Modeling the ammonia absorption capacity of choline chloride-based deep eutectic solvents with artificial neural networks

Abstract

The ammonia (NH3) absorption capacity of choline chloride-based deep eutectic solvents (DESs) was deeply investigated in laboratory-scale, but little attention was devoted to its modeling. Therefore, the current work aims to cover this gap by developing an artificial neural network (ANN) to estimate the NH3 removal capacity of DESs. Indeed, the prediction accuracy of six ANN variants (multilayer perceptron, wavelet, general regression, radial basis function, cascade feedforward, and recurrent) has been compared to find the model with the lowest errors. Results justified that the cascade feedforward neural network (CFFNN) with only ten hidden nodes is the highest accurate paradigm to predict NH3 solubility in DES-based media. This model simulates 793 NH3 solubility data in eleven DESs with the average absolute relative deviation (AARD = 4.98%), mean absolute error (MAE = 0.163), relative absolute error (RAE = 7.44%), root mean squared error (MSE = 0.318), and regression coefficient (R2 = 0.995). Additionally, the influence of temperature, pressure, and DES composition (water content, choline chloride molar dosage, and type and molar dosage of hydrogen bond donor) on NH3 solubility is comprehensively analyzed by the CFFNN. Increasing the pressure and HBD molar dosage and decreasing the temperature and choline chloride molar dosage enhance NH3 solubility in DES. Pure simulation findings clarified that the NH3 capture capacity of DESs increases by increasing the water content to a critical value, and after that, DES's water content reduces the ammonia solubility. Since glycerol has the maximum number of hydroxyl groups among the investigated HBDs, the choline chloride + glycerol DES showed the highest overall capacity for absorbing NH3 molecules. The simulation results approved that the unit mass of this DES can absorb 14.6 mol of NH3 at 298.2 K and 573.2 kPa.