基于最近邻与神经网络融合模型的软测量建模方法

杨逸俊; 王振雷; 王昕

doi:10.11949/0438-1157.20200401

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基于最近邻与神经网络融合模型的软测量建模方法

过程系统工程 | 更新时间：2026-07-24

- 基于最近邻与神经网络融合模型的软测量建模方法
- Soft sensor modeling method based on hybrid model of nearest neighbor and neural network
- 化工学报 2020年71卷第12期页码：5696-5705
- 作者机构：
  
  1.华东理工大学化工过程先进控制和优化技术教育部重点实验室，上海 200237
  2.上海交通大学电工与电子技术中心，上海 200240
- 作者简介：
  
  杨逸俊（1996—），男，硕士研究生，2508567995@qq.com
  王振雷（1975—），男，博士，教授，wangzhen_l@ecust.edu.cn
- 基金信息：
  
  国家重点研发计划项目(2018YFB1701103)
- DOI：10.11949/0438-1157.20200401
  中图分类号： TP 273
- 收稿：2020-04-16，
  
  修回：2020-07-11，
  
  网络首发：2020-12-17，
  
  纸质出版：2020-12-05
- 稿件说明：
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杨逸俊, 王振雷, 王昕. 基于最近邻与神经网络融合模型的软测量建模方法[J]. 化工学报, 2020, 71(12): 5696-5705

YANG Yijun, WANG Zhenlei, WANG Xin. Soft sensor modeling method based on hybrid model of nearest neighbor and neural network[J]. CIESC Journal, 2020, 71(12): 5696-5705
杨逸俊, 王振雷, 王昕. 基于最近邻与神经网络融合模型的软测量建模方法[J]. 化工学报, 2020, 71(12): 5696-5705 DOI： 10.11949/0438-1157.20200401.

YANG Yijun, WANG Zhenlei, WANG Xin. Soft sensor modeling method based on hybrid model of nearest neighbor and neural network[J]. CIESC Journal, 2020, 71(12): 5696-5705 DOI： 10.11949/0438-1157.20200401.

摘要

软测量建模能够有效地解决生产过程中在线分析仪表测量滞后大、价格昂贵、维护保养复杂等问题。目前，基于数据驱动的神经网络是软测量建模的主要工具之一。而在建模数据的采集过程中，主导变量的采集相对辅助变量要困难得多，由此产生了大量缺失标签的数据。但传统的软测量建模方法却忽视了这些无标签数据，只利用少量的有标签数据建模，从而影响了模型的预测精度。为了解决标签缺失的问题，采用最近邻算法对无标签数据进行伪标记，同时设计了由卷积操作与门限循环单元神经网络（GRU）结合的网络结构来进一步利用无标签数据，提取不同时刻数据中的动态特征，提高神经网络的预测精度。最后将该方法应用于丙烯精馏塔塔顶丙烷浓度的预测，实验结果表明该模型能有效处理非线性动态系统的标签缺失问题，具有更高的预测精度。

Abstract

Soft-sensing modeling can effectively solve the problems of large measurement lag

high price

and complex maintenance of online analytical instruments in the production process. At present

neural network based on data-driven is one of the main tools of soft sensor. In the process of modeling data collection

the collection of dominant variables is much more difficult than that of auxiliary variables

resulting in a large amount of unlabeled data. However

traditional soft sensor modeling methods ignore these unlabeled data and only use a small amount of labeled data for modeling

which has negative effect on the prediction accuracy of the model. To solve the problem of label missing

the nearest neighbor algorithm is used to pseudo label the unlabeled data. At the same time

a network structure is designed by combining convolution operation and gated recurrent unit neural network (GRU) to further utilize the unlabeled data

extract the dynamic feature from data at different time

and improve the prediction accuracy of the neural network. Finally

the method is applied to the prediction of propane concentration on the top of propylene distillation column. The results show that the model can solve the problem of label missing in the nonlinear dynamic system and has higher prediction accuracy.

