Feature selection of BOF steelmaking process data by using an improved grey wolf optimizer

Abstract

Basic oxygen furnace (BOF) steelmaking end-point control using soft measurement models has essential value for economy and environment. However, the high-dimensional and redundant data of the BOF collected by the sensors will hinder the performance of models. The traditional feature selection results based on meta-heuristic algorithms cannot meet the stability of actual industrial applications. In order to eliminate the negative impact of feature selection application in the BOF steelmaking, an improved grey wolf optimizer (IGWO) for feature selection was proposed, and it was applied to the BOF data set. Firstly, the proposed algorithm preset the size of the feature subset based on the new encoding scheme, rather than the traditional uncertain number strategy. Then, opposition-based learning was used to initialize the grey wolf population so that the initial population was closer to the potential optimal solution. In addition, a novel population update method retained the features closely related to the best three grey wolves and probabilistically updated irrelevant features through measurement or random methods. These methods were used to search feature subsets to maximize search capability and stability of algorithm on BOF steelmaking data. Finally, the proposed algorithm was compared with other feature selection algorithms on the BOF data sets. The results show that the proposed IGWO can stably select the feature subsets that are conductive to the end-point regression accuracy control of BOF temperature and carbon content, which can improve the performance of the BOF steelmaking.