Strip Hardness Prediction in Continuous Annealing Using Multiobjective Sparse Nonlinear Ensemble Learning With Evolutionary Feature Selection

In the iron and steel industry, the hardness of steel strips is one of the key performance indicators to evaluate strip quality and guide production for the continuous annealing production line (CAPL). However, the hardness cannot be measured online in the actual production process. Consequently, the precise prediction of the strip hardness based on practical data becomes one of the key tasks during production. In this article, a multiobjective sparse nonlinear ensemble learning with evolutionary feature selection (MOSNE-EFS) method is proposed, which is data-driven modeling of the soft sensor. The method mainly consists of two stages: 1) the construction of individual learners based on multiobjective feature selection learning (MOFSL) and 2) the selection and ensemble of individual learners based on sparse nonlinear ensemble learning via differential evolution (SNEL-DE). The final ensemble model obtained by SNEL-DE is used as the prediction model for strip hardness in CAPL. The proposed method is evaluated with industrial production data. Experimental results indicate that the two strategies, i.e., evolutionary feature selection and sparse nonlinear ensemble, are effective in improving the accuracy and robustness of the prediction model, and further comparison results demonstrate the superiority of the MOSNE-EFS model over the other existing methods. Note to Practitioners—Many quality metrics in the iron and steel industry cannot be online checked, which causes great difficulties in process monitoring, control, and operation optimization. The proposed multiobjective sparse nonlinear ensemble learning with evolutionary feature selection method can help practitioners to construct quality prediction models of many other similar production lines, such as hot rolling and cold rolling, and thus, better process monitoring, control, and optimization of product quality can be achieved.