Iranian Journal of Chemical Engineering (IJChE)

Abstract In this study, in-situ bulk polymerization was investigated for obtaining flame retardant polystyrene (PS). The halogenated and phosphoric compounds were used as flame retardant additives and Perkadox 30 was used as a synergist. The flammability of the PS was evaluated by thermogravimetric analyzer (TGA), limiting oxygen index (LOI) and UL-94 tests. The results show that polymerization process for production of flame retardant polystyrene needs lower amount of flame retardant additives compare with the process for production of flame retardant composites. Furthermore, using Perkadox 30 as a synergist lowers the loading of flame retardant additives. LOI tests show that flame retardant polystyrene synthesized by adding at least 0.35 % (w/w) hexabromocyclododecane (HBCD) during polymerization. TGA analysis confirms that with addition of HBCD the degradation temperature decreases and weight loss occurs quickly. The degradation tempresure of the sample consist of 0.8 % (w/w) HBCD was lower than the sample consist of 0.35 % (w/w) HBCD and 0.45 % (w/w) triphenylphosphate (TPP). The pure polystyrene didn’t pass the UL-94 test because of inflammability and greater dripping. For samples with HBCD, shorter time needed to quench the flame and these samples passed the UL-94 test. On the other hand, greater dripping of polymer melt led to transmission of UL-94 rate from V0 to V2. It is also observed that flaming rate for samples with TPP was very low and dripping didn’t occur.

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.