Premium
Fast property prediction in an industrial rubber mixing process with local ELM model
Author(s) -
Jin Weiya,
Liu Yi,
Gao Zengliang
Publication year - 2017
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.45391
Subject(s) - extreme learning machine , mixing (physics) , mooney viscosity , regularization (linguistics) , natural rubber , viscosity , computer science , process (computing) , property (philosophy) , artificial intelligence , biological system , materials science , artificial neural network , composite material , polymer , physics , philosophy , epistemology , quantum mechanics , operating system , copolymer , biology
ABSTRACT Online property prediction in industrial rubber mixing processes is not an easy task. An efficient data‐driven prediction model is developed in this work. The regularized extreme learning machine (RELM) is utilized as the fundamental soft sensor model. To better capture distinguished characteristics in multiple recipes and operating modes, a just‐in‐time RELM modeling method is developed. The number of hidden neurons and the value of regularization parameter of the just‐in‐time RELM model can be efficiently selected using a fast leave‐one‐out strategy. Consequently, without the time‐consuming laboratory analysis process, the Mooney viscosity can be online predicted once a mixing batch has been discharged. The industrial Mooney viscosity prediction results show its better prediction performance in comparison with traditional approaches. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 45391.