The purpose of this research is to find the optimal operating point in the production process of the cumene. Therefore, the production process was optimized through statistical and genetic algorithm-based methods. The performance of an alkylation reactor was optimized through maximizing the yield of cumene production. Response surface methodology (RSM) with design type of central composite was applied for design of experiment, modelling, and optimizing the process. The analysis of variance (ANOVA) was performed for finding the important operative parameters as well as their effects. The effects of three parameters including temperature, reactor length, and pressure on the alkylation process were investigated. Further, two types of feed-forward neural network were applied to model the alkylation reactor. To develop the neural network model, leave-one-out method was used. The best prediction performance belonged to a fitting network with 2 and 8 neurons in the hidden layer, respectively. This model was used for optimizing the performance of the alkylation reactor. The statistical and artificial intelligence systems were capable of prediction of cumene production yield in different conditions with R² of 0.9098 and 0.9986, respectively. Genetic algorithm-based optimization was performed by the developed neural network model. The maximum accessible value of cumene production yield was 0.7771, which can be achieved when the temperature, length of reactor, and column pressure are 160°C, 2 m, and 4000 kPa, respectively. By finding the optimal operating point in the cumene production process, capital cost, energy consumption, and other operating costs can be significantly reduced.

中文翻译：

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通过统计和人工智能方法的混合模型对烷基化过程进行基于遗传的多目标优化

本研究的目的是寻找异丙苯生产过程中的最佳操作点。因此，通过基于统计和遗传算法的方法优化了生产过程。烷基化反应器的性能通过最大化异丙苯生产的产率进行了优化。设计类型为中心复合材料的响应面方法 (RSM) 被应用于实验设计、建模和优化过程。进行方差分析 (ANOVA) 以找出重要的操作参数及其影响。研究了温度、反应器长度和压力三个参数对烷基化过程的影响。此外，应用两种类型的前馈神经网络来模拟烷基化反应器。为了开发神经网络模型，使用了留一法。最好的预测性能属于分别在隐藏层具有 2 个和 8 个神经元的拟合网络。该模型用于优化烷基化反应器的性能。统计和人工智能系统能够用 R 预测不同条件下的异丙苯产量²分别为 0.9098 和 0.9986。基于遗传算法的优化由开发的神经网络模型执行。在温度、反应器长度和塔压分别为 160℃、2 m 和 4000 kPa 时，异丙苯产率的最大可达值为 0.7771。通过在异丙苯生产过程中找到最佳操作点，可以显着降低资本成本、能源消耗和其他操作成本。