复杂化工过程失配子模型深度诊断与修正算法

杨世品; 黄振; 李丽娟; 宋健全; 叶景; 汪辉

doi:10.11949/j.issn.0438-1157.20180907

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复杂化工过程失配子模型深度诊断与修正算法

过程系统工程 | 更新时间：2026-07-24

- 复杂化工过程失配子模型深度诊断与修正算法
- A deep dive diagnostic and correction algorithm for mismatched sub-models in complicated chemical processes
- 化工学报 2019年70卷第4期页码：1485-1493
- 作者机构：
  
  南京工业大学电气工程与控制科学学院，江苏南京211816
- 作者简介：
  
  杨世品（1984—），男，博士，讲师，spyang@njtech.edu.cn
  李丽娟（1976—），女，博士，教授，ljli@njtech.edu.cn
- 基金信息：
  
  国家自然科学基金面上项目(61873121);国家自然科学基金青年基金项目(61403190);江苏省自然科学基金面上项目(BK201801376);江苏省研究生科研创新计划项目(KYCX18_1076)
- DOI：10.11949/j.issn.0438-1157.20180907
  中图分类号： TP202
- 收稿：2018-08-03，
  
  修回：2019-01-04，
  
  网络首发：2020-06-15，
  
  纸质出版：2019-04-12
- 稿件说明：
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杨世品, 黄振, 李丽娟, 宋健全, 叶景, 汪辉. 复杂化工过程失配子模型深度诊断与修正算法[J]. 化工学报, 2019, 70(4): 1485-1493

YANG Shipin, HUANG Zhen, LI Lijuan, SONG Jianquan, YE Jing, WANG Hui. A deep dive diagnostic and correction algorithm for mismatched sub-models in complicated chemical processes[J]. CIESC Journal, 2019, 70(4): 1485-1493
杨世品, 黄振, 李丽娟, 宋健全, 叶景, 汪辉. 复杂化工过程失配子模型深度诊断与修正算法[J]. 化工学报, 2019, 70(4): 1485-1493 DOI： 10.11949/j.issn.0438-1157.20180907.

YANG Shipin, HUANG Zhen, LI Lijuan, SONG Jianquan, YE Jing, WANG Hui. A deep dive diagnostic and correction algorithm for mismatched sub-models in complicated chemical processes[J]. CIESC Journal, 2019, 70(4): 1485-1493 DOI： 10.11949/j.issn.0438-1157.20180907.

摘要

针对复杂化工生产过程中多变量预测控制系统发生模型失配导致控制性能下降造成的产品质量波动的问题，研究了过程失配子模型深度诊断与模型修正方法。考虑到复杂化工生产过程中被控变量为控制通道和扰动通道的综合响应，通过逐次移动操作变量到扰动通道的方法，评价移动后对模型质量指标的影响，从而判断出所移出子模型的性能，进而对失配子模型进行定位。进一步地，利用现场采集的历史数据用自回归滑动平均模型辨识法辨识出失配部分的模型，用于对原有子模型进行修正。实验采用Wood-Berry精馏过程对其进行动态仿真验证，结果证明了该算法的有效性。

Abstract

In the processes of complicated chemical production

the appearance of model mismatch in multivariable predictive control systems usually will cause fluctuations in product quality. To deal with this problem

a deep dive mismatched sub-models diagnostic method and its correction algorithm are proposed. Considering that the control variable is the combined responses of the control channel and the disturbance channel in industrial field

by the successive moving of the operational variables into disturbance channel

we evaluate the impact of the moving on the quality index of the model

then identify the source of the mismatched sub-model according to its performance judging. Furthermore

the historical data were used to identify the details of the mismatched parts of the model by the autoregressive moving average model identification method

so that the original sub-model can be corrected. The dynamic simulation of the experiment was carried out by Wood-Berry distillation process. The results demonstrate the effectiveness of the algorithm.

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references

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