Adaptive soft sensor for quality prediction of chemical processes based on selective ensemble of local partial least squares models

Chemical Engineering Research and Design - Tập 95 - Trang 113-132 - 2015
Weiming Shao1, Xuemin Tian1
1College of Information and Control Engineering, China University of Petroleum, 66#, Changjiang West Road, Huangdao District, Qingdao 266580, China

Tài liệu tham khảo

Chen, 2011, Adaptive local kernel-based learning for soft sensor modeling of nonlinear processes, Chem. Eng. Res. Design, 89, 2117, 10.1016/j.cherd.2011.01.032 Dayal, 1997, Recursive exponentially weighted PLS and its applications to adaptive control and prediction, J. Process Control, 7, 169, 10.1016/S0959-1524(97)80001-7 Deng, 2013, Development and industrial application of soft sensors with on-line Bayesian model updating strategy, J. Process Control, 22, 317, 10.1016/j.jprocont.2012.12.008 Fu, 2008, Adaptive soft-sensor modeling algorithm based on FCMISVM and its application in PX adsorption separation process, Chin. J. Chem. Eng., 16, 746, 10.1016/S1004-9541(08)60150-0 Fujiwara, 2009, Soft-sensor development using correlation-based just-in-time modeling, AIChE J., 55, 1754, 10.1002/aic.11791 Fortuna, 2007 Galicia, 2011, A reduced order soft sensor approach and its application to continuous digester, J. Process Control, 21, 489, 10.1016/j.jprocont.2011.02.001 Ge, 2011, Semisupervised Bayesian method for soft sensor modeling with unlabeled data samples, AIChE J., 57, 2109, 10.1002/aic.12422 Ge, 2014, Ensemble independent component regression models and soft sensing application, Chemom. Intell. Lab. Syst., 130, 115, 10.1016/j.chemolab.2013.09.009 Ge, 2014, Probabilistic combination of local independent component regression model for multimode quality prediction in chemical processes, Chem. Eng. Res. Design, 92, 501, 10.1016/j.cherd.2013.09.010 Geman, 1992, Neural networks and the bias/variance dilemma, Neural Comput., 4, 1, 10.1162/neco.1992.4.1.1 Grbić, 2013, Adaptive soft sensor for online prediction and process monitoring based on a mixture of Gaussian process models, Comput. Chem. Eng., 58, 84, 10.1016/j.compchemeng.2013.06.014 Himmelblau, 2008, Accounts of experiences in the application of artificial neural networks in chemical engineering, Ind. Eng. Chem. Res., 47, 5782, 10.1021/ie800076s Kadlec., 2009 Kadlec, 2009, Data-driven soft sensors in the process industry, Comput. Chem. Eng., 33, 795, 10.1016/j.compchemeng.2008.12.012 Kadlec, 2011, Review of adaptation mechanisms for data-driven soft sensors, Comput. Chem. Eng., 35, 1, 10.1016/j.compchemeng.2010.07.034 Kadlec, 2011, Local learning based adaptive soft sensor for catalyst activation prediction, AIChE J., 57, 1288, 10.1002/aic.12346 Kaneko, 2009, Development of a new soft sensor method using independent component analysis and partial least squares, AIChE J., 55, 87, 10.1002/aic.11648 Kaneko, 2011, Maintenance-free soft sensor models with time difference of process variables, Chemom. Intell. Lab. Syst., 107, 312, 10.1016/j.chemolab.2011.04.016 Kaneko, 2011, Development of soft sensor models based on time difference of process variables with accounting for nonlinear relationship, Ind. Eng. Chem. Res., 58, 10643, 10.1021/ie200692m Kaneko, 2013, Classification of the degradation of soft sensor models and discussion on adaptive models, AIChE J., 59, 2339, 10.1002/aic.14006 Kaneko, 2014, Adaptive soft sensor based on online support vector regression and Bayesian ensemble learning for various states in chemical plants, Chemom. Intell. Lab. Syst., 137, 57, 10.1016/j.chemolab.2014.06.008 Kaneko, 2014, Database monitoring index for adaptive soft sensors and the application to industrial process, AIChE J., 60, 160, 10.1002/aic.14260 Kano, 2010, The state of the art in chemical process control in Japan: good practice and questionnaire survey, J. Process Control, 20, 969, 10.1016/j.jprocont.2010.06.013 Kano, 2013, Virtual sensing technology in process industries: trends and challenges revealed by recent industrial applications, J. Chem. Eng. Jpn., 46, 1, 10.1252/jcej.12we167 Khatibisepehr, 2012, A Bayesian approach to design of adaptive multi-model inferential sensors with application in oil sand industry, J. Process Control, 22, 1913, 10.1016/j.jprocont.2012.09.006 Kim, 2013, Long-term industrial applications of inferential control based on just-in-time soft sensors: economical impact and challenges, Ind. Eng. Chem. Res., 