Smart Design For Man Machine Interface For Electric Arc Furnace To Improve Efficiency

Abstract

The electric arc furnace (EAF) is a key element of the modern steelmaking process that combines flexibility, efficiency, and cost-savings in melting scrap metal into high-quality steel components. This thesis focuses on EAF efficiency improvements by designing a more intelligent man-machine interface (MMI) solution and ways of helping trainees gain knowledge about EAF operation, enabling them to perform faster with less energy consumption and higher efficiency in real-world conditions. The thesis begins with a broad description of EAF operations, introducing its constituents, safety measures, and on-site practices. Operators play a critical role in this industry. Therefore, trainees will learn the basics of safety, process control, and equipment maintenance to maintain furnace performance. This is covered by creating a Business Process Model and Notation (BPMN) diagram, which represents exactly what the operator has to do during each phase of the EAF operation. It is a model used to investigate operator tasks and interactions in the steelmaking process. One of the main contributions in this thesis is creating a virtual control panel for an electric arc furnace (EAF) with visualisation implemented using Python and TKInter. This virtual panel is a training aid that speeds up operators for real-world operations. Operators have full visibility over critical furnace parameters such as temperature level, power usage, current levels, and carbon content via this interface. It provides operators with the experience to monitor and control furnace conditions in real time for the production of high-quality steel while optimising energy usage. Advocating smart MMI designs that enable operators to reach operational excellence in EAF facilities, this thesis highlights the role of human-machine collaboration with industrial processes. Potential research areas could be an extension of the Virtual Control Panel and predictive maintenance with advanced analytics

The electric arc furnace (EAF) is a key element of the modern steelmaking process that combines flexibility, efficiency, and cost-savings in melting scrap metal into high-quality steel components. This thesis focuses on EAF efficiency improvements by designing a more intelligent man-machine interface (MMI) solution and ways of helping trainees gain knowledge about EAF operation, enabling them to perform faster with less energy consumption and higher efficiency in real-world conditions. The thesis begins with a broad description of EAF operations, introducing its constituents, safety measures, and on-site practices. Operators play a critical role in this industry. Therefore, trainees will learn the basics of safety, process control, and equipment maintenance to maintain furnace performance. This is covered by creating a Business Process Model and Notation (BPMN) diagram, which represents exactly what the operator has to do during each phase of the EAF operation. It is a model used to investigate operator tasks and interactions in the steelmaking process. One of the main contributions in this thesis is creating a virtual control panel for an electric arc furnace (EAF) with visualisation implemented using Python and TKInter. This virtual panel is a training aid that speeds up operators for real-world operations. Operators have full visibility over critical furnace parameters such as temperature level, power usage, current levels, and carbon content via this interface. It provides operators with the experience to monitor and control furnace conditions in real time for the production of high-quality steel while optimising energy usage. Advocating smart MMI designs that enable operators to reach operational excellence in EAF facilities, this thesis highlights the role of human-machine collaboration with industrial processes. Potential research areas could be an extension of the Virtual Control Panel and predictive maintenance with advanced analytics