基于深度学习的复杂化工过程软测量模型研究与应用

耿志强; 徐猛; 朱群雄; 韩永明; 顾祥柏

doi:10.11949/j.issn.0438-1157.20181352

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基于深度学习的复杂化工过程软测量模型研究与应用

过程系统工程 | 更新时间：2026-07-24

- 基于深度学习的复杂化工过程软测量模型研究与应用
- Research and application of soft measurement model for complex chemical processes based on deep learning
- 化工学报 2019年70卷第2期页码：564-571
- 作者机构：
  
  1.北京化工大学信息科学与技术学院，北京 100029
  2.智能过程系统工程教育部工程研究中心，北京 100029
  3.中石化炼化工程(集团)股份有限公司，北京 100029
- 作者简介：
  
  耿志强（1973—），男，博士，教授，gengzhiqiang@mail.buct.edu.cn
  韩永明（1987—），男，博士，副教授，hanym@mail.buct.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61533003;61603025;71572008);中央高校基本科研业务费专项资金(XK1802-4)
- DOI：10.11949/j.issn.0438-1157.20181352
  中图分类号： TP 29
- 收稿：2018-11-16，
  
  修回：2018-11-26，
  
  网络首发：2020-06-16，
  
  纸质出版：2019-02-05
- 稿件说明：
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耿志强, 徐猛, 朱群雄, 韩永明, 顾祥柏. 基于深度学习的复杂化工过程软测量模型研究与应用[J]. 化工学报, 2019, 70(2): 564-571

GENG Zhiqiang, XU Meng, ZHU Qunxiong, HAN Yongming, GU Xiangbai. Research and application of soft measurement model for complex chemical processes based on deep learning[J]. CIESC Journal, 2019, 70(2): 564-571
耿志强, 徐猛, 朱群雄, 韩永明, 顾祥柏. 基于深度学习的复杂化工过程软测量模型研究与应用[J]. 化工学报, 2019, 70(2): 564-571 DOI： 10.11949/j.issn.0438-1157.20181352.

GENG Zhiqiang, XU Meng, ZHU Qunxiong, HAN Yongming, GU Xiangbai. Research and application of soft measurement model for complex chemical processes based on deep learning[J]. CIESC Journal, 2019, 70(2): 564-571 DOI： 10.11949/j.issn.0438-1157.20181352.

摘要

针对复杂化工生产过程中的一些原材料消耗量难以直接测量的问题，提出了一种基于深度学习的软测量方法。该方法基于一段时间的历史数据，利用平稳小波变换提取历史数据中的多尺度信息，然后与每一个时间点的可观测数据进行合并得到完整的数据集，再划分出训练集和测试集，用带有注意力机制的深度学习算法进行训练和泛化，进而建立软测量模型。最后将提出的方法应用到对苯二甲酸（PTA）生产装置乙酸消耗的软测量中。通过与极限学习机 (extreme learning machine，ELM)、多层感知器 (multi-layer perceptron，MLP)以及普通长短期记忆网络(long short-term memory，LSTM)方法比较，结果表明，该模型的预测准确度较高，具有一定的有效性和适用性，同时对PTA生产装置的乙酸消耗量进行预测分析，从而提高产能和降低能耗。

Abstract

Because some raw material consumption in complex chemical production process is difficult to measure directly

a soft sensor method based on the deep learning is proposed. Based on a period of the historical data

the proposed method extracts multi-scale information from the historical data using stationary wavelet decomposition. Then the observable data at every point of time are combined to get a complete dataset which is divided into the training dataset and the testing dataset. Moreover

the soft sensor model is trained and obtained by using the depth learning algorithm based on the attention mechanism. Finally

the proposed method is applied to the soft measurement of acetic acid consumption in a terephthalic acid (PTA) production unit. Compared with the extreme learning machine (ELM)

multi-layer perceptron (MLP) and common long short-term memory (LSTM) method

the result analysis shows that the validity and the applicability of the proposed model is verified. Meanwhile

the consumption of acetic acid in the PTA production plant is predicted and analyzed to improve the production capacity and reduce the energy consumption.

关键词

Keywords

references

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