New Smart Models for Minimum Fluidization Velocity Forecasting in the Tapered Fluidized Beds Based on Particle Size Distribution

Intelligent modeling of hydrogen sulfide solubility in various types of single and multicomponent solvents

This study aims to study the solubility of acid gas, i.e., hydrogen sulfide (H₂S) in different solvents. Three intelligent approaches, including Multilayer Perceptron (MLP), Gaussian Process Regression (GPR) and Radial Basis Function (RBF) were used to construct reliable models based on an extensive databank comprising 5148 measured samples from 54 published sources. The analyzed data cover 95 single and multicomponent solvents such as amines, ionic liquids, electrolytes, organics, etc., in broad pressure and temperature ranges. The proposed models require just three simple input variables, i.e., pressure, temperature and the equivalent molecular weight of solvent to determine the solubility. A competitive examination of the novel models implied that the GPR-based one gives the most appropriate estimations with excellent AARE, R² and RRMSE values of 4.73%, 99.75% and 4.83%, respectively for the tested data. The mentioned intelligent model also performed well in describing the physical behaviors of H₂S solubility at various operating conditions. Furthermore, analyzing the William's plot for the GPR-based model affirmed the high reliability of the analyzed databank, as the outlying data points comprise just 2.04% of entire data. In contrast to the literature models, the newly presented approaches proved to be applicable for different types of single and multicomponent H₂S absorbers with AAREs less than 7%. Eventually, a sensitivity analysis based on the GPR model reflected the fact that the solvent equivalent molecular weight is the most influential factor in controlling H₂S solubility.