A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine

doi:10.1016/j.cjche.2016.05.030

›› 2016, Vol. 24 ›› Issue (8): 1013-1019.DOI: 10.1016/j.cjche.2016.05.030

• Process Systems Engineering and Process Safety • 上一篇下一篇

A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine

Miao Zhang¹, Xinggao Liu¹, Zeyin Zhang²

1 State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;
2 Department of Mathematics, Zhejiang University, Hangzhou 310027, China

收稿日期:2015-06-26 修回日期:2015-12-20 出版日期:2016-08-28 发布日期:2016-09-21
通讯作者: Xinggao Liu
基金资助:
Supported by the Major Program of National Natural Science Foundation of China (61590921), the Natural Science Foundation of Zhejiang Province (Y16B040003), Shanghai Aerospace Science and Technology Innovation Fund (E11501) and Aerospace Science and Technology Innovation Fund of China, Aerospace Science and Technology Corporation (E11601).

A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine

Miao Zhang¹, Xinggao Liu¹, Zeyin Zhang²

1 State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China;
2 Department of Mathematics, Zhejiang University, Hangzhou 310027, China

Received:2015-06-26 Revised:2015-12-20 Online:2016-08-28 Published:2016-09-21
Supported by:
Supported by the Major Program of National Natural Science Foundation of China (61590921), the Natural Science Foundation of Zhejiang Province (Y16B040003), Shanghai Aerospace Science and Technology Innovation Fund (E11501) and Aerospace Science and Technology Innovation Fund of China, Aerospace Science and Technology Corporation (E11601).

摘要/Abstract

摘要： In propylene polymerization (PP) process, the melt index (MI) is one of the most important quality variables for determining different brands of products and different grades of product quality. Accurate prediction of MI is essential for efficient and professional monitoring and control of practical PP processes. This paper presents a novel soft sensor based on extreme learning machine (ELM) and modified gravitational search algorithm (MGSA) to estimate MI from real PP process variables, where the MGSA algorithm is developed to find the best parameters of input weights and hidden biases for ELM. As the comparative basis, the models of ELM, APSO-ELM and GSAELM are also developed respectively. Based on the data from a real PP production plant, a detailed comparison of the models is carried out. The research results show the accuracy and universality of the proposed model and it can be a powerful tool for online MI prediction.

关键词: Propylene polymerization, Melt index prediction, Extreme learning machine, Gravitational search algorithm

Abstract: In propylene polymerization (PP) process, the melt index (MI) is one of the most important quality variables for determining different brands of products and different grades of product quality. Accurate prediction of MI is essential for efficient and professional monitoring and control of practical PP processes. This paper presents a novel soft sensor based on extreme learning machine (ELM) and modified gravitational search algorithm (MGSA) to estimate MI from real PP process variables, where the MGSA algorithm is developed to find the best parameters of input weights and hidden biases for ELM. As the comparative basis, the models of ELM, APSO-ELM and GSAELM are also developed respectively. Based on the data from a real PP production plant, a detailed comparison of the models is carried out. The research results show the accuracy and universality of the proposed model and it can be a powerful tool for online MI prediction.

Key words: Propylene polymerization, Melt index prediction, Extreme learning machine, Gravitational search algorithm

Miao Zhang, Xinggao Liu, Zeyin Zhang. A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine[J]. , 2016, 24(8): 1013-1019.

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A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine

A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine

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