Iranian Journal of Chemical Engineering (IJChE)

Abstract In the Bayer process, the reaction of silica in bauxite with caustic soda causes the loss of great amount of NaOH. In this research, the bound-soda losses in Bayer process solid residue (red mud) are predicted using intelligent techniques. This method, based on the application of regression and artificial neural networks (AAN), has been used to predict red mud bound-soda losses in Iran Alumina Company. Multilayer perceptron (MLP), radial basis function (RBF) networks and multiple linear regressions (MLR) were applied. The results of three methodologies were compared for their predictive capabilities in terms of the correlation coefficient (R), mean square error (MSE) and the absolute average deviation (AAD) based on the experimental data set. The optimum MLP network was obtained with structure of two hidden layer including 13 and 15 neurons in each layer respectively. The results showed that the RBF model with 0.117, 5.909 and 0.82 in MSE, AAD and R, respectively, is extremely accurate in prediction as compared with MLP and MLR.

Abstract In this work, the influence of Red Mud in the pyrolysis of general purpose polystyrene (GPPS) has been studied. The catalytic pyrolysis experiments were carried out using an unstirred semi-batch stainless steel 6.28 liter reactor under nitrogen atmosphere (air free) with Red Mud as a catalyst. The reaction effluents (oil and gases) were condensed
in water-cooled condenser. The condensate was weighted to determine the total yield. Gas Chromatography-Mass Spectroscopy (GC-MS) analysis was used to characterize the condensed liquid organic compounds. According to the GC-MS results, styrene, benzene and its derivatives, toluene, naphthalene and a small amount of the other hydrocarbon compounds were identified in condensate liquid. Density, specific gravity, API gravity, kinematics and dynamic viscosity, flash point, fire point, cloud point and pour point of condensate were also measured. Under optimum reaction condition, the yield of pyrolysis reaction was above 90%. The air free reaction condition showed good catalytic pyrolysis of polystyrene with less than 1% coke formation and above 90% selectivity towards formation of aromatic compounds. The specifications of condensate, i.e. density, API gravity, viscosity and flash point observed showed that the liquid
obtained can safely be classed as a kerosene. The specific gravity value was close to the specific gravity value of diesel.