Experimental investigations and developing multilayer neural network models for prediction of CO2 solubility in aqueous MDEA/PZ and MEA/MDEA/PZ blends

Abstract

AbstractIn this research, a new set of experimental data for CO2 solubility in aqueous blended amine solvents were investigated experimentally over the CO2 partial pressure range from 8 to 100 kPa at 40 °C and were compared with the benchmark aqueous 30 wt.% MEA solution. This work developed two multilayer neural network models named models A and B, for predicting the CO2 solubility in various aqueous blended amine solvents including 36 wt.% MDEA + 17 wt.% PZ, 24 wt.% MDEA + 26 wt.% PZ, and 6 wt.% MEA + 25 wt.% MDEA + 17 wt.% PZ. Models A and B were developed by using Levenberg–Marquardt back propagation algorithm with 427 and 301 of reliable experimental data sets gathered from the published data, respectively. The results indicate that the high accuracy prediction of the CO2 solubility in Methyldiethanolamine/Piperazine (MDEA/PZ) blends could be obtained by the network developed by Tan‐sigmoid transfer function with two hidden layers consist of eight and four neurons, while the network developed by Tan‐sigmoid transfer function with three hidden layers consist of 20, 10, and five neurons provided the highest accuracy for predicting the CO2 solubility in MEA/MDEA/PZ blends comparing to other model structures. The comparison results show that the neural network modeling provided more closer predictions to the experimental results than the simulator and other thermodynamic models when predicting the CO2 equilibrium solubility in blended amine solvents. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd.