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

Miao Zhang; Xinggao Liu; Zeyin Zhang

doi:10.1016/j.cjche.2016.05.030

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

Process Systems Engineering and Process Safety | Updated：2026-01-06

- A soft sensor for industrial melt index prediction based on evolutionary extreme learning machine
- Chinese Journal of Chemical Engineering Vol. 24, Issue 8, Pages: 1013-1019(2016)
- Affiliations：
  
  1. State Key Laboratory of Industrial Control Technology, Department of Control Science and Engineering, Zhejiang University,Hangzhou,China,310027
  2. Department of Mathematics, Zhejiang University,Hangzhou,China,310027
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2016.05.030
  CLC：
- Published：2016
- Accepted：
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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.
DOI：

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. DOI： 10.1016/j.cjche.2016.05.030.

摘要

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.

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