基于JIT-MOSVR的软测量方法及应用

doi:10.11949/j.issn.0438-1157.20161605

化工学报 ›› 2017, Vol. 68 ›› Issue (3): 947-955.DOI: 10.11949/j.issn.0438-1157.20161605

基于JIT-MOSVR的软测量方法及应用

汪世杰¹, 王振雷¹, 王昕²

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

收稿日期:2016-11-14 修回日期:2016-11-24 出版日期:2017-03-05 发布日期:2017-03-05
通讯作者: 王振雷,wangzhen_l@ecust.edu.cn
基金资助:
国家科技支撑计划项目（2015BAF22B02）；国家自然科学基金面上项目（21276078）；上海市自然然科学基金项目（14ZR1421800）；流程工业综合自动化国家重点实验室开放课题基金项目（PAL-N201404）。

Soft-sensor method based on JIT-MOSVR and its application

WANG Shijie¹, WANG Zhenlei¹, WANG Xin²

1 Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China;
2 Center of Electrical & Electronic Technology, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2016-11-14 Revised:2016-11-24 Online:2017-03-05 Published:2017-03-05
Contact: 10.11949/j.issn.0438-1157.20161605
Supported by:
supported by the National Key Technology R&D Program (2015BAF22B02),the National Natural Science Foundation of China (2127 6078),the Natural Science Foundation of Shanghai (14ZR1421800) and the State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201404).

摘要/Abstract

摘要：

针对传统多模型软测量方法在面对复杂、多变工况时缺少在线更新机制、更新时输出精度降低等问题，提出了一种基于即时学习算法（JIT）的多模型在线软测量方法（MOSVR）。离线阶段首先采用模糊C均值聚类（FCM）对训练数据进行聚类，接着采用SVR建立初始模型集。在线部分以多模型输出作为主要输出，当出现新工况时，通过在线模型更新策略（OSMU）将输出模式切换为JIT，同时多模型集进行在线更新。该方法不仅拥有多模型输出的快速性、精确性，而且在模型更新时通过JIT模式还能保证输出的连续性、稳定性、精确性。最后将该软测量方法进行了数值仿真并运用到乙烷浓度软测量中，验证了该方法的有效性。

关键词: 软测量, 动态建模, 过程系统, 模型, 即时学习

Abstract:

In case of complex and changeable working conditions, traditional multi-model soft-sensor techniques lacked an online update mechanism and decreased accuracy upon updating. A new soft-sensor method based on just-in-time algorithm (JIT) and multi-model online support regression (MOSVR) was proposed. In offline phase, fuzzy C-mean clustering (FCM) was employed to classify training data and SVR was used to build initial model set. In online phase, main output was multi-model SVR works, which would be switched to JIT model by online strategy of model updating (OSMU) and the current model set was updated online simultaneously when new working condition was encountered. The new method not only possessed rapidity and accuracy of multi-model outputs, but also guaranteed continuity, stability and accuracy of JIT outputs at model updating. Method effectiveness was demonstrated by numerical simulation and application in soft-sensor measurement of ethane concentration in ethylene production.

Key words: soft-sensor, dynamic modeling, process systems, model, just-in-time

中图分类号:

TP273

汪世杰, 王振雷, 王昕. 基于JIT-MOSVR的软测量方法及应用[J]. 化工学报, 2017, 68(3): 947-955.

WANG Shijie, WANG Zhenlei, WANG Xin. Soft-sensor method based on JIT-MOSVR and its application[J]. CIESC Journal, 2017, 68(3): 947-955.