关键词

Keywords

references

Qi Q, Hirai S, Ho V A. Wrinkled soft sensor with variable afferent morphology[J ] . IEEE Robotics and Automation Letters, 2019, 4 (2): 1908-1915.

Han S, Kim T, Kim D, et al. Use of deep learning for characterization of microfluidic soft sensors[J ] . IEEE Robotics and Automation Letters, 2018, 3 (2): 873-880.

Chamil A. Design and applications of soft sensors in polymer processing: a review[J ] . IEEE Sensors Journal, 2019, 19 (8): 2801-2813.

刘剑, 张凌波, 王潇凌. 基于混沌菌群算法改进BP神经网络的焦炭塔生焦高度软测量建模[J ] . 化工自动化及仪表, 2019, 46 (10): 811-815+833.

Liu J, Zhang L B, Wang X L. Soft sensor modeling of coke tower coke height based on improved BP neural network based on chaos flora algorithm [J ] . Control and Instruments in Chemical Industry, 2019, 46 (10): 811-815 + 833.

李文静, 李萌, 乔俊飞. 基于互信息和自组织RBF神经网络的出水BOD软测量方法[J ] . 化工学报, 2019, 70 (2): 687-695.

Li W J, Li M, Qiao J F. Soft sensing method of effluent BOD based on mutual information and self-organizing RBF neural network [J ] . CIESC Journal, 2019, 70 (2): 687-695.

廉小亲, 王俐伟, 安飒, 等. 基于SOM-RBF神经网络的COD软测量方法[J ] . 化工学报, 2019, 70 (9): 3465-3472.

Lian X Q, Wang L W, An S, et al. Soft sensing method of COD based on SOM-RBF neural network[J ] . CIESC Journal, 2019, 70 (9): 3465-3472.

Yan X F. Hybrid artificial neural network based on BP-PLSR and its application in development of soft sensors[J ] . Chemometrics and Intelligent Laboratory Systems, 2010, 103 (2): 152-159.

Wang Y, Chen C, Yan X F. Structure and weight optimization of neural network based on CPA-MLR and its application in naphtha dry point soft sensor[J ] . Neural Computing and Applications, 2013, 22 (1): 75-82.

Wang J S, Shen N N, Ren X D, et al. RBF neural network soft-sensor modeling of rotary kiln pellet quality indices optimized by biogeography-based optimization algorithm[J ] . Journal of Chemical Engineering of Japan, 2015, 48 (1): 7-15.

李军, 桑桦. 基于SCKF的Elman递归神经网络在软测量建模中的应用[J ] . 信息与控制, 2017, 46 (3): 342-349+357.

Li J, Sang H. Application of Elman recurrent neural network based on SCKF in soft sensor modeling [J ] . Information and Control, 2017, 46 (3): 342-349 + 357.

谢一飞. 基于神经网络和支持向量机的蔗渣锅炉烟气氧含量软测量模型[D]. 南宁: 广西大学, 2016.

Xie Y F. Soft sensing model of oxygen content in flue gas of bagasse boiler based on neural network and support vector machine[D]. Nanning: Guangxi University, 2016.

李丁园. 回声状态网络结构设计及应用研究[D]. 吉林: 吉林大学, 2019.

Li D Y. Design and application of echo state network structure[D]. Jilin: Jilin University, 2019.

乔俊飞, 李瑞祥, 柴伟, 等. 基于PSO-ESN神经网络的污水BOD预测[J ] . 控制工程, 2016, 23 (4): 463-467.

Qiao J F, Li R X, Chai W, et al. BOD prediction of sewage based on PSO-ESN neural network[J ] . Control Engineering of China, 2016, 23 (4): 463-467.

Luciano S, David A, Jose O, et al. Assessing the health of LiFePO4 traction batteries through monotonic echo state networks[J ] . Sensors, 2018, 18 (1): 9-16.

