Modeling hydrogen solubility in alcohols using group method of data handling and genetic programming

Abstract

Having accurate information about the solubility of hydrogen (H2) in alcoholic solvents is crucial for the design and implementation of numerous chemical processes. In this communication, two robust correlative techniques, Genetic programming (GP) and Group method of data handling (GMDH) were used to estimate H2 solubility in alcohols. For the mentioned purpose, 673 laboratory data of H2 solubility for 26 distinct alcoholic solvents were collected over a broad interval of operating pressure from 0.101 MPa to 110.3 MPa and temperature from 213.15 K to 524.9 K. These solvents include fatty alcohols, aliphatic alcohols, diols, glycols, and hydroxypolyether with molecular weights ranging from 32.042 to 242.446 g/mol. The algorithms' input parameters were selected to be molecular weight of alcohol, the temperature and pressure of the solubility system, critical temperature and pressure of alcohols. According to the graphical and statistical assessments, the GMDH model was shown to be the best choice for estimating H2 solubility in alcoholic solvents, with a root mean square error of 0.00482 and a coefficient of determination of 0.9841. Furthermore, according to sensitivity analysis, the greatest influence on H2 solubility in alcoholic solvents is dedicated to pressure, temperature, and molecular weight of alcohols. Furthermore, the Leverage technique was used to identify the application domain of the GMDH model and outlier data, with the findings indicating that GMDH has a high credit for estimating H2 dissolution in alcoholic media.