Automated determination of optimal component design for a binary solvent for absorption-based acid gas removal

Abstract. Natural gases containing impurities, namely carbon dioxide (CO2), heavy hydrocarbons, hydrogen sulfide (H2S), and water vapour, need treatment for removing acidic (CO2 H2S) to reduce corrosion enhance the heat capacity of gas. This gas is commercially known as "sour", typically, sour any that contains significant levels sulfide. The presence can affect natural quality, which also lead CO2 freezing issues; hence reliable techniques reducing H2S from necessary. New blends amines show uptake comparable traditional MEA benchmark solutions. work aimed create different regression models using open-source software estimate best fit model a given amine solvent. For this purpose, data were obtained simulation Aspen HYSYS V12.1 MDEA (40-45 wt.%), +PZ (42-50wt.% + 0-2.5wt.%), DEA (21-26wt.%). Regression solvent developed validated. study showed XGB was suited solution, while PZ multiple linear regression. generated ASPEN created in python correlating simulation-generated values with results. These low MSE, RMSE high R2 tried solvents.