Simulation and Optimization of Styrene Monomer Production Using Neural Network

Due to wide application of styrene for production of different materials, it is considered as an important product in industry. therefore, optimizing styrene production conditions is of great importance in petrochemical industry. in this paper, styrene production reactors of tabriz petrochemical complex are modeled using artificial neural network (ann) model and adaptive neuro fuzzy inference system (anfis). comparison of two models revealed that the neural networks are more reliable. the process of design and evaluation of models are carried out using industrial data which show credibility of designed models. the neural networks are designed to predict the styrene output from reactors as a function of effective input parameters on the styrene production. predictions of designed neural networks were used to study the effect of each variable, such as oxygen flow rate and steam oil ratio, on the amount of styrene produced. also, the optimal values of effective variables for maximum production of styrene were obtained. furthermore, in order to obtain accurate results, catalyst deactivation of styrene reactors has been modeled using fuzzy inference system. as a result, catalyst activity as a function of time is obtained.