Soft sensor development for the key variables of complex chemical processes using a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square
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- 作者：YanLin He ; ZhiQiang Geng ; QunXiong Zhu ; zhuqx_buctielab@163.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：Partial least square ; Extreme learning machine ; Bagging ; Soft sensor ; Tennessee Eastman process ; Purified Terephthalic Acid Process
- 刊名：Chemometrics and Intelligent Laboratory Systems
- 出版年：2016
- 出版时间：15 February 2016
- 年：2016
- 卷：151
- 期：Complete
- 页码：78-88
- 全文大小：1365 K
文摘
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An effective RB-PLSIELM model is proposed for modeling complex processes.
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The bagging strategy is adopted to generate the sub-training data.
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An improved ELM with the double parallel structure is adopted in the individual model.
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RB-PLSIELM can achieve high prediction accuracy and stability.
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RB-PLSIELM based soft sensor is developed to predict the key process variables in TEP and PTAP.