A real‐time Mooney‐viscosity prediction model of the mixed rubber based on the Independent Component Regression‐Gaussian Process algorithm

With the rapid development of rubber industry, it becomes more and more important to improve the performance of the quality control system of rubber mixing process. Unfortunately, the large measurement time delay of Mooney viscosity, one of the most important quality parameters of mixed rubber, badly blocks the further development of the issue. The independent component regression‐Gaussian process (ICR‐GP) algorithm is used to solve such typical nonlinear “black‐box” regression problem for the first time to predict Mooney viscosity. In the ICR‐GP method, the non‐Gaussian information is extracted by the independent component regression method firstly, and then the residual Gaussian information is extracted by the Gaussian process method. Meanwhile, both the linear and nonlinear relationships between the input and output variables can be extracted through the ICR‐GP method. With the fact that there is no need to optimize parameters, the ICR‐GP method is especially suitable for “black‐box” regression problems. The highest prediction accuracy was achieved at M = 0.8765 (the root mean square error), which was high enough considering the measuring accuracy (M = ±0.5) of the Mooney viscometer. It is by using the online‐measured rheological parameters as the input variables that the measurement time delay of Mooney viscosity could be dramatically decreased from about 240 to 2 min. Consequently, such Mooney‐viscosity prediction model is very helpful for the development of the rubber mixing process, especially of the emerging one‐step rubber mixing technique. The practical applications performed on the rubber mixing process in a large‐scale tire factory strongly proved the outstanding regression performance of this ICR‐GP Mooney‐viscosity prediction model. Copyright © 2012 John Wiley & Sons, Ltd.

[1]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[2]  Chih-Chou Chiu,et al.  Financial time series forecasting using independent component analysis and support vector regression , 2009, Decis. Support Syst..

[3]  Fernando di Sciascio,et al.  Biomass estimation in batch biotechnological processes by Bayesian Gaussian process regression , 2008, Comput. Chem. Eng..

[4]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[5]  G. Padmavathi,et al.  Neural modelling of Mooney viscosity of polybutadiene rubber , 2005, Comput. Chem. Eng..

[6]  Ying-wei Zhang,et al.  Complex process quality prediction using modified kernel partial least squares , 2010 .

[7]  Philip K. Freakley.  Rubber processing and production organization , 1985 .

[8]  Zhi-huan Song,et al.  Process Monitoring Based on Independent Component Analysis - Principal Component Analysis ( ICA - PCA ) and Similarity Factors , 2007 .

[9]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[10]  J Kocijan,et al.  Application of Gaussian processes for black-box modelling of biosystems. , 2007, ISA transactions.

[11]  Bo-Suk Yang,et al.  Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors , 2007, Expert Syst. Appl..

[12]  Yingwei Zhang,et al.  Optimized independent components for parameter regression , 2010 .

[13]  Frank Westad,et al.  Independent component analysis and regression applied on sensory data , 2005 .

[14]  Geoffrey E. Hinton,et al.  Evaluation of Gaussian processes and other methods for non-linear regression , 1997 .

[15]  E. Martin,et al.  Gaussian process regression for multivariate spectroscopic calibration , 2007 .

[16]  Yuehjen E. Shao,et al.  Mixture control chart patterns recognition using independent component analysis and support vector machine , 2011, Neurocomputing.

[17]  Hiromasa Kaneko,et al.  Development of a new soft sensor method using independent component analysis and partial least squares , 2009 .

[18]  Yingwei Zhang,et al.  Process Monitoring, Fault Diagnosis and Quality Prediction Methods Based on the Multivariate Statistical Techniques , 2010 .

[19]  Ying-wei Zhang,et al.  Complex process monitoring using modified partial least squares method of independent component regression , 2009 .

[20]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[21]  Chunhui Zhao,et al.  An improved independent component regression modeling and quantitative calibration procedure , 2010 .

[22]  Bojan Likar,et al.  Predictive control of a gas-liquid separation plant based on a Gaussian process model , 2007, Comput. Chem. Eng..

[23]  X. Z. Wang,et al.  A New Approach to Near-Infrared Spectral Data Analysis Using Independent Component Analysis , 2001, J. Chem. Inf. Comput. Sci..

[24]  Haiqing Wang,et al.  Study of discharge modeling method using support vector machine for rubber mixing process , 2003, Proceedings of the 2003 American Control Conference, 2003..

[25]  An adaptive neuro-fuzzy inference system as a soft sensor for viscosity in rubber mixing process , 2000 .

[26]  W. Cai,et al.  A new regression method based on independent component analysis. , 2006, Talanta.

[27]  Yi Liu,et al.  Online prediction of Mooney viscosity in industrial rubber mixing process via adaptive kernel learning method , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[28]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[29]  In-Beum Lee,et al.  Fault Detection of Non-Linear Processes Using Kernel Independent Component Analysis , 2008 .

[30]  Kai Song,et al.  A novel nonlinear adaptive Mooney-viscosity model based on DRPLS-GP algorithm for rubber mixing process , 2012 .