Black box operation optimization of basic oxygen furnace steelmaking process with derivative free optimization algorithm

Abstract Mastering the smelting status and making the material adding strategy are important and challenging issue in the basic oxygen furnace (BOF) steelmaking process. Unfortunately, complex physicochemical reaction with multi-input and multi-output makes the first-principles models unusable. To this end, a black box operation optimization (BBOO) problem integrated black box data analytics modeling and optimization is abstracted from the BOF steelmaking process. Moreover, a derivative free optimization (DFO) method based on the trust region framework is developed to solve the operation optimization problem. In the proposed algorithm, we integrate quadratic interpolation and sparse modeling to construct the surrogate model. Wherein, l 0 -norm optimization, which is solved through converting it into a convex optimization problem with introducing the slack variables, is used to determine the sparse surrogate model. Finally, we apply the proposed DFO algorithm and other DFO algorithms to solve the BBOO problem in BOF steelmaking process. The results demonstrate that the proposed method can be an alternative for providing solutions and insights of material addition in BOF process and the proposed algorithm is comparable.

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