Using Artificial Intelligence Techniques to Design Ethylene/1-Olefin Copolymers

被引：5
|
作者：
Charoenpanich, Thanutchoke [1 ]
Anantawaraskul, Siripon [1 ,2 ]
Soares, Joao B. P. [3 ]
机构：
[1] Kasetsart Univ, Fac Engn, Ctr Excellence Petrochem & Mat Technol, Dept Chem Engn, Bangkok 10900, Thailand
[2] Kasetsart Univ, Ctr Adv Studies Nanotechnol & Its Applicat Chem, Food & Agr Ind, Bangkok 10900, Thailand
[3] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 1H9, Canada
来源：
MACROMOLECULAR THEORY AND SIMULATIONS | 2020年 / 29卷 / 06期
关键词：
artificial intelligence; global optimization; kinetics (polym; polyethylene; simulations; MELT INDEX PREDICTION; ANT COLONY OPTIMIZATION; METALLOCENE CATALYSTS; POLYMERIZATION; POLYMERS; MODEL;
D O I：
10.1002/mats.202000048
中图分类号：
O63 [高分子化学（高聚物）];
学科分类号：
070305 ; 080501 ; 081704 ;
摘要：
Four global optimization techniques, genetic algorithm, particle swarm, improved ant colony, and modified artificial bee colony, are compared to find alternative polymerization conditions to make ethylene/1-olefin copolymers with targeted microstructures and polymerization yields. The polymer microstructure targets are divided in three groups: 1) molecular weight distribution, chemical composition distribution, and polymer yield; 2) number and weight average molecular weights, average comonomer content, and polymer yield; and 3) molecular weight distribution, short chain branching distribution, and polymer yield. The modified artificial bee colony optimization generated the fewest number of incorrect solutions, while the polymer microstructure target group 1 generated the most successful solutions.
引用
收藏
页数：28
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