Quality prediction for multi-grade processes by just-in-time latent variable modeling with integration of common and special features

Abstract

To cope with the difficulty of on-line quality prediction for multi-grade processes widely operated in process industries, a just-in-time latent variable modeling method is proposed based on extracting the common and special features of multi-grade processes. Considering the complicated nonlinear characteristics of multi-grade processes encountered in engineering applications, a just-in-time learning (JITL) strategy is developed to choose the relevant samples from different grades with respect to the query sample. A novel common feature extraction algorithm is proposed to determine the common directions shared by different grades of processes. After extracting the common features, a partial least-squares modeling algorithm is used to extract the special directions for each grade, respectively. Hence, product quality prediction can be simply conducted by integrating the common and special parts of each grade for model building in terms of a JITL strategy. A numerical case and an industrial polyethylene process are used to demonstrate the effectiveness and advantage of the proposed method.