Semi-Supervised Hybrid Local Kernel Regression for Soft Sensor Modelling of Rubber-Mixing Process

Soft sensor techniques have been widely adopted in chemical industry to estimate important indices that cannot be online measured by hardware sensors. Unfortunately, due to the instinct time-variation, the small-sample condition and the uncertainty caused by the drifting of raw materials, it is exceedingly difficult to model the fed-batch processes, for instance, rubber internal mixing processing. Meanwhile, traditional global learning algorithms suffer from the outdated samples while online learning algorithms lack practicality since too many labelled samples of current batch are required to build the soft sensor. In this paper, semi-supervised hybrid local kernel regression (SHLKR) is presented to leverage both historical and online samples to semi-supervised model the soft sensor using proposed time-windows series. Moreover, the recursive formulas are deduced to improve its adaptability and feasibility. Additionally, the rubber Mooney soft sensor of internal mixing processing is implemented using real onsite data to validate proposed method. Compared with classical algorithms, the performance of SHLKR is evaluated and the contribution of unlabelled samples is discussed.

[1]  P. K. Freakley,et al.  Internal Mixing: A Practical Investigation of the Flow and Temperature Profiles during a Mixing Cycle , 1985 .

[2]  J. Suykens,et al.  Chaos control using least squares support vector machines , 1999 .

[3]  Gunnar Rätsch,et al.  Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers , 2001, ICANN.

[4]  장윤희,et al.  Y. , 2003, Industrial and Labor Relations Terms.

[5]  Mikhail Belkin,et al.  Manifold Regularization : A Geometric Framework for Learning from Examples , 2004 .

[6]  Geoffrey I. Webb,et al.  Lazy Learning of Bayesian Rules , 2000, Machine Learning.

[7]  Ronald Rosenfeld,et al.  Semi-supervised learning with graphs , 2005 .

[8]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[9]  In-Beum Lee,et al.  A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction , 2005 .

[10]  Mikhail Belkin,et al.  Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples , 2006, J. Mach. Learn. Res..

[11]  Ping Li,et al.  Kernel classifier with adaptive structure and fixed memory for process diagnosis , 2006 .

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

[13]  Xiaojin Zhu,et al.  Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[14]  Pierantonio Facco,et al.  Moving average PLS soft sensor for online product quality estimation in an industrial batch polymerization process , 2009 .

[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]  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..

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

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

[19]  Ping Li,et al.  Adaptive least contribution elimination kernel learning approach for rubber mixing soft-sensing modeling , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[20]  W. Marsden.  I and J , 2012 .

[21]  Junjie Yan,et al.  Recursive weighted kernel regression for semi-supervised soft-sensing modeling of fed-batch processes , 2012 .

[22]  Zengliang Gao,et al.  Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes , 2012 .

[23]  Yi Liu,et al.  Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes , 2013 .

[24]  Junghui Chen,et al.  Auto-Switch Gaussian Process Regression-Based Probabilistic Soft Sensors for Industrial Multigrade Processes with Transitions , 2015 .

[25]  Yi Liu,et al.  Real-time property prediction for an industrial rubber-mixing process with probabilistic ensemble Gaussian process regression models , 2015 .

[26]  Junghui Chen,et al.  Flame Images for Oxygen Content Prediction of Combustion Systems Using DBN , 2017 .

[27]  Junghui Chen,et al.  Active Selection of Informative Data for Sequential Quality Enhancement of Soft Sensor Models with Latent Variables , 2017 .

[28]  Xuejin Gao,et al.  Industrial Mooney viscosity prediction using fast semi-supervised empirical model , 2017 .

[29]  Jian Zhang,et al.  Automatic Pearl Classification Machine Based on a Multistream Convolutional Neural Network , 2018, IEEE Transactions on Industrial Electronics.

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

[31]  Yi Liu,et al.  Just-in-time semi-supervised soft sensor for quality prediction in industrial rubber mixers , 2018, Chemometrics and Intelligent Laboratory Systems.

[32]  Qi Xuan,et al.  Multiview Generative Adversarial Network and Its Application in Pearl Classification , 2019, IEEE Transactions on Industrial Electronics.

[33]  W. Hager,et al.  and s , 2019, Shallow Water Hydraulics.

[34]  R. Shah,et al.  Least Squares Support Vector Machines , 2022 .