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 R 2 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.
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