参考文献 30

[1]	FERNANDEZ D C J, SAZ-OROZCO P D, BARATTI R, et al. Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network[J]. Expert Systems with Applications, 2016, 63:8-19.
[2]	HIROMASA K, KIMITO F. Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants[J]. Chemometrics and Intelligent Laboratory Systems, 2014, 137:57-66.
[3]	SOUZA F, ARAUJO R, MENDES J. Review of soft sensor methods for regression applications[J]. Chemometrics and Intelligent Laboratory Systems, 2016, 152:69-79.
[4]	曹鹏飞, 罗雄麟. 化工过程软测量建模方法研究进展[J]. 化工学报, 2013, 64(3):788-800. CAO P F, LUO X L. Modeling of soft sensor for chemical process[J]. CIESC Journal, 2013, 64(3):788-800.
[5]	唐苦, 王振雷. 一种基于KPCA-BN的软测量建模方法[J]. 计算机与应用化学, 2012, 29(9):1009-1102. TANG K, WANG Z L. A soft sensing method based on KPCA and BN[J]. Computers and Applied Chemistry, 2012, 29(9):1009-1102.
[6]	王振雷, 唐苦, 王昕. 一种基于D-S和ARIMA的多模型软测量建模方法[J]. 控制与决策, 2014, 29(7):1160-1166. WANG Z L, TANG K, WANG X. A multi-model soft sensing method based on D-S and ARIMA model[J]. Control and Decision, 2014, 29(7):1160-1166.
[7]	TIAN Z D, LI S J, WANG Y H, et al. A multi-model fusion soft sensor modelling method and its application in rotary kiln calcination zone temperature prediction[J]. Transactions of the Institute of Measurement and Control, 2016, 38(1):110-124.
[8]	熊伟丽, 李妍君, 姚乐, 等. 一种动态校正的AGMM-GPR多模型软测量建模方法[J]. 大连理工大学学报, 2016, 56(1):77-84. XIONG W L, LI Y J, YAO L, et al. A dynamically corrected AGMM-GPR multi-model soft sensor modeling method[J]. Journal of Dalian University of Technology, 2016, 56(1):77-84.
[9]	JIN H P, CHEN X G, YANG J W, et al. Multi-model adaptive soft sensor modeling method using local learning and online support vector regression for nonlinear time-variant batch processes[J]. Chemical Engineering Science, 2015, 131:282-303.
[10]	李喆. 自适应多变量统计过程监控研究[D]. 杭州:浙江大学, 2015. LI Z. Research on adaptive multivariable statistical process monitoring[D]. Hangzhou:Zhejiang University, 2015.
[11]	王春鹏,于佐军, 孟凡强. 折息移动窗递推PLS算法及其在聚丙烯生产过程中的应用[J]. 化工学报, 2013, 64(12):4593-4598. WANG C P, YU Z J, MENG F Q. Discount moving window recursive PLS algorithm and its application to process of polypropylene production[J]. CIESC Journal, 2013, 64(12):4593-4598.
[12]	MIN H, LUO X L. Calibration of soft sensor by using Just-in-time modeling and AdaBoost learning method[J]. Chinese Journal of Chemical Engineering, 2016, 24(8):1038-1046.
[13]	李奇安, 郭强. 基于在线SVM的裂解炉燃料气热值软测量[J]. 浙江大学学报(工学版), 2015, 49(3):457-487. LI Q A, GUO Q. Soft measurement for calorific value of cracking fuel gas based on Online SVM algorithm[J]. Journal of Zhejiang University (Engineering Science), 2015, 49(3):457-487.
[14]	ZHANG M, LIU X G. A real-time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction[J]. Journal of Chemometrics, 2016, 30(6):324-331.
[15]	ZHANG M, LIU X G, ZHANG Z Y. A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine[J]. Chinese Journal of Chemical Engineering, 2016, 24(8):1013-1019.
[16]	HE Y L, GENG Z Q, ZHU Q X. Data driven soft sensor development for complex chemical processes using extreme learning machine[J]. Chemical Engineering Research & Design, 2015, 102:1-11.
[17]	瓦普尼克. 统计学习理论的本质[M]. 张学工, 译. 北京:清华大学出版社, 2000. VLADIMIR N V. The Essence of Statistical Learning Theory[M]. ZHANG X G, trans. Beijing:Tsinghua University Press, 2000.
