Digital production platform aims to make steel production more sustainable and energy-efficient
Ambition and specific objectives
The transition to low carbon and eco-friendly steel production in Europe requires a significant transformation of the steelmaking processes, particularly the introduction of new steelmaking routes. There is a clear need for enablers to plan and manage this revolution and ensure sustainable steel production. In this context, the steel production requires breakthrough technologies to reduce its environmental footprint as close to zero as possible. Additionally, a seamless digitalisation of the production processes and skilled personnel are necessary to support and comprehend the transformation process. DiGreeS will tackle these challenges by implementing an integrated digitalisation approach throughout the steel value chain, to enable an enhanced use of the industrial data collected along the process chain and ensuring the uptake of human experiences for easier industrial integration. The aim of DiGreeS is to develop a user-friendly digital platform for networked production based on novel and soft sensors as well as related approaches and models, which will be demonstrated in three individual use cases targeting different segments of the steel value chain. Within DiGreeS comprehensive digital twins will be developed to support the efficient feedstock verification and real-time control of the crude steel (CS) production with the electric arc furnace (EAF) and to increase the process yield while improving the quality of intermediate and final steel products. In this context, the potential of artificial intelligence (AI) and machine learning (ML) technologies will be fully exploited to support the optimal use of process data, and various scenarios specific to each use case (UC) will be modelled. Consequently, DiGreeS will apply digitalisation solutions to improve the product quality of CS and final products, to enhance the raw material and energy efficiency of the steel production process, and thus to increase the circularity and reduce the CO2 emissions of steel production.
- These aforementioned challenges will be tackled through the following specific objectives:
- Development of a data ecosystem for planning and process management that increases the data availability across the steel supply chain
- Integration of novel sensor technologies combined with digital process models for real-time process monitoring and prediction of process conditions (e.g. slag parameters), as well as control and optimization of process parameters, including forecasting energy requirement and product quality
- Tracking of relevant process and material information across the entire process chain for synergetic process optimizations with improved database
- Feedstock characterisation of HMS through cutting-edge sensors, inline sensor and machine control-data feeding digital process models
- Establishment of decision support tools to support operators and process experts
Use cases along the steel value chain
The UC1 is focused on the heavy melting scrap (HMS) verification. Currently, the HMS characterisation is based on visual inspection by experienced employees and random sample spectroscopic analysis with handheld X-Ray fluorescence. An improved and reliable scrap characterisation is needed to allow operator-friendly sorting and better separation to reduce impurities in the targeted steel heat.
UC2 focuses on optimising the production of crude steel by EAF. Currently, the process control of the scrap-based EAF is highly empirical, relying mainly on the individual skills of the operators and on fixed operating patterns, but not on real-time measured process data, making the current process inefficient in terms of material and energy. An innovative solution is needed to monitor and control sustainability of the running process conditions, to set up countermeasures to stay into the optimal process window.
UC3 is focused on the quality assurance of semi-finished products particularly steel sheets, and the optimisation of the process parameters. The current process control is based on long-term system behaviour, lacks regular updates and doesn't consider changes in the production system or inline sensor data, and the final quality of the product is determined after its levelling, blanking and annealing. Around 4% of the steel sheets need to be reworked (levelling and annealing), that leads to up to 20% of additional plant utilization because a rework of plate material is significantly more time consuming than the processing of the coil material. A new approach is needed to optimise the levelling process using data from previous process steps, as well as predicting final product quality before annealing, to reduce the re-work by half (down to 2%).
Impact
Various scenarios will be modeled for three different use cases: The innovative digitalization solutions used are intended to increase the product quality of the steel products, the raw material and energy efficiency of the manufacturing process and thus increase their recyclability. At the same time, the digital platform aims to reduce the steel industry's CO2 emissions by up to 6 million tons per year and save annual costs of up to €800 million.