Afzali, Sayyed Faridoddin
Khan, Kamil A.
MacGregor, John F.
Madabhushi, Pranav Bhaswanth
Marlin, Thomas E.
Swartz, Chris L. E.
Supervised by Dr. Chris L.E. Swartz
B.Tech, Chemical Engineering, IIT Bombay
E.I.DuPont India Pvt Ltd., Intern, May Jun 2012
BASF India Ltd., Intern, Nov Dec 2011
Dynamic Optimization, State Estimation and Control of Electric Arc Furnace (EAF) Operation
Electric Arc Furnaces (EAF) are widely used in steel industries to produce molten steel from scrap metal. The EAF operation involves a very low level of automation and almost all decisions are made by the operator using past experience. However, this experience can be limited due to the multivariate interactions and relationships that may not be immediately apparent. The steel industry can potentially save a significant amount of money by optimizing the amount and timing of additions of scrap, arc power, fluxes, methane, carbon and oxygen. The main aim of this research is to develop a mathematical model and computational strategy for use as an on-line optimization-based decision support tool for EAF operation. A dynamic first principles model of the EAF process is developed and then used within an optimization framework to determine the optimal input trajectories. The implementation of the model-based optimization is envisaged to generate significant savings. State estimation is also investigated through implementing a Moving Horizon state Estimator (MHE) that could be used as a decision support tool for the operator to identify the optimized trajectories of the control variables.
- Shyamal, S., Swartz, C. L. E. A Multi-rate Moving Horizon Estimation Framework for Electric Arc Furnace Operation, IFAC-PapersOnLine,, 49 (7) 1175–1180 (2016) [ Publisher Version ]
S. Shyamal, Y. E. M. Ghobara and C. L. E. Swartz, “Dynamic Optimization of Electric Arc Furnace Operation with State Estimation as a Decision Support Tool,” 64th Canadian Chemical Engineering Conference, Niagara Falls, Canada (2014)