[18]	唐苦, 王昕, 王振雷. 基于证据合成规则的多模型软测量[J]. 控制理论与应用, 2014, 31(5):632-637. TANG K, WANG X, WANG Z L. Multi-model soft sensor based on Dempster-Shafer rule[J]. Control Theory & Application, 2014, 31(5):632-637.
[19]	LU Y, YANG H Z. A multi-model approach for soft sensor development based on feature extraction using weighted kernel Fisher criterion[J]. Chinese Journal of Chemical Engineering, 2014, 22(2):146-152.
[20]	SU Z G, WANG P H, SHEN J O, et al. Multi-model strategy based evidential soft sensor model for predicting evaluation of variables with uncertainty[J]. Applied Soft Computing, 2011, 11(2):2595-2610.
[21]	庞强, 邹涛, 丛秋梅, 等. 基于高斯混合模型与主元分析的模型切换方法[J]. 化工学报, 2013, 64(8):2938-2946. PANG Q, ZOU T, CONG Q M, et al. Multi-model switching based on Guassian mixture model and principal component analysis[J]. CIESC Journal, 2013, 64(8):2938-2946.
[22]	XIONG W L, ZHANG W, XU B G, et al. JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy[J]. Computers & Chemical Engineering, 2016, 90:260-267.
[23]	ZHANG M X, LI Y, KANO M. Quality prediction in complex batch processes with just-in-time learning model based on non-Gaussian dissimilarity measure[J]. Industrial & Engineering Chemistry Research, 2015, 54(31):7694-7705.
[24]	LIU Y, CHEN J H. Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes[J]. Journal of Process Control, 2013, 23:793-804.
[25]	范苗. 基于高斯混合模型的时变过程软测量建模[D]. 杭州:浙江大学, 2015. FAN M. Gaussian mixture model-based time-varying soft sensor development[D]. Hangzhou:Zhejiang University, 2015.
[26]	HIROMASA K, KIMITO F. Application of online support vector regression for soft sensors[J]. Dept. of Chemical System Engineering, 2014, 60(2):600-612.
[27]	ZENG J, WNG J, GUO J. Local multi-model method based on similarity of vector[J]. Application Research of Computers, 2012, 29(5):1631-1634.
[28]	罗雄麟, 赵晓鹰, 吴博, 等. 乙烯精馏塔异常工况在线侦测与控制[J]. 化工学报, 2014, 65(11):4517-4523. LUO X L, ZHAO X Y, WU B, et al. Online detection and control of ethylene column abnormal condition[J]. CIESC Journal, 2014, 65(11):4517-4523.
[29]	陈德呈. 乙烯精馏过程软测量技术应用研究[D]. 上海:华东理工大学, 2011. CHEN D C. Soft sensing technology in the ethylene distillation process applied research[D]. Shanghai:East China University of Science and Technology, 2011.
[30]	LI S J, QIAN F. Application of soft sensors in the estimation of ethylene distillation column composition[C]//Proceedings of the 5th World Congress on Intelligent Control and Automation. Hangzhou, 2004:426-3430.ocesses[J]. Journal of Process Control, 2013, 23: 793-804.
[11]	李奇安, 郭强. 基于在线SVM的裂解炉燃料气热值软测量[J]. 浙江大学学报(工学版), 2015, 49(3): 457-487. LI Q, GUO Q. Soft measurement for calorific value of cracking fuel gas based on Online SVM algorithm[J]. Journal of Zhejiang University (Engineering Science), 2015, 49(3): 457-487.
[12]	范苗. 基于高斯混合模型的时变过程软测量建模[D]. 杭州: 浙江大学, 2015. Fan M, Gaussian Mixture Model-based Time-varying Soft Sensor Development[D]. Hangzhou: Zhejiang University, 2015.
[13]	ZENG J, WNG J, GUO J. Local multi-model method based on similarity of vector[J]. Application Research of Computers, 2012, 29(5): 1631-1634.
[14]	SU Z G, WANG P H, SHEN J O, et al. Multi-model strategy based evidential soft sensor model for predicting evaluation of variables with uncertainty[J]. Applied Soft Computing, 2011, 11(2): 2595-2610.
