Artificial neural network prediction of transport properties of novel MPDL-based solvents for post combustion carbon capture
Novel N-methyl-4-piperidinol (MPDL)-based solvents have been considered as high potential solvents for post combustion carbon capture, especially for power generation industry. To comprehensively investigate the CO2 absorption-regeneration performance of MPDL-based solvents, transport properties (i....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | Energy Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S235248472200364X |
| _version_ | 1811268137960603648 |
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| author | Prathana Nimmanterdwong Patipon Janthboon Paitoon Tontiwachwuthikul Hongxia Gao Zhiwu Liang Teerawat Sema |
| author_facet | Prathana Nimmanterdwong Patipon Janthboon Paitoon Tontiwachwuthikul Hongxia Gao Zhiwu Liang Teerawat Sema |
| author_sort | Prathana Nimmanterdwong |
| collection | DOAJ |
| description | Novel N-methyl-4-piperidinol (MPDL)-based solvents have been considered as high potential solvents for post combustion carbon capture, especially for power generation industry. To comprehensively investigate the CO2 absorption-regeneration performance of MPDL-based solvents, transport properties (i.e., density, viscosity, and physical CO2 diffusivity) are required. These data are reported in the literature and can be estimated by conventional predictive correlations. However, the conventional correlation is applicable for an individual solvent at various blended ratios and temperatures. Thus, artificial neural network (ANN) was then applied for prediction of the transport properties of MPDL-based solvents, including aqueous solutions of MPDL, MPDL-monoethanolamine (MEA), MPDL-2-amino-2-methyl-1-propanol (AMP), and MPDL-piperazine (PZ). Three learning algorithms of (i) Levenberg–Marquardt (LM), (ii) Bayesian Regularization (BR), and (iii) Scaled Conjugate Gradient (SCG) were applied to develop the predictive ANN models with various hidden neurons. As a result, 6 hidden neurons BR-ANN model was the most convincible single prediction platform for the three transport properties. The develop model can be very beneficial for further applications associated with the novel MPDL-based solvents. |
| first_indexed | 2024-04-12T21:16:22Z |
| format | Article |
| id | doaj.art-bd53116a7ae64ab8b38dc1b4f5cf0e44 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-12T21:16:22Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-bd53116a7ae64ab8b38dc1b4f5cf0e442022-12-22T03:16:26ZengElsevierEnergy Reports2352-48472022-08-0188894Artificial neural network prediction of transport properties of novel MPDL-based solvents for post combustion carbon capturePrathana Nimmanterdwong0Patipon Janthboon1Paitoon Tontiwachwuthikul2Hongxia Gao3Zhiwu Liang4Teerawat Sema5Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok, 10330, ThailandDepartment of Chemical Technology, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok, 10330, ThailandClean Energy Technologies Research Institute, Faculty of Engineering and Applied Science, University of Regina, SK, S4S 0A2, CanadaJoint 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 ChinaJoint 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 ChinaDepartment of Chemical Technology, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok, 10330, Thailand; Center of Excellence on Petrochemical and Materials Technology, Chulalongkorn University, Pathumwan, Bangkok, 10330, Thailand; Corresponding author at: Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Pathumwan, Bangkok, 10330, Thailand.Novel N-methyl-4-piperidinol (MPDL)-based solvents have been considered as high potential solvents for post combustion carbon capture, especially for power generation industry. To comprehensively investigate the CO2 absorption-regeneration performance of MPDL-based solvents, transport properties (i.e., density, viscosity, and physical CO2 diffusivity) are required. These data are reported in the literature and can be estimated by conventional predictive correlations. However, the conventional correlation is applicable for an individual solvent at various blended ratios and temperatures. Thus, artificial neural network (ANN) was then applied for prediction of the transport properties of MPDL-based solvents, including aqueous solutions of MPDL, MPDL-monoethanolamine (MEA), MPDL-2-amino-2-methyl-1-propanol (AMP), and MPDL-piperazine (PZ). Three learning algorithms of (i) Levenberg–Marquardt (LM), (ii) Bayesian Regularization (BR), and (iii) Scaled Conjugate Gradient (SCG) were applied to develop the predictive ANN models with various hidden neurons. As a result, 6 hidden neurons BR-ANN model was the most convincible single prediction platform for the three transport properties. The develop model can be very beneficial for further applications associated with the novel MPDL-based solvents.http://www.sciencedirect.com/science/article/pii/S235248472200364XNeural networkPredictionCO2Carbon capture |
| spellingShingle | Prathana Nimmanterdwong Patipon Janthboon Paitoon Tontiwachwuthikul Hongxia Gao Zhiwu Liang Teerawat Sema Artificial neural network prediction of transport properties of novel MPDL-based solvents for post combustion carbon capture Energy Reports Neural network Prediction CO2 Carbon capture |
| title | Artificial neural network prediction of transport properties of novel MPDL-based solvents for post combustion carbon capture |
| title_full | Artificial neural network prediction of transport properties of novel MPDL-based solvents for post combustion carbon capture |
| title_fullStr | Artificial neural network prediction of transport properties of novel MPDL-based solvents for post combustion carbon capture |
| title_full_unstemmed | Artificial neural network prediction of transport properties of novel MPDL-based solvents for post combustion carbon capture |
| title_short | Artificial neural network prediction of transport properties of novel MPDL-based solvents for post combustion carbon capture |
| title_sort | artificial neural network prediction of transport properties of novel mpdl based solvents for post combustion carbon capture |
| topic | Neural network Prediction CO2 Carbon capture |
| url | http://www.sciencedirect.com/science/article/pii/S235248472200364X |
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