Study On Catalytic Oxidation Of Spent Alkali Liquor With High Sulphide And Phenols Under Room Temperature And Atmospheric Pressure

Spent caustic is a kind of high toxic and alkaline industrial waste water discharged from the petroleum and chemical plant, and it contains high content of COD, large quantity of sulfide and phenol. The aim of this paper is to find an effective and economic method to treat the spent caustic under room temperature and atmospheric pressure.This test chose the mixture of ferrous sulfate (FeSO4), ferric oxide(Fe2O3) and manganese(MnO2) as to be a catalyst to oxidize the spent caustic by air under room temperature and atmospheric pressure, almost all the sulfide(S2-) were oxidized to be thiosulfate(S2O32-), sulfite(SO32-), and sulfate(SO42-), the phenols were oxidized to be carbon dioxide(CO2)and water(H2O) or others'intermediate products, so the concentration of COD was reduced at the mean time. The various conditions which affect removal rates were studied, the experimental showed that the phenols removal was more than 99%, S2- and COD removal reached 99% and 68% respectively. Treatment condition is as follows: reaction temperature of 65℃, PH of 10.5, catalytic oxidation reaction time of 40h, FeSO4,Fe2O3 and MnO2 catalysts of 1.5g/l respectively. The catalysts could be circulated, the result satisfied the request of treatment.A back propagation (BP) artificial neural networks (ANN) model with 4 parameters of reaction temperature, reaction time, the catalysts amount and PH was developed for spent caustic treating prediction based on the theory and method of ANN and the characteristics of spent caustic treating. The paper adopted MATLAB as the computational platform to train the model with the Neural Network toolboxes. By comparing the prediction results of the BP model with the measured data, it is proposed that the result of BP model which was stable after trained simulating the spent caustic treating had high precision, the maximum relative errors between the prediction results of the BP model and the measured data of COD and phenols were 7.3052% and 8.0075% respectively, which were not more than 10%,the errors were acceptable, so the model was satisfied.