Reaction kinetics determination and neural networks modeling of methanol dehydration over nano γ-Al2O3 catalyst

In this research nano γ-Al2O3 catalyst was synthesized through precipitation process then characterized and utilized for methanol dehydration reaction in a slurry batch reactor in route to the indirect synthesis of the dimethyl ether (DME). In this venue, effects of the key parameters on methanol conversion and catalyst stability were investigated. Moreover, the internal and external mass transfer resistances were eliminated; hence the intrinsic kinetics controlled the reaction. Therefore, the optimum conditions for temperature, methanol concentration, catalyst mass and stirrer speed were determined to be 300 °C, 1.18 mol/l, 1.5 g and 1100 rpm, respectively. Next, different reaction rate equations from literature were applied to the measured experimental data where their generality compared to a new reaction rate equation examined. Ultimately, artificial neural networks applied to determine a model for the reaction rate estimation. It has been shown that the proposed reaction rate equation might be used rather satisfactorily to provide a base model for the neural networks; consequently a very good proximity to the reaction dynamics resulted.