Iranian Journal of Chemical Engineering (IJChE)

Abstract 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.

Abstract In this paper, a heterogeneous one-dimensional model for simulation of cascade methanol reactor has been developed, while the various percent of CO was injected to these reactors. In the cascade reactor configuration, a system with two-catalyst bed instead of one single catalyst bed is applied for methanol synthesis. In the first catalyst bed, the synthesis gas is partly converted to methanol in a water-cooled reactor. In the second bed, which is a gas-cooled reactor, the reaction heat is used to preheat the feed gas to the first bed. In this study, the various percent CO injections of feed entrance (1% and 2%) to different reactors (first and second reactor) of conventional dual type methanol reactor have been considered. The simulation results represent 5.127% and 4.158% enhancement in the yield of methanol production in comparison with conventional dual type methanol reactor (CDMR) and membrane dual type methanol reactor (MDMR) respectively, while 2% CO was injected to the first reactor. Also, by CO injection to the feed, water produced during methanol synthesis via CO2 hydrogenation which accelerates the catalyst deactivation and reduces methanol
production rate, is reduced greatly.