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

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 GSA-ELM 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. (C) 2016 The Chemical Industry and Engineering Society of China, and Chemical Industry Press. All rights reserved.