Adaptive Monitoring Method for Batch Processes Based on Phase Dissimilarity Updating with Limited Modeling Data
Adaptive Monitoring Method for Batch Processes Based on Phase Dissimilarity Updating with Limited Modeling Data
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DOI：
10.1021/ie061320f
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发表时间：
2007-06
影响因子：
4.2
通讯作者：
Chunhui Zhao;Fuli Wang;F. Gao;N. Lu;Mingxing Jia
中科院分区：
文献类型：
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作者：
Chunhui Zhao;Fuli Wang;F. Gao;N. Lu;Mingxing Jia
提出了一种间歇过程的监控方法，从有限的参考批次开始，然后随着新正常批次的积累来更新模型数据结构。在分析展开矩阵（（时间×批次）×变量）的基础上，提出了一种广义移动窗口方法，用于探索局部协方差结构、相位划分和监测模型的建立。然后，自适应更新算法，基于特定相位的相异度分析，开发模型更新，以适应额外的信息，从新的批次数据的积累，并探索随时间变化的行为，从批次。所提出的方法说明了两个过程，一个工业规模的实验注射成型和模拟补料分批青霉素发酵。结果表明该方法是有效的。
A monitoring method is proposed for batch processes, starting with limited reference batches and then updating model data structure with accumulation of new normal batches. On the basis of analysis of the unfolded matrix ((time × batch) × variable), a generalized moving window method is introduced for exploring local covariance structure, phase division, and the development of monitoring models. Then, an adaptive updating algorithm, based on phase-specific dissimilarity analysis, is developed for model updating to accommodate additional information from the accumulation of new batch data and to explore time-varying behaviors from batch to batch. The proposed method is illustrated with two processes, an industrial scale experimental injection molding and a simulated fed-batch penicillin fermentation. Both results show that the proposed method is effective.