Hidden semi - markov probability models for monitoring two-dimensional batch operation / Junghui Chen in Industrial & engineering chemistry research, Vol. 50 N° 6 (Mars 2011)  

Public ISBD [article]  inIndustrial & engineering chemistry research > Vol. 50 N° 6 (Mars 2011) . - pp. 3345–3355 Titre : Hidden semi - markov probability models for monitoring two-dimensional batch operation Type de document : texte imprimé Auteurs : Junghui Chen, Auteur ; Yan-Cheng Jiang, Auteur Année de publication : 2011 Article en page(s) : pp. 3345–3355 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Dynamic Probability Résumé : The repetitive batch operation has a two-dimensional dynamic behavior, including a finite time interval of each batch run in the time domain and infinite repetitions along the batch domain. In this Article, a novel monitoring method that combines dynamic multiway principal component analysis (DMPCA) and hidden segmental semi-Markov models (HSMM) is proposed 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. By constructing a two-dimensional model, the proposed method can generate simple probability monitoring charts and monitor the progress in each batch run. The proposed method has the temporal property of HSMM and the batch-to-batch dynamic characteristics of DMPCA. Its advantages are demonstrated through a simulated fed-batch penicillin cultivation process characterized by fault sources. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie101189g [article] Hidden semi - markov probability models for monitoring two-dimensional batch operation [texte imprimé] / Junghui Chen, Auteur ; Yan-Cheng Jiang, Auteur . - 2011 . - pp. 3345–3355. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 6 (Mars 2011) . - pp. 3345–3355 Mots-clés : Dynamic Probability Résumé : The repetitive batch operation has a two-dimensional dynamic behavior, including a finite time interval of each batch run in the time domain and infinite repetitions along the batch domain. In this Article, a novel monitoring method that combines dynamic multiway principal component analysis (DMPCA) and hidden segmental semi-Markov models (HSMM) is proposed 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. By constructing a two-dimensional model, the proposed method can generate simple probability monitoring charts and monitor the progress in each batch run. The proposed method has the temporal property of HSMM and the batch-to-batch dynamic characteristics of DMPCA. Its advantages are demonstrated through a simulated fed-batch penicillin cultivation process characterized by fault sources. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie101189g

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