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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 2
- 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) 33
- 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 6 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 10 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 11
- 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 12
- 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’ 13
- 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 14
Hinweis der Redaktion
- 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