Bayesian Inference and Joint Probability Analysis for Batch Process Monitoring

被引:11
|
作者
Ge, Zhiqiang [1 ]
Song, Zhihuan [1 ]
机构
[1] Zhejiang Univ, Inst Ind Proc Control, State Key Lab Ind Control Technol, Dept Control Sci & Engn, Hangzhou 310027, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
multiphase batch process; multimode; Bayesian inference; process monitoring; mode identification; INDEPENDENT COMPONENT ANALYSIS; FAULT-DETECTION; PRINCIPAL COMPONENTS; NONLINEAR PROCESSES; PCA; MODEL; FERMENTATION; DIAGNOSIS;
D O I
10.1002/aic.14119
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
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. (c) 2013 American Institute of Chemical Engineers AIChE J, 59: 3702-3713, 2013
引用
收藏
页码:3702 / 3713
页数:12
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