A novel modeling approach and its application in polymer quality index prediction

Abstract

Accurate and reliable quality index prediction is indispensable in quality control of the industrial propylene polymerization (PP) processes. This paper presents a novel modeling approach for quality index prediction based on optimal fuzzy wavelet neural network (FWNN) with improved gravitational search algorithm (IGSA), where the constant or a linear function of inputs in conclusion part of traditional TSK fuzzy model is replaced with wavelet neural network (WNN). Then, an online learning algorithm of the FWNN model is derived by using gradient descent algorithm, and an IGSA algorithm is proposed to online adapt the learning rates of FWNN. Research on the proposed soft sensor is carried out with the data from a real industrial PP plant, and the results are compared among the WNN, FWNN and IGSA-FWNN models. The research results show that the proposed prediction model achieves a good performance in practical industrial quality index, melt index, prediction process.