Multiway Interval Partial Least Squares for Batch Process Performance Monitoring
Multiway Interval Partial Least Squares for Batch Process Performance Monitoring
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DOI：
10.1021/ie303562t
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发表时间：
2013-05
影响因子：
4.2
通讯作者：
S. Stubbs;Jie Zhang;Julian Morris
中科院分区：
文献类型：
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作者：
S. Stubbs;Jie Zhang;Julian Morris
将区间偏最小二乘法与多向偏最小二乘法相结合，建立了改进的统计过程性能监测模型。提出了一种新的算法，用于将使用光谱数据的应用中的批次持续时间或光谱数据分割成几个子区间，可以为这些子区间构建独立的偏最小二乘模型。该方法与使用等长子区间的方法和仅选择这些子区间的子集用于预测和/或监测的做法不同。提出的方法大大减少了所需的子区间数量，并随后减少了提高预测和监控性能所需的最小二乘模型的数量。将该方法应用于著名的基准补料间歇青霉素生产模拟器Pensim，用于质量变量预测和故障检测。
The method of interval Partial Least Squares (iPLS) is combined with multiway partial least-squares (MPLS) to allow the building of enhanced statistical process performance monitoring models. A novel algorithm is proposed for segmenting batch duration, or spectral data in applications employing spectroscopy data into several subintervals for which independent PLS models can be constructed. The approach deviates from the method of using subintervals of equal length and the practice of choosing only a subset of these subintervals for prediction and/or monitoring. The proposed approach provides dramatic reduction in the number of subintervals required and subsequently the number of PLS models required to give improved prediction and monitoring performance. The proposed method, MiPLS, is applied to the well-known benchmark fed-batch penicillin production simulator, Pensim, for quality variable prediction and fault detection.