Smart Design For Man Machine Interface For Electric Arc Furnace To Improve Efficiency
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Author
Azadegan, Pooriya <1991>
Date
2024-07-18Data available
2024-07-25Abstract
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
Type
info:eu-repo/semantics/masterThesisCollections
- Laurea Magistrale [4822]