Effect of operating variables on isopropanol absorbed by triethylene glycol solution and discussions on the mass transfer coefficient predicted by artificial neural network.

AbstractThe mass transfer performance of isopropanol (IPA) absorption by triethylene glycol (TEG) aqueous solution in a spray-bed absorber was investigated, and the mass transfer coefficients predicted by an artificial neural network (ANN) and response surface methodology were compared in this study. The operating variables affecting the mass transfer performance of a spray-bed absorber include the gas and TEG fluxes, concentration of IPA, concentration of TEG aqueous solution, and operating temperature. The experimental results demonstrated that the mass transfer coefficient of a spray-bed absorber increased with increases in gas and liquid fluxes and decreased with increases in concentration of IPA. The Back-Propagation algorithm in the ANN model was selected to predict the mass transfer coefficients of the spray–bed absorption system. Hidden layers with 2, 4 and 6 neurons respectively were set to analyze the effect of the number of neurons on the target value, and then different transfer functions were used to compare the accuracy in predicting mass transfer coefficient. Finally, the influences of the number of hidden layers and different learning rules on the prediction of mass transfer coefficients were examined. The simulated results showed that the average error of the ANN using the TanhAxon transfer function, the Conjugate Gradient learning rule, two hidden layers, and 6 neurons in each hidden layer was within 1%. The results not only verified the successful establishment of the experimental device but also proved the accurate prediction of mass transfer coefficients by the ANN model. [ABSTRACT FROM AUTHOR]