JITL based MWGPR soft sensor for multi-mode process with dual-updating strategy

Abstract Process nonlinearity, multiple operating modes and time-varying characteristics often deteriorate the prediction performance of process models. In this article, a multi-mode moving-window Gaussian process regression (MWGPR) based approach for ARX modeling is proposed to effectively capture process nonlinearity or switching dynamics. The Gaussian mixture model (GMM) is first introduced to separate the data into different operating modes. Then the MWGPR strategy is applied to identify the local ARX model. Just-in-time learning (JITL) and dual updating are applied for more effective tracking of process dynamics. A simulation of a continuous fermentation process and a pilot scale experiment are presented to demonstrate the effectiveness of the proposed method.

[1]  Manabu Kano,et al.  Soft‐sensor development using correlation‐based just‐in‐time modeling , 2009 .

[2]  Hui-he Shao,et al.  On-line estimation of concentration parameters in fermentation processes. , 2005, Journal of Zhejiang University. Science. B.

[3]  Tor Arne Johansen,et al.  Explicit stochastic predictive control of combustion plants based on Gaussian process models , 2008, Autom..

[4]  Yeong-Koo Yeo,et al.  A recursive PLS-based soft sensor for prediction of the melt index during grade change operations in HDPE plant , 2009 .

[5]  Shao Hui-he.  GP-based Soft Sensor Modeling , 2005 .

[6]  Zhiqiang Ge,et al.  Quality prediction for polypropylene production process based on CLGPR model , 2011 .

[7]  Lyle H. Ungar,et al.  A first principles approach to automated troubleshooting of chemical plants , 1990 .

[8]  Milton Mori,et al.  Application of steady-state and dynamic modeling for the prediction of the BOD of an aerated lagoon , 2004 .

[9]  Thomas Mc Avoy.  Intelligent “control” applications in the process industries , 2002 .

[10]  Jie Yu,et al.  A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses , 2012, Comput. Chem. Eng..

[11]  Thomas J. McAvoy.  Intelligent “control” applications in the process industries , 2000 .

[12]  René Vidal,et al.  Identification of Hybrid Systems: A Tutorial , 2007, Eur. J. Control.

[13]  Min-Sen Chiu,et al.  Nonlinear process monitoring using JITL-PCA , 2005 .

[14]  Dale E. Seborg,et al.  Nonlinear control strategies for continuous fermenters , 1992 .

[15]  Biao Huang,et al.  Multiple model approach to nonlinear system identification with an uncertain scheduling variable using EM algorithm , 2013 .

[16]  Ravindra D. Gudi,et al.  Identification of complex nonlinear processes based on fuzzy decomposition of the steady state space , 2003 .

[17]  Carl E. Rasmussen,et al.  Derivative Observations in Gaussian Process Models of Dynamic Systems , 2002, NIPS.

[18]  Ping Wu,et al.  Online dual updating with recursive PLS model and its application in predicting crystal size of purified terephthalic acid (PTA) process , 2006 .

[19]  Thomas J. McAvoy,et al.  Nonlinear PLS Modeling Using Neural Networks , 1992 .

[20]  Luigi Fortuna,et al.  SOFT ANALYSERS FOR A SULFUR RECOVERY UNIT , 2002 .

[21]  Gordon Lightbody,et al.  Gaussian process approach for modelling of nonlinear systems , 2009, Eng. Appl. Artif. Intell..

[22]  Luiz Augusto da Cruz Meleiro,et al.  ANN-based soft-sensor for real-time process monitoring and control of an industrial polymerization process , 2009, Comput. Chem. Eng..

[23]  Jesús Picó,et al.  Multi-phase principal component analysis for batch processes modelling , 2006 .

[24]  Furong Gao,et al.  A survey on multistage/multiphase statistical modeling methods for batch processes , 2009, Annu. Rev. Control..

[25]  A. J. Morris,et al.  A comparison of canonical variate analysis and partial least squares for the identification of dynamic processes , 1999, Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251).

[26]  Xiao Fan Wang,et al.  Soft sensing modeling based on support vector machine and Bayesian model selection , 2004, Comput. Chem. Eng..

[27]  Satish T S Bukkapatnam,et al.  Forecasting the evolution of nonlinear and nonstationary systems using recurrence-based local Gaussian process models. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[28]  Bogdan Gabrys,et al.  Review of adaptation mechanisms for data-driven soft sensors , 2011, Comput. Chem. Eng..

[29]  Wei Sun,et al.  A method for multiphase batch process monitoring based on auto phase identification , 2011 .

[30]  A. Simoglou,et al.  Dynamic multivariate statistical process control using partial least squares and canonical variate analysis , 1999 .

[31]  Dale E. Seborg,et al.  Application of steady-state and dynamic modeling for the prediction of the BOD of an aerated lagoon at a pulp and paper mill Part II. Nonlinear approaches , 2004 .