Intelligent Steel Production
Task 5 Team
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Dr Richard Thackray
University of Sheffield
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Dr Michael Auinger
University of Warwick
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Dr Aurash Karimi
University of Warwick
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Dr Uchenna Kesieme
University of Sheffield
Introduction
Many existing process models for steel production do not allow for process alignment and are too complex for meaningful real-time predictive use. The primary aim of Task 5 is to take a different view on steel production in its entirety by not seeking to improving product qualities in one step alone but by focussing on decreasing energy usage and building links over the entire process chain. This will be achieved by development and optimisation of process level models supported by experimental verification, analysis of process data, and by benchmarking current process routes to quantitatively assess how efficiently industry currently uses both energy and materials.
Outcomes
The project officially began in October 2020. The initial task aims did not change significantly, although during discussions with industry, one or two extra ideas and opportunities arose. For example, a focus on the life cycle of refractory materials wasn’t originally in the plans, but after discussions with industrial partners, it was added to the list of potential investigations. Another change was an increased focus on the use of methods from artificial intelligence such as machine learning for regression, as opposed to improving physics-based modelling approaches. The original process modelling template was planned to be applied for liquid metal processing but ended up with a case study for temperature distribution in blast furnace stoves.
Collection and analysis of comprehensive steelmaking data to allow material and energy efficiencies to be calculated. Methodologies are being developed/adapted to be able to turn these data into meaningful conclusions and suggestions for efficiency gains.
Development of a hybrid model to combine the accuracy of physics-based simulations with the speed of machine learning methods. This forms a template to be used for many process simulations across the entire steel manufacturing route.
Figure 1. Schematic reaction zone model for a blast furnace
Impact
The methodologies being developed with regard to analysis of steelmaking parameters can be used to compare the resource efficiency of various individual components in a particular process step and thus identify overall potential efficiency gains – for example a decrease in raw material input, an increase in yield and the recovery of by-products.
With the model being a template for the steel industry, it allows for synergies and knowledge transfer between processes. The model can also, once verified with specific process data, be used as a platform for the process behaviour under worst-case scenarios. This could become a digital testing ground to develop guidelines for a “what-if scenario” in relation to equipment failures.
Next Steps
Completion of the material and resource efficiency data analysis and implementation of suitable methodologies.
Seeking additional funding to widen the applicability of the model for processes across the wider steel production route.