A real‐time model based on optimized least squares support vector machine for industrial polypropylene melt index prediction

Abstract

The accurate and reliable real‐time prediction of melt index (MI) is indispensable in quality control of the industrial propylene polymerization (PP) processes. This paper presents a real‐time soft sensor based on optimized least squares support vector machine (LSSVM) for MI prediction. First, the hybrid continuous ant colony differential evolution algorithm (HACDE) is proposed to optimize the parameters of LSSVM. Then, considering the complexity and nondeterminacy of PP plant, an online correcting strategy (OCS) is adopted to update the modeling data and to revise the model's parameters adaptively. Thus, the real‐time prediction model, HACDE‐OCS‐LSSVM, is obtained. Based on the data from a real PP plant, the models of HACDE‐LSSVM, DE‐LSSVM and LSSVM are also developed for comparison. The research results show that the proposed real‐time model achieves a good performance in the practical industrial MI prediction process. Copyright © 2016 John Wiley & Sons, Ltd.

Modeling and prediction of wear rate of grinding media in mineral processing industry using multiple kernel support vector machine

“…In recent years, data-driven soft sensors due to delayfree and low-cost properties have been widely developed and utilized to predict the behavior of the chemical processes, especially multi-grade processes [20][21][22]. It has also been accepted that among these methods, nonlinear soft sensors such as neural networks [23,24], Gaussian process regression (GPR) [25] and support vector regression (SVR) [26][27][28][29] are more attractive mainly because of the nonlinear relation existing between the response variable of process and operating conditions. In fact, these techniques can relatively easily develop without deep understanding of the process mechanism [29].…”

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

“…Jiang et al (2012) developed a new MI prediction method by introducing relevance vector machine (RVM). Zhang and Liu (2016) presented a real-time soft sensor based on optimized least squares support vector machine (LSSVM) and the online correcting strategy (OCS). In spite of improvements in MI forecasting approaches, MI forecast even suffers from high errors.…”

Quality prediction via semisupervised Bayesian regression with application to propylene polymerization

“…Recently, it has attracted numerous researchers to focus on quality prediction for MI estimation in industrial polymerization processes . To describe the dynamics of Ziegler‐Natta ethylene polymerization, Embiruçu and Marcelo presented a mathematical model to infer the MI value .…”

“…polypropylene processes, it is a challenging problem how to develop an effective quality prediction model by estimating MI with satisfying accuracy and efficiency.Recently, it has attracted numerous researchers to focus on quality prediction for MI estimation in industrial polymerization processes. [8][9][10][11][12][13] To describe the dynamics of Ziegler-Natta ethylene polymerization, Embiruçu and Marcelo presented a mathematical model to infer the MI value. 14 To overcome the coupling of high dimensional variables, multivariable techniques such as principal component analysis, independent component analysis, and partial least squares techniques were used to figure out relationships between latent vectors and quality variables of the process.…”