Abstract:

It is very important for ethylene pyrolysis process to obtain product yields on line.To address the problem with few valid sampling data, soft-sensor models of several kinds of product yields were developed based on support vector regression (SVR).Particle swam optimization (PSO) algorithm was used to determine the proper parameters of SVR model, and model efficiency and performance were then improved.SVR based product yield models got high accuracy and good trend tracking performance on the real industrial data.