Comparison of Different Dynamic Monte Carlo Methods for the Simulation of Olefin Polymerization

被引:11
|
作者
Brandao, Amanda L. T. [1 ]
Soares, Joao B. P. [2 ]
Pinto, Jose C. [1 ]
Alberton, Andre L. [3 ]
机构
[1] Univ Fed Rio de Janeiro, Programa Engn Quim COPPE, CP 68502, BR-21941972 Rio De Janeiro, RJ, Brazil
[2] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2V4, Canada
[3] Univ Estado Rio de Janeiro, Inst Quim, BR-20550900 Rio De Janeiro, RJ, Brazil
关键词
Monte Carlo methods; polymer reaction engineering; polymerization modeling and simulation; CHEMICALLY REACTING SYSTEMS; EXACT STOCHASTIC SIMULATION; MOLECULAR-WEIGHT; COPOLYMERIZATION; DISTRIBUTIONS; POLYOLEFINS; EVOLUTION;
D O I
10.1002/masy.201500111
中图分类号
O63 [高分子化学(高聚物)];
学科分类号
070305 ; 080501 ; 081704 ;
摘要
In this work, Monte Carlo methods were used to simulate olefin polymerization with coordination catalysts: the Direct method (DM), the First Reaction method (FRM), the Next Reaction method (NRM), and the t-Leaping method. The first three methods are exact stochastic simulation algorithms (SSA), while the t-Leaping is an approximate method with faster computation times. It is shown that all four methods predict similar polymer microstructures, but require significantly different computation times. The t-Leaping method is the fastest, being recommended when complex polymerization mechanisms are being investigated. The NRM, because of its intelligent data storage and handling approach, is the best among the SSA.
引用
收藏
页码:160 / 178
页数:19
相关论文
共 50 条
  • [21] Monte Carlo Methods in the Assessment of New Products: A Comparison of Different Approaches
    Esber, Said
    Baier, Daniel
    CLASSIFICATION AS A TOOL FOR RESEARCH, 2010, : 701 - 708
  • [22] Monte Carlo Simulation in Electron Probe Microanalysis. Comparison of Different Simulation Algorithms
    Francesc Salvat
    Xavier Llovet
    José M. Fernández-Varea
    Josep Sempau
    Microchimica Acta, 2006, 155 : 67 - 74
  • [23] Monte Carlo simulation in electron probe microanalysis.: Comparison of different simulation algorithms
    Salvat, Francesc
    Llovet, Xavier
    Fernandez-Varea, Jose M.
    Sempau, Josep
    MICROCHIMICA ACTA, 2006, 155 (1-2) : 67 - 74
  • [24] Comparison of behaviors of three single-electron dynamic memories with different structures based on Monte Carlo simulation
    Xu, HX
    Li, QG
    Min, YQ
    Li, ZY
    ACTA PHYSICA SINICA, 2004, 53 (05) : 1483 - 1489
  • [25] Monte Carlo Simulation of Propylene Polymerization (Ⅰ) Effects of Impurity on Propylene Polymerization
    罗正鸿
    曹志凯
    苏耀堂
    Chinese Journal of Chemical Engineering, 2006, (02) : 194 - 199
  • [26] A New Approach for Monte Carlo Simulation of RAFT Polymerization
    Ganjeh-Anzabi, Pejman
    Hadadi-Asl, Vahid
    Salami-Kaljahi, Mehdi
    Roghani-Mamaqani, Hossein
    IRANIAN JOURNAL OF CHEMISTRY & CHEMICAL ENGINEERING-INTERNATIONAL ENGLISH EDITION, 2012, 31 (03): : 75 - 84
  • [27] Predicting purchase decisions with different conjoint analysis methods - A Monte Carlo simulation
    Backhaus, Klaus
    Hillig, Thomas
    Wilken, Robert
    INTERNATIONAL JOURNAL OF MARKET RESEARCH, 2007, 49 (03) : 341 - 364
  • [28] Application of Monte Carlo methods for modelling of polymerization reactions
    Platkowski, K
    Reichert, KH
    POLYMER, 1999, 40 (04) : 1057 - 1066
  • [29] CONVENTIONAL PROBABILITY METHODS AND MONTE-CARLO SIMULATION TECHNIQUES - COMPARISON OF RESULTS
    LE, KD
    IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1978, 97 (04): : 1013 - 1013
  • [30] Comparison of Bootstrap Confidence Interval Methods for GSCA Using a Monte Carlo Simulation
    Jung, Kwanghee
    Lee, Jaehoon
    Gupta, Vibhuti
    Cho, Gyeongcheol
    FRONTIERS IN PSYCHOLOGY, 2019, 10