Antonelo E A, Camponogara E, Foss B. Echo-state networks for data-driven downhole pressure estimation in gas-lift oil wells[J ] . Neural Networks, 2017, 85 (1): 106-117.

Muhammad Q R, Nadarajah M, Li J, et al. Multivariate ensemble forecast framework for demand prediction of anom alous days[J ] . IEEE Transactions on Sustainable Energy, 2020, 11 (1): 27-36.

Sun W, Wang J. Elman neural network soft-sensor model of conversion velocity in polymerization process optimized by chaos whale optimization algorithm[J ] . IEEE Access, 2017, 5 : 13062-13076.

耿志强, 徐猛, 朱群雄, 等. 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 Z Q, Xu M, Zhu Q X, et al. Research and application of soft sensing model for complex chemical process based on deep learning[J ] . CIESC Journal, 2019, 70 (2): 564-571.

伊金静. 基于深度学习的工业过程软测量[D]. 杭州: 浙江大学, 2019.

Yi J J. Soft measurement of industrial process based on deep learning[D]. Hangzhou: Zhejiang University, 2019.

Wang Y, Shen Y X, Mao S W, et al. LASSO and LSTM integrated temporal model for short-term solar intensity forecasting[J ] . IEEE Internet of Things Journal, 2019, 6 (2): 2933-2944.

Cheng Y W, Zhu H P, Wu J, et al. Machine health monitoring using adaptive kernel spectral clustering and deep long short-term memory recurrent neural networks[J ] . IEEE Transactions on Industria l Informatics, 2019, 15 (2): 987-997.

Zhang Q, Li F, Long F, et al. Vehicle emission forecasting based on wavelet transform and long short-term memory network[J ] . IEEE Access, 2018, 6 : 56984-56994.

Qin D M, Yu J, Zou G J, et al. A novel combined prediction scheme based on CNN and LSTM for urban PM2. 5 concentration[J ] . IEEE Access, 2019, 7 : 20050-20059.

邵伟明, 葛志强, 李浩, 等. 基于循环神经网络的半监督动态软测量建模方法[J ] . 电子测量与仪器学报, 2019, 33 (11): 7-13.

Shao W M, Ge Z Q, Li H, et al. Modeling method of semi supervised dynamic soft measurement based on cyclic neural network [J ] . Journal of Electronic Measurement and Instrumentation, 2019, 33 (11): 7-13.

熊伟丽, 薛明晨, 李妍君. 基于EM算法的半监督局部加权PLS在线建模方法[J ] . 系统仿真学报, 2018, 30 (1): 8-17.

Xiong W L, Xue M C, Li Y J. Semi supervised partial weighted PLS online modeling method based on EM algorithm [J ] . Journal of System Simulation, 2018, 30 (1): 8-17

李浩. 基于深度学习的半监督软测量建模方法[D]. 杭州: 浙江大学, 2019.

Li H. Modeling method of semi supervised soft sensing based on deep learning[D]. Hangzhou: Zhejiang University, 2019.

Bao L, Yuan X, Ge Z. Co-training partial least squares model for semi-supervised soft sensor development[J ] . Chemometrics & Intelligent Laboratory Systems, 2015, 147 (5): 75-85.

Chen X J, Yuan G W, Nie F P, et al. Semi-supervised feature selection via sparse rescaled linear square regression[J ] . IEEE Transactions on Knowledge and Data Engineering, 2020, 32 (1): 165-176.

Ge Z, Huang B, Song Z. Nonlinear semisupervised principal component regression for soft sensor modeling and its mixture form[J ] . Journal of Chemometrics, 2015, 28 (11): 793-804.

Ersin Y, Dursun A. Estimation of right censored nonparametric regression solved by kNN imputation: a comparative study[J ] . Turkiye Klinikleri Journal of Biostatistics, 2019, 11 (2): 83-92.

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