, significant end-users)
Description of objectives, targets and action plans (incl. task ownership)
Periodic review dates
Guidelines will be drawn up with reference to DOE requirements
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13. Going forward: Larger DoE study
Strengthening data!
Detailed industry engagement
Workshops
Detailed data collection,
subject to NDAs
Reported in aggregate form
LEAP model development
Long term baseline (2050)
Sensitivity analysis for
forecasting assumptions
Scenario development for
energy intensity ‘paths’
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14. Thank You
Contact details:
Energy Research Centre
6th floor, Menzies Building
Upper Campus, University of Cape Town
Private Bag X3, Rondebosch 7701
Tel: +27 (0) 21 650 3230
Fax: +27 (0) 21 650 2830
Email: guy.cunliffe@uct.ac.za
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Notes de l'éditeur
Thank Alf, NCPC, Luvuyo
Mention study performed for Luvuyo and team
Through SANEDI, funded by UNIDO
Desktop study based entirely on public information
Forms first part of larger study on Domestic Heavy Industry, energy planning group
Several challenges with the broader study, the most fundamental of which has been data
Therefore, interim desktop study into iron and steel proposed as a preliminary step, to uncover some basic information that can inform the larger process
Data, info and literature that could be accessed in the public domain
Emissions intensity benchmarking study
Case studies at plants, based on EMS and ESO carried out under NCPC-IEE Project
Informal stakeholder engagement – hence, while we tried to be as broad as possible in terms of our reach, our underlying data for the project was quite weak
Data was and remains a key challenge
Nevertheless, I wish to share a few findings…
Brief background context;
Trade and Industrial Policy Strategies group 2016
Decline by 33% 2006 – 2015
Not followed the global post-08 recovery and rise
Oversupply – downward pressure on exports
Rise in imports
ProudlySA – Mr Mashimbye (local procurement)
Effects well documented, including financial losses and distress, job losses, etc.
(not unique to steel)
Rising energy and electricity costs – Mr Louis Bosch
Motivation for EMS,
Brief overview – most probably already familiar
Two traditional routes – BF-BOF; DRI-EAF
Also COREX (liquid) / MIDREX (DRI) combo at Saldanha – waste gas from COREX recovered as energy feedstock for MIDREX
CONARC
Unique to Saldanha, SA
Use of scrap – temp regulation in BOF
Electricity, coal, LPG, steam
BF-BOF dominates…
Therefore overview in diagram of BF-BOF (not an artist joke)
‘clouds’ = material feedstock; ovals = energy
Integrated plant – recovery of thermal energy from waste gas streams
Waste heat recovered through steam boiler – electricity; e.g. AMSA 40 MW cogen
Blast furnace most energy – hot blast heated to ~1200C (process heat the main energy end use)
High energy also for DRI, smelting the iron using H2, CO
Gas-based DRI less energy intensive, more commonly used globally
High level findings
Not validated, nor verified; fuel splits are estimates, subject to further correction
Analysis, calibrated data to 2012 energy balances, average of 26.5
Not shown, but 2 GJ/t – 10% ‘margin for error’
China 45% of global total
Cannot just ‘switch’ to EAF
32% difference
Limiting factors – everything will vary from plant to plant, blast furnace to blast furnace; not possible to have identical performance
Remember, the accuracy of the results can be improved with better data
Listed some technology interventions in the report
Top pressure recovery turbine; pulverised coal injection; coke dry quenching; VSDs
R&D – slag heat recovery; use of biomass in sinter making
Importance of what Alf Hartzenburger said to me yesterday, when I asked this very question, that technology is only part of the story; and of what Mr Pieter de Bruyn (ProductivitySA) said about Continuous Improvement
EMS, explained very well by Mr Sashay Ramdharee and Mr Kalev Taim (MPACT) yesterday
Case studies – Mr Bosch pump systems optimization (low hanging fruits – ensuring pumps were clean, volume pumped, cooling of the motors, etc.)
Saldanha – optimised their LPG use, cooling, water cooling, and general awareness improvement; saved 80 GWh, 90 mil from 500k spent
Overlaps with what Luvuyo (presumably) presented
Note that ERC has drafted a template that can be used, which accommodates the main items and allows for measuring
Detailed study, which I mentioned at the start, for the Planning Team
Aim here is to strengthen the data, by means of active engagement with industry
Obviously there are concerns about confidentiality, and about what we (ERC) do with the data, and who we give it to
In the process of agreeing NDAs, and will only report aggregate data to DOE
Will help us to develop a LEAP model, baseline of energy use (very careful about assumptions of growth, future etc.)
Scenario development, based on potential interventions in future years – this is a long term model, expected to go up to 2050