Analysis of initial conditions for polymerization reaction using fuzzy neural network and genetic algorithm

Taizo Hanai, Toshihiko Ohki, Hiroyuki Honda, Takeshi Kobayashi

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

In order to determine initial conditions for preparation of polybutadiene with given physicochemical characteristics, a fuzzy neural network (FNN) model was constructed to estimate the physicochemical characteristics of the polymer (the ratio of cis form polymer and the polydispersity index (PDI) and the conversion ratio from the initial conditions in the batch polymerization process. The mean absolute errors of the FNN model for the conversion ratio, the ratio of cis form polymer and PDI as the actual scale were 7.13, 0.23 and 0.17%, respectively. Analyzing for the constructed FNN model, the relationships between the process conditions and physicochemical characteristics were obtained as IF-THEN rules. Using the constructed FNN model and a genetic algorithm (GA) combined with reliability index (RI), the process conditions with the given physicochemical characteristics and conversion ratio were calculated. The calculated and actual process conditions showed an average relative error of 3.9%.

Original languageEnglish
Pages (from-to)1011-1019
Number of pages9
JournalComputers and Chemical Engineering
Volume27
Issue number7
DOIs
Publication statusPublished - Jul 15 2003

Fingerprint

Fuzzy neural networks
Genetic algorithms
Polymerization
Polymers
Polydispersity
Polybutadienes

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Cite this

Analysis of initial conditions for polymerization reaction using fuzzy neural network and genetic algorithm. / Hanai, Taizo; Ohki, Toshihiko; Honda, Hiroyuki; Kobayashi, Takeshi.

In: Computers and Chemical Engineering, Vol. 27, No. 7, 15.07.2003, p. 1011-1019.

Research output: Contribution to journalArticle

Hanai, Taizo ; Ohki, Toshihiko ; Honda, Hiroyuki ; Kobayashi, Takeshi. / Analysis of initial conditions for polymerization reaction using fuzzy neural network and genetic algorithm. In: Computers and Chemical Engineering. 2003 ; Vol. 27, No. 7. pp. 1011-1019.
@article{9609c97a0a064fcbb1e7ebd7214a9699,
title = "Analysis of initial conditions for polymerization reaction using fuzzy neural network and genetic algorithm",
abstract = "In order to determine initial conditions for preparation of polybutadiene with given physicochemical characteristics, a fuzzy neural network (FNN) model was constructed to estimate the physicochemical characteristics of the polymer (the ratio of cis form polymer and the polydispersity index (PDI) and the conversion ratio from the initial conditions in the batch polymerization process. The mean absolute errors of the FNN model for the conversion ratio, the ratio of cis form polymer and PDI as the actual scale were 7.13, 0.23 and 0.17{\%}, respectively. Analyzing for the constructed FNN model, the relationships between the process conditions and physicochemical characteristics were obtained as IF-THEN rules. Using the constructed FNN model and a genetic algorithm (GA) combined with reliability index (RI), the process conditions with the given physicochemical characteristics and conversion ratio were calculated. The calculated and actual process conditions showed an average relative error of 3.9{\%}.",
author = "Taizo Hanai and Toshihiko Ohki and Hiroyuki Honda and Takeshi Kobayashi",
year = "2003",
month = "7",
day = "15",
doi = "10.1016/S0098-1354(03)00034-6",
language = "English",
volume = "27",
pages = "1011--1019",
journal = "Computers and Chemical Engineering",
issn = "0098-1354",
publisher = "Elsevier BV",
number = "7",

}

TY - JOUR

T1 - Analysis of initial conditions for polymerization reaction using fuzzy neural network and genetic algorithm

AU - Hanai, Taizo

AU - Ohki, Toshihiko

AU - Honda, Hiroyuki

AU - Kobayashi, Takeshi

PY - 2003/7/15

Y1 - 2003/7/15

N2 - In order to determine initial conditions for preparation of polybutadiene with given physicochemical characteristics, a fuzzy neural network (FNN) model was constructed to estimate the physicochemical characteristics of the polymer (the ratio of cis form polymer and the polydispersity index (PDI) and the conversion ratio from the initial conditions in the batch polymerization process. The mean absolute errors of the FNN model for the conversion ratio, the ratio of cis form polymer and PDI as the actual scale were 7.13, 0.23 and 0.17%, respectively. Analyzing for the constructed FNN model, the relationships between the process conditions and physicochemical characteristics were obtained as IF-THEN rules. Using the constructed FNN model and a genetic algorithm (GA) combined with reliability index (RI), the process conditions with the given physicochemical characteristics and conversion ratio were calculated. The calculated and actual process conditions showed an average relative error of 3.9%.

AB - In order to determine initial conditions for preparation of polybutadiene with given physicochemical characteristics, a fuzzy neural network (FNN) model was constructed to estimate the physicochemical characteristics of the polymer (the ratio of cis form polymer and the polydispersity index (PDI) and the conversion ratio from the initial conditions in the batch polymerization process. The mean absolute errors of the FNN model for the conversion ratio, the ratio of cis form polymer and PDI as the actual scale were 7.13, 0.23 and 0.17%, respectively. Analyzing for the constructed FNN model, the relationships between the process conditions and physicochemical characteristics were obtained as IF-THEN rules. Using the constructed FNN model and a genetic algorithm (GA) combined with reliability index (RI), the process conditions with the given physicochemical characteristics and conversion ratio were calculated. The calculated and actual process conditions showed an average relative error of 3.9%.

UR - http://www.scopus.com/inward/record.url?scp=0038294678&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0038294678&partnerID=8YFLogxK

U2 - 10.1016/S0098-1354(03)00034-6

DO - 10.1016/S0098-1354(03)00034-6

M3 - Article

VL - 27

SP - 1011

EP - 1019

JO - Computers and Chemical Engineering

JF - Computers and Chemical Engineering

SN - 0098-1354

IS - 7

ER -