Machine learning assisted prediction of the nitric oxide (NO) solubility in various deep eutectic solvents

Abstract

Deep eutectic solvents (DESs) are recently proposed as green materials to remove nitric oxide (NO) from released streams into the atmosphere. The mathematical aspect of this process attracted less attention than it deserved. A straightforward approach in this field will help engineer DES chemistry and optimize the equilibrium conditions to maximize the amount of removed NO. This study covers this gap by constructing a reliable artificial neural network (ANN) to correlate the NO removal capacity of DES with equilibrium pressure/temperature and solvent chemistry. So, firstly, the physical meaningful features are selected to make the DES chemistry quantitative. It was found that the density is the best representative for the hydrogen-bound acceptor and hydrogen-bound donor. Also, the density and viscosity of the DESs exhibit the highest correlation with the NO solubility. Then, the hyperparameters of three famous ANN types (feedforward, recurrent, and cascade) are determined by combining trial-and-error and sensitivity analyzes. Finally, the ranking test distinguishes the ANN type with the lowest uncertainty toward estimating NO dissolution in DESs. The cascade neural network (CNN) with twelve and one neurons in the hidden and output layers equipped with the tangent hyperbolic and radial basis transfer functions is identified as the best ANN type for the given purpose. This model predicts 292 DES-NO equilibrium records collected from the literature with mean absolute errors = 0.033, relative absolute errors = 1.49 %, mean squared errors = 0.002, and coefficient of determination = 0.9998. Also, the present study helps understand the role of DES chemistry and operating conditions on the amount of removable NO by DESs. 1,3-dimethylthioureaP4444Cl (3:1) is recognized as the best DES to separate NO molecules from gaseous streams, respectively. The simulation results show that the unit mass of the best DES is capable of absorbing up to ∼27 mol of NO.