复杂化工过程调控操纵策略的深度学习方法

doi:10.11949/0438-1157.20210357

摘要/Abstract

摘要：

现代复杂化工过程生产运行记录了大量的过程调控时序数据，如何提取其中有价值的调控操纵经验和规则，对于提升过程运行智能化水平具有重要意义。时间序列聚类是一种挖掘历史调控操纵序列的有效方法，然而由于实际工况经常与历史数据出现偏差，使得重构准确的过程调控操纵策略出现困难。为此，本文提出了一种基于数据碎片化深度学习的过程调控操纵提取方法，采用基于Levenshtein距离的凝聚层次时序聚类获取不同过程扰动状态类别，提取对应的有效操纵序列进行碎片化处理，采用卷积神经网络对调控操纵策略进行深度学习和重构。将此方法在工业换热器过程上进行了应用，获得了满意的结果，表明所提出的方法能够克服常规操纵序列挖掘的工程应用适应性差、对数据源依赖性强等缺点。

关键词: 操纵策略, 层次聚类, Levenshtein距离, SAX符号化, 卷积神经网络

Abstract:

Production operations of modern chemical processes record a large amount of process control manipulating temporal data. How to extract valuable manipulating experiences and rules is of great significance to enhance process operation intelligent level. Previous research results have shown that time series clustering is an effective method for mining historical control manipulating sequences. However, practical working conditions often deviate from the historical data, making it difficult to reconstruct accurate process control manipulating strategies. In response to this problem, this paper proposes a process control manipulation extraction method based on deep learning of data fragmentation, using agglomerative hierarchical temporal clustering based on Levenshtein distance to obtain different process disturbance state classes, extracting corresponding effective manipulating sequences for fragmentation, and employing convolutional neural networks for deep learning and reconstructions of manipulating strategies. The approach is demonstrated by industrial heat exchanger processes, in which, the experiment shows that the proposed approach is able to overcome the poor adaptability and strong dependence on data sources of conventional control manipulating sequence mining methods in practical applications, achieving satisfactory results.

Key words: manipulation strategies, hierarchical clustering, Levenshtein distance, SAX symbolizations, convolutional neural networks

中图分类号:

TQ 028.8

唐晓婕, 杨博, 李宏光. 复杂化工过程调控操纵策略的深度学习方法[J]. 化工学报, 2021, 72(9): 4830-4837.

Xiaojie TANG, Bo YANG, Hongguang LI. Deep learning approaches to complex chemical process control manipulating strategies[J]. CIESC Journal, 2021, 72(9): 4830-4837.

图/表 14

图1 复杂化工过程调控

Fig.1 Diagrams of complex chemical process manipulations

图2 凝聚层次聚类树

Fig.2 Aggregations of hierarchical clustering trees

图3 SAX方法示意图

Fig.3 Diagram of the symbol aggregation approximation method

图4 调控操纵序列符号化

Fig.4 Symbolizations of process control manipulating time series

图5 复杂化工过程调控操纵策略深度学习

Fig.5 Flow charts of manipulating strategy deep learning for complex chemical processes

图6 面向工业过程时序数据深度学习的CNN结构

Fig.6 CNN structures for deep learning of industrial process time series data

图7 换热器流程示意图

Fig.7 Flow diagram of the heat exchanger

图8 T1和F1的过程扰动变量的凝聚聚类结果

Fig.8 Hierarchical agglomerative clustering results of T1 and F1

图9 T1和F1的扰动类别

Fig.9 Disturbance categories of T1 and F1

图10 过程扰动变量的扰动状态

Fig.10 Disturbance states of the process disturbances variables

图11 操纵变量SAX符号化

Fig.11 SAX symbolizations of manipulating variables

图12 多分类混淆矩阵示例

Fig.12 Examples of a multi-category confusion matrix

表1 混淆矩阵的关键评价指标

Table 1 Key evaluation indicators of the confusion matrix

重构类别	精确率	准确率	召回率	F₁-Score
1	95.33%	89.77%	94.05%	91.99%
2	92.98%	92.86%	92.86%	92.86%
3	99.33%	94.44%	94.44%	94.44%
4	94.33%	95.83%	87.62%	91.54%
5	94.1%	87.14%	87.14%	87.14%

表2 各类扰动状态下关键变量的IAE指标

Table 2 IAE contrasts of the key variable corresponding to the disturbance states

过程扰动状态	关键变量IAE(监督控制)	关键变量IAE(学习策略)
1	9989.62	9129.6
2	10003.09	9061.3
3	9949.4	9201.5
4	9960.45	10381
5	9909.4	9733
6	10019.84	9061.3
7	—	10144

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