Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

doi:10.1016/j.cjche.2014.05.003

›› 2014, Vol. 22 ›› Issue (7): 828-836.DOI: 10.1016/j.cjche.2014.05.003

• SOFT SENSOR • 上一篇下一篇

Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Weiming Shao¹, Xuemin Tian¹, Ping Wang^1,2

1. College of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao 266580, China;
2. State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Huadong), Qingdao 266580, China

收稿日期:2013-06-25 修回日期:2013-11-27 出版日期:2014-07-28 发布日期:2014-08-23
通讯作者: Xuemin Tian
基金资助:
Supported by the National Natural Science Foundation of China (61273160) and the Fundamental Research Funds for the Central Universities (14CX06067A, 13CX05021A).

Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Weiming Shao¹, Xuemin Tian¹, Ping Wang^1,2

1. College of Information and Control Engineering, China University of Petroleum (Huadong), Qingdao 266580, China;
2. State Key Laboratory of Heavy Oil Processing, China University of Petroleum (Huadong), Qingdao 266580, China

Received:2013-06-25 Revised:2013-11-27 Online:2014-07-28 Published:2014-08-23
Supported by:
Supported by the National Natural Science Foundation of China (61273160) and the Fundamental Research Funds for the Central Universities (14CX06067A, 13CX05021A).

摘要/Abstract

摘要： Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation, which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted. Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.

关键词: Local learning, Online soft sensing, Partial least squares, F-test, Multi-output process, Process state division

Abstract: Local learning based soft sensing methods succeed in coping with time-varying characteristics of processes as well as nonlinearities in industrial plants. In this paper, a local partial least squares based soft sensing method for multi-output processes is proposed to accomplish process states division and local model adaptation, which are two key steps in development of local learning based soft sensors. An adaptive way of partitioning process states without redundancy is proposed based on F-test, where unique local time regions are extracted. Subsequently, a novel anti-over-fitting criterion is proposed for online local model adaptation which simultaneously considers the relationship between process variables and the information in labeled and unlabeled samples. Case study is carried out on two chemical processes and simulation results illustrate the superiorities of the proposed method from several aspects.

Key words: Local learning, Online soft sensing, Partial least squares, F-test, Multi-output process, Process state division

Weiming Shao, Xuemin Tian, Ping Wang. Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division[J]. , 2014, 22(7): 828-836.