52, 12346, 10.1021/ie303488m Kim, 2013, Development of soft sensor using locally weighted PLS with adaptive similarity measure, Chemom. Intell. Lab. Syst., 124, 43, 10.1016/j.chemolab.2013.03.008 Liu, 2007, On-line soft sensor for polyethylene process with multiple production grades, Control Eng. Pract., 15, 769, 10.1016/j.conengprac.2005.12.005 Liu, 2010, Development of self-validating soft sensors using fast moving window partial least squares, Ind. Eng. Chem. Res., 49, 11530, 10.1021/ie101356c Liu, 2012, Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes, Ind. Eng. Chem. Res., 51, 4313, 10.1021/ie201650u Liu, 2013, Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes, J. Process Control, 33, 793, 10.1016/j.jprocont.2013.03.008 Liu, 2014, A novel unified correlation model using ensemble support vector regression for prediction of flooding velocity in randomly packed towers, J. Ind. Eng. Chem., 22, 1109, 10.1016/j.jiec.2013.06.049 Lv, 2013, A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler, Energy, 55, 319, 10.1016/j.energy.2013.02.062 Ni, 2012, Moving-window GRP for nonlinear dynamic system modeling with dual updating and dual preprocessing, Ind. Eng. Chem. Res., 51, 6416, 10.1021/ie201898a Ni, 2012, Localized, adaptive recursive partial least squares regression for dynamic system modeling, Ind. Eng. Chem., 55, 8025, 10.1021/ie203043q Ni, 2014, A localized adaptive soft sensor for dynamic system modeling, Chem. Eng. Sci., 111, 250 Pani, 2011, A survey of data treatment techniques for soft sensor design, Chem. Product Process Model., 6, 1 Qin, 1998, Recursive PLS algorithms for adaptive data modeling, Comput. Chem. Eng., 22, 503, 10.1016/S0098-1354(97)00262-7 Shao, 2012, Online learning soft sensor method based on recursive kernel algorithm for PLS, J. Chem. Ind. Eng. Soc. China, 63, 2887 Shao, 2013, Adaptive anti-over-fitting soft sensing method based on local learning, Prepr. 10th IFAC Int. Symp. Dyn. Control Process Syst., 10, 415 Shao, 2014, Local partial least squares based online soft sensing method for multi-output processes with adaptive process states division, Chin. J. Chem. Eng., 22, 828, 10.1016/j.cjche.2014.05.003 Tang, 2012, On-line principal component analysis with application to process modeling, Neurocomputing, 82, 167, 10.1016/j.neucom.2011.10.026 Tang, 2012, Soft sensor for parameters of mill load based on multi-spectral segments PLS sub-models and on-line adaptive weighted fusion algorithm, Neurocomputing, 78, 38, 10.1016/j.neucom.2011.05.028 Tang, 2013, Modeling load parameters of ball mill in grinding process based on selective ensemble multisensory information, IEEE Trans. Autom. Sci. Eng., 10, 726, 10.1109/TASE.2012.2225142 Wu, 2010, Soft sensing method for magnetic tube recovery ratio via fuzzy systems and neural networks, Neurocomputing, 73, 2489, 10.1016/j.neucom.2009.12.036 Xie, 2014, Novel just-in-time learning-based soft sensor utilizing non-Gaussian information, IEEE Trans. Control Syst. Technol., 22, 360, 10.1109/TCST.2013.2248155 Xu, 2014, Melt index prediction by fuzzy functions with dynamic fuzzy neural networks, Neurocomputing, 142, 191, 10.1016/j.neucom.2013.10.025 Yu, 2012, A Bayesian inference based two-stage support vector regression framework for soft sensor development in batch bioprocesses, Comput. Chem. Eng., 41, 134, 10.1016/j.compchemeng.2012.03.004 Yu, 2012, 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, 22, 10.1016/j.ces.2012.07.018 Yu, 2013, A Bayesian model averaging based multi-kernel Gaussian process regression framework for nonlinear state estimation and quality prediction of multiphase batch processes with transient dynamics and uncertainty, Chem. Eng. Sci., 93, 96, 10.1016/j.ces.2013.01.058 Zhang, 2012, Real-time product quality control for batch processes based on stacked least squared support vector regression models, Comput. Chem. Eng., 36, 217, 10.1016/j.compchemeng.2011.05.015 Zhang, 2013, Online quality prediction for cobalt oxalate synthesis process using least squares support vector regression approach with dual updating, Control Eng. Pract., 21, 1267, 10.1016/j.conengprac.2013.06.002 Zhao, L.J., Chai, T.Y., Yuan, D.C., 2012. Selective ensemble extreme learning machine modeling of effluent quality in wastewater treatment plants. 9(6), 627–633. Zhou, 2002, Ensembling neural networks: many could be better than all, Artif. Intell., 137, 239, 10.1016/S0004-3702(02)00190-X