Mixture modeling for industrial soft sensor application based on semi-supervised probabilistic PLS

Abstract Due to the difficulty in measuring key performance indices in the process, only a small portion of collected data may have values for both routinely recorded variables and key performance indices, while a large portion of data only has values for routinely recorded variables. In order to improve the performance of data-driven soft sensor modeling, the idea of semi-supervised learning is incorporated with the traditional partial least squares modeling method. Furthermore, the single semi-supervised model structure is extended to the mixture form, in order to handle more complex data characteristics. An efficient Expectation-Maximization algorithm is designed for model training. An industrial case study is presented for performance evaluation of the developed method, with a Bayesian inference approach developed for results integration of different local models.

[1]  Jing Wang,et al.  Quality-Relevant Fault Monitoring Based on Locally Linear Embedding Orthogonal Projection to Latent Structure , 2019 .

[2]  Zhiqiang Ge,et al.  Review on data-driven modeling and monitoring for plant-wide industrial processes , 2017 .

[3]  Chudong Tong,et al.  Distributed partial least squares based residual generation for statistical process monitoring , 2019, Journal of Process Control.

[4]  Zhiqiang Ge,et al.  Process Data Analytics via Probabilistic Latent Variable Models: A Tutorial Review , 2018, Industrial & Engineering Chemistry Research.

[5]  Yuan Yao,et al.  Semi-supervised mixture discriminant monitoring for chemical batch processes , 2014 .

[6]  Richard G. Brereton,et al.  A short history of chemometrics: a personal view , 2014 .

[7]  Jian Yang,et al.  Performance monitoring method based on balanced partial least square and Statistics Pattern Analysis. , 2018, ISA transactions.

[8]  Zhihuan Song,et al.  Semisupervised learning for probabilistic partial least squares regression model and soft sensor application , 2018 .

[9]  Zhiqiang Ge,et al.  Big data quality prediction in the process industry: A distributed parallel modeling framework , 2018, Journal of Process Control.

[10]  Marco S. Reis,et al.  Data-driven methods for batch data analysis - A critical overview and mapping on the complexity scale , 2019, Comput. Chem. Eng..

[11]  Zhiqiang Ge,et al.  Deep Learning of Semisupervised Process Data With Hierarchical Extreme Learning Machine and Soft Sensor Application , 2018, IEEE Transactions on Industrial Electronics.

[12]  R. Brereton.  Pattern recognition in chemometrics , 2015 .

[13]  Randy J. Pell,et al.  Process analytical chemistry and chemometrics, Bruce Kowalski's legacy at The Dow Chemical Company , 2014 .

[14]  Soon Keat Tan,et al.  Localized, Adaptive Recursive Partial Least Squares Regression for Dynamic System Modeling , 2012 .

[15]  Haiqing Wang,et al.  Soft Chemical Analyzer Development Using Adaptive Least-Squares Support Vector Regression with Selective Pruning and Variable Moving Window Size , 2009 .

[16]  Bogdan Gabrys,et al.  Data-driven Soft Sensors in the process industry , 2009, Comput. Chem. Eng..

[17]  Biao Huang,et al.  A Bayesian framework for real‐time identification of locally weighted partial least squares , 2015 .

[18]  Furong Gao,et al.  Review of Recent Research on Data-Based Process Monitoring , 2013 .

[19]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[20]  Jerry Workman,et al.  Process analytical chemistry. , 2009, Analytical chemistry.

[21]  Zhiqiang Ge,et al.  Locally Weighted Prediction Methods for Latent Factor Analysis With Supervised and Semisupervised Process Data , 2017, IEEE Transactions on Automation Science and Engineering.

[22]  K. Funatsu,et al.  Partial constrained least squares (PCLS) and application in soft sensor , 2018, Chemometrics and Intelligent Laboratory Systems.

[23]  Hiromasa Kaneko,et al.  Classification of the Degradation of Soft Sensor Models and Discussion on Adaptive Models , 2013 .

[24]  G. Alwan.  Adaptive Genetic PH Control of a Wastewater Treatment Unit via LABView , 2012 .

[25]  Junghui Chen,et al.  Dynamic soft sensors with active forward-update learning for selection of useful data from historical big database , 2018 .

[26]  Hiromasa Kaneko,et al.  Selective Use of Adaptive Soft Sensors Based on Process State , 2014 .

[27]  Jin Wang,et al.  Comparison of the performance of a reduced-order dynamic PLS soft sensor with different updating schemes for digester control , 2012 .

[28]  Di Tang,et al.  A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application , 2017, IEEE Transactions on Industrial Electronics.

[29]  Zhiqiang Ge,et al.  Probabilistic learning of partial least squares regression model: Theory and industrial applications , 2016 .

[30]  Morimasa Ogawa,et al.  The state of the art in chemical process control in Japan: Good practice and questionnaire survey , 2010 .

[31]  Zhiqiang Ge,et al.  Dynamic Probabilistic Latent Variable Model for Process Data Modeling and Regression Application , 2019, IEEE Transactions on Control Systems Technology.

[32]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[33]  Ali Cinar,et al.  Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods , 2012, Comput. Chem. Eng..

[34]  Zhiqiang Ge,et al.  Scalable Semisupervised GMM for Big Data Quality Prediction in Multimode Processes , 2019, IEEE Transactions on Industrial Electronics.

[35]  Chao Yang,et al.  Ensemble deep kernel learning with application to quality prediction in industrial polymerization processes , 2018 .

[36]  O. Chapelle,et al.  Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews] , 2009, IEEE Transactions on Neural Networks.

[37]  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..

[38]  Dexian Huang,et al.  Probabilistic slow feature analysis‐based representation learning from massive process data for soft sensor modeling , 2015 .

[39]  Zhiqiang Ge,et al.  Data Mining and Analytics in the Process Industry: The Role of Machine Learning , 2017, IEEE Access.

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

[41]  Steven D. Brown,et al.  Highly-overlapped, recursive partial least squares soft sensor with state partitioning via local variable selection , 2018 .

[42]  Zhiqiang Ge,et al.  Semisupervised JITL Framework for Nonlinear Industrial Soft Sensing Based on Locally Semisupervised Weighted PCR , 2017, IEEE Transactions on Industrial Informatics.

[43]  Bo Lu,et al.  Semi-supervised online soft sensor maintenance experiences in the chemical industry , 2017 .