A Novel Operation Optimization Method Based on Mechanism Analytics for the Quality of Molten Steel in the BOF Steelmaking Process

This paper investigates a dynamic analytics and operation optimization method for the basic oxygen furnace (BOF) steelmaking process, which is a multi-stage process. First, based on reaction kinetics, fluid dynamics and conservation of mass, that a novel discrete-time nonlinear system with process disturbance, time delay and the constrained operation input is established characterizes the dynamics of the quality (the carbon content and the temperature) of molten steel in the BOF steelmaking process. It is difficult and complicated to realize analytics and operation optimization for the BOF steelmaking process by using the established nonlinear system directly, so a surrogate-model-based discrete-time switched system with time delay and actuator saturation is given to describe the multi-stage BOF steelmaking process. Then, sufficient conditions are derived under which the stability is guaranteed and the tracking performance is achieved. The parameters of analytics-based operation optimization algorithm can be obtained by solving linear matrix inequality problems (LMIPs). Finally, the effectiveness of the proposed method is verified by a BOF steelmaking numerical experiment example. Note to Practitioners—This paper deals with the problem of dynamic analytics and operation optimization arising from the BOF steelmaking process. Based on the real-time estimation of the carbon content and the temperature of molten steel, the plan for the operation of oxygen lance and the addition of auxiliary materials is given to produce the qualified molten steel. This paper establishes a system model based on the mechanism, and the model parameters are derived from papers verified by a large amount of data to ensure the correctness of the model. In addition, actuator saturation is applied to describe the phenomenon of restricted operation variables. Through a surrogate model approximation and theoretical analysis, the parameters of dynamic analytics and operation optimization algorithm can be obtained by solving LMIPs, which is a convex optimization problem that is easy to solve. The numerical experiment results show that the proposed method can give operators some desired references.