화학공학소재연구정보센터
Industrial & Engineering Chemistry Research, Vol.57, No.51, 17437-17451, 2018
Unsupervised-Multiscale-Sequential-Partitioning and Multiple-SVDD-Model-Based Process-Monitoring Method for Multiphase Batch Processes
For the effective monitoring of batch processes with uneven multiphases, phase partitioning and discriminant analysis are two critical problems. To fully solve these two problems, a systematic strategy including fuzzy phase partitioning and hybrid discriminant analysis is proposed First, using a new unsupervised, multiscale, sequential partition (UMSP), each batch is divided into phases with transitions using different clustering scales. On this basis, two multiple-support-vector-data-description (SVDD) models are built for online phase partitioning and monitoring, and a hybrid-discriminant-analysis method is then developed for online fault detection. The effectiveness and advantages of the proposed method are illustrated with a 2D, handwritten example and a fed-batch penicillin-fermentation process.