参考文献

[1] L. Fortuna, S. Graziani, A. Rizzo, G.M. Xibilia, Soft sensors for monitoring and control of industrial processes, Springer-Verlag, London, 2007.
[2] P. Kadlec, B. Gabrys, S. Strandt, Data-driven soft sensors in the process industry, Comput. Chem. Eng. 33 (4) (2009) 795-814.
[3] Z.Q. Ge, Z.H. Song, Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples, AIChE J. 57 (8) (2011) 2109-2118.
[4] H.J. Galicia, Q.P. He, W. Jin, A reduced order soft sensor approach and its application to continuous digester, J. Process Control 21 (4) (2011) 489-500.
[5] J.C.B. Gonzaga, L.A.C. Meleiro, C. Kiang, R. Maciel Filho, ANN-based soft-sensor for real-time process motoring and control of an industrial polymerization process, Comput. Chem. Eng. 33 (1) (2009) 43-49.
[6] J. Yu, A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses, Comput. Chem. Eng. 41 (1) (2012) 134-144.
[7] P. Kadlec, R. Grbić, Review of adaptation mechanisms for data-driven soft sensors, Comput. Chem. Eng. 35 (1) (2011) 1-24.
[8] J. Tang, W. Yu, T.Y. Chai, L.J. Zhao, On-line principal component analysis with application to process modeling, Neurocomputing 87 (4) (2012) 167-178.
[9] S.J. Qin, Recursive PLS algorithms for adaptive data modeling, Comput. Chem. Eng. 22 (4-5) (1998) 503-514.
[10] P.Wang,H.G. Tian,X.M.Tian, D.X. Huang,Anewapproach foronlineadaptivemodeling using incremental support vector regression, CISEC J. 61 (8) (2010) 2040-2045.
[11] O. Carlos, O. Edward, Efficient disk-based k-means clustering for relational databases, IEEE Trans. Knowl. Data Eng. 16 (8) (2004) 909-921.
[12] Y.F. Fu, H.Y. Su, Y. Zhang, J. Chu, Adaptive soft-sensor modeling algorithm based on FCMISVMand its application in PX adsorption separation process, Chin. J. Chem. Eng. 16 (5) (2008) 746-751.
[13] J. Yu, Online quality prediction of nonlinear and non-Gaussian chemical processes with shifting dynamics using finite mixture model based Gaussian process regression approach, Chem. Eng. Sci. 82 (2012) 22-30.
[14] K. Fujiwara, M. Kano, S. Hasebe, A. Takinami, Soft-sensor development using correlation-based just-in-time modeling, AIChE J. 55 (7) (2009) 1754-1764.
[15] W.D. Ni, S.K. Tan,W.J. Ng, S.D. Brown, Localized, adaptive recursive partial least squares regression for dynamic system modeling, Ind. Eng. Chem. Res. 51 (8) (2012) 8025-8039.
[16] P. Kadlec, B. Gabrys, Local learning-based adaptive soft sensor for catalyst activation prediction, AIChE J. 57 (5) (2009) 1288-1301.
[17] S. Khatibisepehr, B. Huang, F.W. Xu, A. Espejo, A Bayesian approach to design of adaptive multi-model inferential soft sensors with application in oil sand industry, J. Process Control 22 (10) (2012) 1913-1929.
[18] S.N. Zhang, F.L. Wang, D.K. He, R.D. Jia, Real-time product quality control for batch processes based on stacked least-squares support vector regressionmodels, Comput. Chem. Eng. 36 (10) (2012) 217-226.
[19] C. Cheng, M.S. Chiu, A newdata-basedmethodology for nonlinear processmodeling, Chem. Eng. Sci. 59 (13) (2004) 2801-2810.
[20] K. Chen, J. Ji,H.Q.Wang, Z.H. Song,Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes, Chem. Eng. Res. Des. 89 (10) (2011) 2117-2124.
[21] Y. Liu, Z.L. Gao, P. Li, H.Q.Wang, Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes, Ind. Eng. Chem. Res. 51 (11) (2012) 4313-4327.
[22] Y.Q. Liu, D.P. Huang, Y. Li, Development of interval soft sensors using enhanced just-in-time learning and inductive confidence predictor, Ind. Eng. Chem. Res. 51 (8) (2012) 3356-3367.
[23] P. Kadlec, B. Gabrys, Adaptive on-line prediction soft sensing without historical data, Proceedings of the 2010 International Joint Conference on Neural Networks, Barcelona, 2010.
[24] P. Geladi, R.K. Bruce, Partial least-squares regression: a tutorial, Anal. Chim. Acta. 185 (1986) 1-17.
[25] F. Lindgren, P. Geladi, S.Wold, The kernel algorithmfor PLS, J. Chemom. 7 (1) (1993) 45-59.
[26] B.S. Dayal, J.F. MacGregor, Improved PLS algorithms, J. Chemom. 11 (1) (1997) 73-85.
[27] J.L. Liu, Development of self-validating soft sensors using fast moving window partial least squares, Ind. Eng. Chem. Res. 49 (22) (2010) 11530-11546.
[28] X.Q. He, multivariate statistical analysis, China Renmin University Press, Beijing, 2004.
[29] Y. Liu, H.Q. Wang, J. Yu, P. Li, Selective recursive kernel learning for online identification of nonlinear systems with NARX form, J. Process Control 20 (2) (2010) 181-194.

Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

Local Partial Least Squares Based Online Soft Sensing Method for Multi-output Processes with Adaptive Process States Division

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价

[1]	Jian Tang, Tianyou Chai, Zhuo Liu, Wen Yu. Selective ensemble modeling based on nonlinear frequency spectral feature extraction for predicting load parameter in ball mills[J]. Chinese Journal of Chemical Engineering, 2015, 23(12): 2020-2028.
[2]	王丽, 侍洪波. Improved Kernel PLS-based Fault Detection Approach for Nonlinear Chemical Processes[J]. Chinese Journal of Chemical Engineering, 2014, 22(6): 657-663.
[3]	赵露平, 赵春晖, 高福荣. Phase Transition Analysis Based Quality Prediction for Multi-phase Batch Processes^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(6): 1191-1197.
[4]	费正顺, 胡斌, 叶鲁彬, 梁军. ARX-NNPLS Model Based Optimization Strategy and Its Application in Polymer Grade Transition Process^*[J]. Chinese Journal of Chemical Engineering, 2012, 20(5): 971-979.
[5]	胡斌, 郑平友, 梁军. Multi-loop Internal Model Controller Design Based on a Dynamic PLS Framework[J]. , 2010, 18(2): 277-285.
[6]	宋凯, 王海清, 李平, 冯志刚. Quality Based Prioritized Sensor Fault Monitoring Methodology[J]. , 2008, 16(4): 584-589.
[7]	傅永峰, 苏宏业, 褚健. 基于混合建模技术的复合肥养分含量MIMO软测量模型[J]. , 2007, 15(4): 554-559.
[8]	李运锋,汪志锋, 袁景淇. 基于支持向量机MPLS的间歇过程故障诊断方法[J]. , 2006, 14(6): 754-758.
[9]	梁军, 钱积新. 多变量统计过程监控:进展及其在化学工业的应用[J]. , 2003, 11(2): 191-203.