Multiobjective Operation Optimization of Continuous Annealing Based on Data Analytics

Abstract

Continuous annealing production process generally consists of multiple complex processes that are coupled to each other, and each process contains many control variables. It is difficult to establish a precise mechanism model of the production process. The operators mainly set these control variables based on past production experience, which often result in great fluctuations of product quality (even unqualified products) and high energy consumption. This in turn significantly affected production cost and economic benefits of the cold rolling mill. To efficiently handle this problem, an ensemble learning modeling method based on production data is first proposed for this production process, and then, a multiobjective operation optimization model is established to optimize the operation of continuous annealing production process. Finally, an improved multiobjective differential evolution algorithm based on search process memory is developed to solve this model and achieve the optimal setting of control variables. The computational results on both benchmark problems and practical problems illustrate that the proposed algorithm is superior to some powerful multiobjective evolutionary algorithms in the literature and it can effectively achieve good setting of control variables for the continuous annealing production process.