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首页> 外文期刊>Journal of Macromolecular Science. Pure and Applied Chemistry >Neural networks and genetic algorithms used for modeling and optimization of the siloxane-siloxane copolymers synthesis
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Neural networks and genetic algorithms used for modeling and optimization of the siloxane-siloxane copolymers synthesis

机译：神经网络和遗传算法，用于建模和优化硅氧烷-硅氧烷共聚物的合成

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摘要

This paper presents the use of neural networks and genetic algorithms as tools for modeling and optimization applied to a complex polymerization process-synthesis of statistical dimethyl-methylvinylsiloxane copolymers. A feed forward neural network models the dependence between the conversion of monomers and copolymer composition (output variables) and working conditions (temperature, reaction time, amount of catalyst and initial composition of monomers-input variables). The training and validation data sets are gathered by ring-opening copolymerization of the octamethylcyclotetrasiloxane (D-4) with 1,3,5,7-tetravinyl-1,3,5,7- tetramethylcyclotetrasiloxane (D-4(V)), with a cation exchange (styrene-divinylbenzene copolymer containing sulfonic groups) as a catalyst, in the absence of solvent. This model is included into an optimization procedure based on a scalar objective function and solved with a simple genetic algorithm. The genetic algorithm computes the optimal values for the control variables and for the weight coefficients attached to the individual objectives. An inverse neural network modeling, that is the identification of reaction conditions leading to a desired value for copolymer composition, is presented as particular variant of optimization. The genetic algorithm and neural networks prove to be good and accessible tools for solving an optimization problem performed with a multi-objective scalar function and provide important information for the experimental practice.
机译：本文介绍了神经网络和遗传算法作为建模和优化工具的应用，这些工​​具可应用于统计二甲基-甲基乙烯基硅氧烷共聚物的复杂聚合过程合成。前馈神经网络对单体和共聚物组成的转化率（输出变量）与工作条件（温度，反应时间，催化剂用量和单体的初始组成-输入变量）之间的相关性进行建模。通过八甲基环四硅氧烷（D-4）与1,3,5,7-四乙烯基-1,3,5,7-四甲基环四硅氧烷（D-4（V））的开环共聚来收集训练和验证数据集，在没有溶剂的情况下用阳离子交换剂（含有磺酸基的苯乙烯-二乙烯基苯共聚物）作为催化剂。该模型包含在基于标量目标函数的优化过程中，并通过简单的遗传算法求解。遗传算法为控制变量和附加到各个目标的权重系数计算最佳值。作为优化的特定变体，提出了逆神经网络建模，即确定导致共聚物组成具有所需值的反应条件。遗传算法和神经网络被证明是解决使用多目标标量函数执行的优化问题的好工具，并且可以为实验实践提供重要信息。

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