### 抄録

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%.

元の言語 | 英語 |
---|---|

ページ（範囲） | 1011-1019 |

ページ数 | 9 |

ジャーナル | Computers and Chemical Engineering |

巻 | 27 |

発行部数 | 7 |

DOI | |

出版物ステータス | 出版済み - 7 15 2003 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Chemical Engineering(all)
- Computer Science Applications

### これを引用

*Computers and Chemical Engineering*,

*27*(7), 1011-1019. https://doi.org/10.1016/S0098-1354(03)00034-6

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

研究成果: ジャーナルへの寄稿 › 記事

*Computers and Chemical Engineering*, 巻. 27, 番号 7, pp. 1011-1019. https://doi.org/10.1016/S0098-1354(03)00034-6

}

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 -