Intelligent Transfer Optimization for Ironmaking Process With Nonanalytic Constraints

In order to guarantee the smooth operation of the blast furnace ironmaking process, it is essential to consider the constraints for the optimization of this process. Due to the complexity of the process, it is challenging to obtain the description of the constraint functions and quantify the feasibility of solutions, making traditional optimization methods helpless. To address this problem, a transfer optimization framework is proposed that consists of a two-stage generation mapping algorithm, and an improved grey wolf optimizer (GWO) algorithm. The method achieves the transformation from constrained optimization to unconstrained optimization by establishing a proper mapping with distribution- and boundary-sensitive two-stage generation algorithm. Meanwhile, the density-amended GWO algorithm with adaptive search steps depended on the solution density distribution is applied to locate the optimal solutions. The intelligent transfer optimization method is validated by both numerical tests and practical data, and the results demonstrate the effectiveness of the proposed algorithm.

Automated summary

In order to optimize the blast furnace ironmaking process, a transfer optimization framework is proposed. It consists of a two-stage generation mapping algorithm for transforming constrained to unconstrained optimization, and an improved grey wolf optimizer algorithm for locating optimal solutions. The method's effectiveness is validated through numerical tests and practical data.