Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

1.60

CiteScore

Zhi Fan  1 and Jie Cao1

1Lanzhou University of Technology, Lanzhou, Gansu, 730050, P.R. China


 

Received: April 12, 2016
Accepted: August 28, 2016
Publication Date: December 1, 2016

Download Citation: ||https://doi.org/10.6180/jase.2016.19.4.02  

ABSTRACT


Accurate and reliable prediction of melt index is crucial in polypropylene production. In order to establish an accurate prediction model, a process state detection method and a novel improved particle swarm optimization algorithm is proposed. A polypropylene product melt index soft sensor model is built based on process state detection and the improved optimization algorithm. According to the research on the data from real plant, the experiments demonstrate that the proposed approach can improve the prediction accuracy, and the soft sensor model meets the requirement of on-line optimal control.


Keywords: System Modeling, Soft Sensor, Melt Index, State Detection, Particle Swarm Optimization


 

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