Bayesian inference and joint probability analysis for batch process monitoring. Issue 10 (3rd May 2013)

• Record Type: Journal Article
• Authors: Ge, Zhiqiang; Song, Zhihuan
• Is Part Of: AIChE journal. Volume 59:Issue 10(2013:Oct.)
• Page Start: 3702
• Page End: 3713
• Publication Date: 2013-05-03
• Subjects: Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.14119
• Languages: English
• ISSNs: 0001-1541

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>A new probabilistic monitoring method for batch processes that have multiple operating conditions is described. Particularly, for multiphase batch processes, a phase‐based Bayesian inference strategy is introduced, which can efficiently combine the information of multiple operation modes together into a single model in each specific phase. Therefore, without any process knowledge, local monitoring results in different operation modes can be automatically integrated. Besides, the information of the operation mode can be obtained through joint probability analysis under the Bayesian monitoring framework. Potential extensions of the proposed method for fault diagnosis and identification are also discussed. A benchmark case study on the penicillin fermentation process is given to evaluate the feasibility and efficiency of the proposed method. It is demonstrated that the monitoring performance and the process comprehension have both been improved. © 2013 American Institute of Chemical Engineers <italic>AIChE J</italic>, 59: 3702–3713, 2013</p> </abstract>