[15]	Min H, Luo X L. Calibration of soft sensor by using Just-in-time modeling and AdaBoost learning method[J]. Chinese Journal of Chemical Engineering, 2016, 24(8): 1038-1046.
[16]	Xiong W L, ZHANG W, XU B G, et al. JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy[J]. Computers & Chemical Engineering, 2016, 90: 260-267.
[17]	ZHANG M X, LI Y, Kano M. Quality Prediction in Complex Batch Processes with Just-in-Time Learning Model Based on Non-Gaussian Dissimilarity Measure[J]. Industrial & Engineering Chemistry Research, 2015, 54(31): 7694-7705.
[18]	SU Z G, WANG P H, SHEN J, et al. Multi-model strategy based evidential soft sensor model for predicting evaluation of variables with uncertainty. Applied Soft Computing[J], 2011, 11: 2595-2610.
[19]	HIROMASA K, KIMITO F. Application of online support vector regression for soft sensors[J]. Dept. of Chemical System Engineering, 2014, 60(2): 600-612.
[20]	ZHANG M, LIU X G. A real-time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction[J]. Journal of Chemometrics, 2016, 30(6): 324-331.
[21]	唐苦, 王振雷. 一种基于KPCA-BN的软测量建模方法[J], 计算机与应用化学, 2012, 29(9): 1009-1102. TANG K, WANG Z L. A soft sensing method based on KPCA and BN[J]. Computers and Applied Chemistry, 2012, 29(9): 1009-1102.
[22]	罗雄麟, 赵晓鹰, 吴博, 等. 乙烯精馏塔异常工况在线侦测与控制[J]. 化工学报, 2014, 65(11): 4517-4523. LUO X L, ZHAO X Y, WU B, et al. Online detection and control of ethylene column abnormal condition[J]. CIESC Journal, 2014, 65(11): 4517-4523.
[23]	李喆. 自适应多变量统计过程监控研究[D]. 杭州: 浙江大学, 2015. LI Z. Research on adaptive multivariable statistical process monitoring[D]. Hangzhou: Zhejiang University, 2015.
[24]	唐苦, 王昕, 王振雷. 基于证据合成规则的多模型软测量[J]. 控制理论与应用, 2014, 31(5): 632-637. TANG K, WANG X, WANG Z L. Multi-model soft sensor based on Dempster-Shafer rule[J], Control Theory & Application, 2014, 31(5): 632-637.
[25]	ZHANG M, LIU X G, ZHANG Z Y. A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine[J]. Chinese Journal of Chemical Engineering, 2016, 24(8): 1013-1019.
[26]	HE Y L, Geng Z Q, ZHU Q X. Data driven soft sensor development for complex chemical processes using extreme learning machine[J]. Chemical Engineering Research & Design, 2015, 102:1-11.
[27]	LU Y, YANG H Z. A Multi-model Approach for Soft Sensor Development Based on Feature Extraction Using Weighted Kernel Fisher Criterion[J]. Chinese Journal of Chemical Engineering, 2014, 22(2): 146-152.
[28]	陈德呈. 乙烯精馏过程软测量技术应用研究[D]. 上海: 华东理工大学, 2011. CHEN D C. Soft sensing technology in the ethylene distillation process applied research[D]. Shanghai: East China University of Science and Technology, 2011.
[29]	LI S J, QIAN F. Application of soft sensors in the estimation of ethylene distillation column composition[A]. Proceedings of the 5th World Comgress on Intelligent Control and Automation[C]. Hangzhou, P.R. China, 2004: 426-3430.
[30]	Vladimir N. Vapnik. The Essence of Statistical Learning Theory[M]. ZHANG X G, trans. Beijing: Tsinghua University Press, 2000.

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基于JIT-MOSVR的软测量方法及应用

Soft-sensor method based on JIT-MOSVR and its application

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