Comprehensive mass transfer analysis of CO absorption in high potential ternary AMP-PZ-MEA solvent using three-level factorial design.

Mass transfer of CO absorption in 2-amino-2-methyl-1-propanol (AMP) - piperazine (PZ) - monoethanolamine (MEA) was statistically investigated in terms of overall mass transfer coefficient ([Formula: see text]) and CO removal percentage. The parameters of interest were lean solvent flux (A), rich gas flux (B), CO loading in the lean solvent (C), and ratio of the sampling height to the total column height [Formula: see text] (D). From ANOVA, A was the most impactable parameter on both responses with three-quarters of the overall contribution.

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification.

Solubility of hydrogen sulfide (HS) in 46 single and blended physical absorbents, amines, ionic liquids, and hybrid absorbents of amines + ionic liquids and amines + physical absorbents was successfully predicted based on artificial neural networks (ANNs). Three neural network algorithms of Levenberg-Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) were applied for architecting the ANN models. The results showed that both the number of hidden neurons and the prediction algorithm affected the prediction of HS solubility.