A modeling approach for estimating hydrogen sulfide solubility in fifteen different imidazole-based ionic liquids.

ABSTRACT: Absorption has always been an attractive process for removing hydrogen sulfide (H₂S). Posing unique properties and promising removal capacity, ionic liquids (ILs) are potential media for H₂S capture. Engineering design of such absorption process needs accurate measurements or reliable estimation of the H₂S solubility in ILs. Since experimental measurements are time-consuming and expensive, this study utilizes machine learning methods to monitor H₂S solubility in fifteen various ILs accurately. Six robust machine learning methods, including adaptive neuro-fuzzy inference system, least-squares support vector machine (LS-SVM), radial basis function, cascade, multilayer perceptron, and generalized regression neural networks, are implemented/compared.

SUBMITTER: Abdi J 

PROVIDER: S-EPMC8924225 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature