Effects of operating parameters of packed columns on the KGav for CO2 absorption by amine solutions using optimization–simulation framework

Abstract The aim of this study is to investigate the effects of key operating parameters of packed absorption columns on the performance of mass transfer considering the volumetric overall mass transfer coefficient ( K G a V ). The effects were studied for CO 2 absorption by using 4-diethylamino-2-butanol (DEAB) and N,N-Diethylethanolamine (DEEA) mixed with monoethanolamine (MEA) as novel amine solutions. In doing so, an optimization–simulation framework was developed based on the two-film theory model, thermodynamic model, multi-layer perceptron neural network (MLPNN), and statistical technique. To predict the CO 2 loading as one of the parameters in input of MLPNN model, the Deshmukh–Mather model, as an electrolyte thermodynamic model, was developed for CO 2  + DEAB + H 2 O and CO 2  + DEEA + MEA + H 2 O systems. The effect of enhancement factor on the K G a V was considered based on the series resistances model including pseudo-first order enhancemnt factor and instantaneous enhancemnt factor. To optimize and rank the key operating factors that simultaneously affect the K G a V , the Taguchi method was used. Statistical indices showed that our model could efficiently predict the experimental data with AARDs of 5.6%, 0.43% and 4.96%, respectively, for K G a V data, CO 2 loading data of DEAB and DEEA + MEA. A significant order of process variables affecting the K G a V values was as CO 2 mole fraction > amine temperature > amine flow rate > gas temperature > packing type > CO 2 loading > amine concentration. Moreover, the sensitivity analysis results showed that by increasing the CO 2 mole fraction in gas feed, gas temperature, and CO 2 loading, the K G a V values decreased, and by increasing the amine concentration, amine flow rate, and amine temperature, the K G a V values increased.