基于信息增量矩阵的多阶段间歇过程质量预测

doi:10.11949/j.issn.0438-1157.20180570

化工学报 ›› 2018, Vol. 69 ›› Issue (12): 5164-5172.DOI: 10.11949/j.issn.0438-1157.20180570

基于信息增量矩阵的多阶段间歇过程质量预测

李征^1,2,3,4, 王普^1,2,3,4, 高学金^1,2,3,4, 齐咏生⁵, 常鹏^1,2,3,4

1. 北京工业大学信息学部, 北京 100124;
2. 数字社区教育部工程研究中心, 北京 100124;
3. 城市轨道交通北京实验室, 北京 100124;
4. 计算智能与智能系统北京市重点实验室, 北京 100124;
5. 内蒙古工业大学电力学院, 内蒙古呼和浩特 010051

收稿日期:2018-05-28 修回日期:2018-08-27 出版日期:2018-12-05 发布日期:2018-12-05
通讯作者: 高学金
基金资助:
国家自然科学基金项目（61640312，61763037）；北京市自然科学基金项目（4172007）；北京市教育委员会资助项目。

Information increment matrix based quality prediction for multi-phase batch processes

LI Zheng^1,2,3,4, WANG Pu^1,2,3,4, GAO Xuejin^1,2,3,4, QI Yongsheng⁵, CHANG Peng^1,2,3,4

1. Department of Information, Beijing University of Technology, Beijing 100124, China;
2. Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China;
3. Beijing Laboratory for Urban Mass Transit, Beijing 100124, China;
4. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;
5. School of Electric Power, Inner Mongolia University of Technology, Hohhot 010051, Inner Mongolia, China

Received:2018-05-28 Revised:2018-08-27 Online:2018-12-05 Published:2018-12-05
Supported by:
supported by the National Natural Science Foundation of China (61640312, 61763037), the Natural Science Foundation of Beijing (4172007) and the Beijing Municipal Commission of Education.

摘要/Abstract

摘要：

针对间歇过程划分阶段方法很少考虑过程的时序性和动态特性，易将时间上不连续但具有相似特征的样本划分到同一阶段，影响建模精确性的问题，提出一种基于信息增量矩阵-偏最小二乘（information increment matrix-partial least square，ⅡMPLS）的多阶段间歇过程质量预测方法。将历史三维数据沿批次方向展开为二维数据，将其切分成融合质量变量的扩展时间片，依据扩展时间片的信息增量使用滑动窗划分阶段，对各个阶段内数据建立PLS模型进行质量预测。该方法考虑变量之间的相关关系沿采样时刻的变化，利用信息增量捕获系统的动态特性并时序地划分阶段。青霉素仿真平台与大肠杆菌实际生产数据验证了方法的可行性和有效性。

关键词: 间歇式, 阶段划分, 预测, 信息增量矩阵, 偏最小二乘, 过程控制

Abstract:

A sequential quality prediction algorithm based on information increment matrix is proposed for multi-phase batch processes. It can overcome the limits of some phase partition algorithms, which cannot cope with the sequence and dynamics of the processes and may inevitably divide the samples with discontinuous time sequence but similar characteristics into the same phase. First, the 3D data is transformed into 2D data by batch-wise unfolding and splitted into extended time slices that equipped with quality variables. Then a sliding window is used to divide the sub-phases according to information increment of extended time slices. PLS models of each sub-phase constitute the global quality prediction strategy. The proposed algorithm takes the correlations among variables into consideration and uses information increment to capture the dynamics. The feasibility and effectiveness of the proposed algorithm are illustrated by a penicillin simulation platform and an industrial application of E. coli fermentation, respectively.

Key words: batch-wise, phase partition, prediction, information incremental matrix, partial least squares, process control

中图分类号:

TP277

李征, 王普, 高学金, 齐咏生, 常鹏. 基于信息增量矩阵的多阶段间歇过程质量预测[J]. 化工学报, 2018, 69(12): 5164-5172.

LI Zheng, WANG Pu, GAO Xuejin, QI Yongsheng, CHANG Peng. Information increment matrix based quality prediction for multi-phase batch processes[J]. CIESC Journal, 2018, 69(12): 5164-5172.

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基于信息增量矩阵的多阶段间歇过程质量预测

Information increment matrix based quality prediction for multi-phase batch processes

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