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

Prathana Nimmanterdwong; Rachaneeporn Changpun; Patipon Janthboon; Sukanya Nakrak; Hongxia Gao; Zhiwu Liang; Paitoon Tontiwachwuthikul; Teerawat Sema

doi:10.1021/acsomega.1c05169

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

ACS Omega. 2021 Nov 10;6(46):31321-31329. doi: 10.1021/acsomega.1c05169. eCollection 2021 Nov 23.

Authors

Prathana Nimmanterdwong¹, Rachaneeporn Changpun¹, Patipon Janthboon¹, Sukanya Nakrak¹, Hongxia Gao², Zhiwu Liang², Paitoon Tontiwachwuthikul³, Teerawat Sema^{1

4}

Affiliations

¹ Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand.
² Joint International Center for CO2 Capture and Storage (iCCS), Provincial Hunan Key Laboratory for Cost-effective Utilization of Fossil Fuel Aimed at Reducing CO2 Emissions, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
³ Clean Energy Technologies Research Institute (CETRi), Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S0A2, Canada.
⁴ Center of Excellence on Petrochemical and Materials Technology, Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand.

Abstract

Solubility of hydrogen sulfide (H₂S) 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 H₂S solubility. Based on the mean square error (MSE) and determination coefficient (R ²), the most attractive model was the LM-ANN model with 17 hidden neurons. As a result, very satisfactory prediction performance (for the testing data set) with an MSE of 0.0014 and an R ² of 0.9817 was obtained from the developed LM-ANN model. Additionally, a parity chart confirmed that the predicted solubility of H₂S well aligned with the experimental data. To effectively absorb H₂S and maintain high solubility of H₂S, the absorbent should be well complied with the operating pressure. For a low-pressure range of less than 100 kPa, amines are very attractive. As the pressure elevated to 100-1000 kPa, amines and hybrid amine + physical absorbents are suggested. Lastly, at a high pressure over 1000 kPa, physical absorbents and ionic liquids are recommended.