Monte Carlo simulation of sequential structure control of AN-MA-IA aqueous copolymerization by different operation modes

doi:10.1016/j.cjche.2021.06.022

Abstract

Abstract: The regulation of polyacrylonitrile (PAN) copolymer composition and sequence structure is the precondition for producing high-quality carbon fiber high quality. In this work, the sequential structure control of acrylonitrile (AN), methyl acrylate (MA) and itaconic acid (IA) aqueous copolymerization was investigated by Monte Carlo (MC) simulation. The parameters used in Monte Carlo were optimized via machine learning (ML) and genetic algorithms (GA) using the experimental data from batch copolymerization. The results reveal that it is difficult to control the aqueous copolymerization to obtain PAN copolymer with uniform sequence structure by batch polymerization with one-time feeding. By contrary, it is found that the PAN copolymer with uniform composition and sequence structure can be obtained by adjusting IA feeding quantity in each reactor of a train of five CSTRs. Hopefully, the results obtained in this work can provide valuable information for the understanding and optimization of AN copolymerization process to obtain high-quality PAN copolymer precursor.

Key words: Polyacrylonitrile, Monte Carlo simulation, Machine learning, Genetic algorithms, Sequence structure, Operation method

摘要： The regulation of polyacrylonitrile (PAN) copolymer composition and sequence structure is the precondition for producing high-quality carbon fiber high quality. In this work, the sequential structure control of acrylonitrile (AN), methyl acrylate (MA) and itaconic acid (IA) aqueous copolymerization was investigated by Monte Carlo (MC) simulation. The parameters used in Monte Carlo were optimized via machine learning (ML) and genetic algorithms (GA) using the experimental data from batch copolymerization. The results reveal that it is difficult to control the aqueous copolymerization to obtain PAN copolymer with uniform sequence structure by batch polymerization with one-time feeding. By contrary, it is found that the PAN copolymer with uniform composition and sequence structure can be obtained by adjusting IA feeding quantity in each reactor of a train of five CSTRs. Hopefully, the results obtained in this work can provide valuable information for the understanding and optimization of AN copolymerization process to obtain high-quality PAN copolymer precursor.

关键词: Polyacrylonitrile, Monte Carlo simulation, Machine learning, Genetic algorithms, Sequence structure, Operation method

Tong Qin, Zhenhao Xi, Ling Zhao, Weikang Yuan. Monte Carlo simulation of sequential structure control of AN-MA-IA aqueous copolymerization by different operation modes[J]. Chinese Journal of Chemical Engineering, 2022, 46(6): 231-242.

Tong Qin, Zhenhao Xi, Ling Zhao, Weikang Yuan. Monte Carlo simulation of sequential structure control of AN-MA-IA aqueous copolymerization by different operation modes[J]. 中国化学工程学报, 2022, 46(6): 231-242.

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