LAUSR

In this study, phenol production process was simulated. Further, the performance of distillation column was optimized through maximizing the mole fraction of cumene in upstream flow. Response surface methodology was applied for design of experiment, modelling, and optimizing the cumene mole fraction in upstream flow of separation column. The analysis of variance was performed for finding the important operative parameters as well as their effects. In this experiment, the effects of three parameters on separation performance were investigated, including number of tray ( A ), column temperature ( B ), and reflux ratio ( C ). Further, radial basis function (RBF) was applied to model the separation column. To develop the neural network model, leave-one-out method was used. This robust model was used for optimizing the performance of separation column. The statistical and artificial intelligence system were capable of predicting mole fraction in upstream flow of distillation column in different conditions with R 2 of 0.99 and 0.93, respectively. According to statistical and RBF models, the optimized values of cumene mole fraction are 0.45 and 0.44, respectively.