Batch Process Monitoring Using Two-Dimensional Hidden Semi-Markov Models
نویسنده
چکیده
In this paper, a novel monitoring method for the repetitive batch operation with two-dimensional dynamic behavior is proposed. It combines dynamic multiway principal component analysis (DMPCA) and hidden segmental semi-Markov models (HSMM) to resolve the problem caused by the two-dimensional behavior of batch processes. DMPCA utilizes the batch-to-batch dynamic characteristics and eliminates the batch correlation among process variables. HSMM is used to construct the temporal behavior among process variables during each batch run. The proposed method has the temporal property of HSMM and the batch-to-batch dynamic characteristics of DMPCA. To demonstrate the performance of the proposed method, data from the monitoring practice in a fed-batch penicillin cultivation process are conducted.
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