Improved Batch Process Monitoring and Diagnosis Based on Multiphase KECA

Multiple phases with transitions from phase to phase are important characteristics of many batch processes. The linear characteristics of batch processes are usually taken into consideration in the traditional algorithms while the nonlinearity is neglected. However, to monitor batch processes more accurately and efficiently, such process features are needed to be considered carefully. In this paper, a new similarity index based on KECA (kernel entropy component analysis) is defined for batch processes with nonlinear characteristics. A new phase division and monitoring method based on the proposed similarity index is brought forward simultaneously. First, nonlinear characteristics can be extracted in feature space via performing KECA on each preprocessed time-slice data matrix. Then phase division is achieved with the similarity change of the extracted feature information. By establishing a series of KECA models for transitions and steady phases, it reflects the diversity of transitional characteristics objectively and can preferably solve the stage-transition monitoring problem in multistage batch processes. Finally, in order to overcome the problem that the traditional contribution plot cannot be applied to the kernel mapping space, a nonlinear contribution plot diagnosis algorithm is proposed. Both results of simulation study and industrial application clearly demonstrate the effectiveness and feasibility of the proposed method. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.