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Process Control and Optimization of the AOD Process Using Genetic Algorithm

Abstract The AOD process of making stainless steel is very popular because of its fast rate of decarburization and excellent control of composition and temperature. Both static and dynamic models are available for process control. The objective of the present study is to demonstrate the applicability of GA in adapting a process control model to a particular plant environment and subsequently optimize the blowing scheme to reduce overall costs. Deviation of the predicted results of the process control model, from the actual plant data, is first minimized by adjusting the chemical reaction rate parameters and heat balance equation. It is found that the adaptation of the process control model to an actual situation is feasible only below 0.8% carbon. After adapting the model, optimization is done through GA wherein the objective function contains the cost of refractory, process time, chromium loss etc. It is thus possible to devise a blowing scheme to arrive at the desired end point composition and temperature at the lowest cost.