Improved quality estimation and knowledge extraction in a batch process by bootstrapping-based generalized variable selection

This paper proposes a novel variable selection method for the improvement of the quality estimation performance and knowledge extraction in a batch process. The quality estimation method is an effective alternative to the costly and time-consuming quality measurement. However, degradation of the prediction performance of the quality estimation model caused by inclusion of insignificant variables is a serious problem. The preprocessing of variable selection is thus important to improve the prediction accuracy by removing the variables uncorrelated with quality variables. The variable selection technique also can be used as a knowledge-extraction tool. The technique allows us to identify the process characteristics related to product quality. The problem of inaccurate variable selection results caused by a large number of variables and a limited number of samples of batch process data is solved by the bootstrapping technique. Despite increased computational load, combination of bootstrapping with variable selection enhances the reliability of the variable selection result. An industrial poly(vinyl chloride) polymerization process is used as a case study to show the improved performance of the proposed method compared with multiway partial least squares (MPLS). The proposed method shows better accuracy than MPLS in both detecting the quality-related variables and estimating the real values of quality variables.