Mixed MDEA-PZ amine solutions for CO2 capture: Modeling and optimization using RSM and ANN approaches
The process of capturing and reducing carbon dioxide (CO2) emissions through chemical absorption is widely acknowledged as the most effective technique, especially in dealing with natural gas streams or flue gases produced by fossil fuel power plants. In this research, we delve into the modeling and...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Case Studies in Chemical and Environmental Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666016423002141 |
| _version_ | 1827630300891447296 |
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| author | Pedram Zafari Ahad Ghaemi |
| author_facet | Pedram Zafari Ahad Ghaemi |
| author_sort | Pedram Zafari |
| collection | DOAJ |
| description | The process of capturing and reducing carbon dioxide (CO2) emissions through chemical absorption is widely acknowledged as the most effective technique, especially in dealing with natural gas streams or flue gases produced by fossil fuel power plants. In this research, we delve into the modeling and optimization of CO2 mass transfer flux (NCO₂). To accomplish this, employed a combination of Piperazine (PZ) and Methyldiethanolamine (MDEA) amines for CO2 absorption. The approach utilized artificial neural networks (ANN) and response surface methodology (RSM). We used Pi-Buckingham theory to derive dimensionless numbers for the input variables in both ANNs and RSM. The resulting models offer satisfactory outcomes by effectively capturing the influence of independent variables and their interactions on the objective function, thereby optimizing the CO2 capture process. The RSM approach employs a quadratic model. Through optimization, neural networks were fine-tuned to achieve the lowest error and the closest fit to experimental data. Both ANNs and RSM models demonstrated acceptable performance in predicting experimental data, with maximum R2 values of 0.99924 and 0.9663, respectively. Considering the mean squared error of 5.2 × 10−4 obtained from the simulations, the ANN is recommended as the preferred method for developing absorption simulation models. |
| first_indexed | 2024-03-09T14:03:54Z |
| format | Article |
| id | doaj.art-683d586e78dd4f75883b1f8cc679d8c0 |
| institution | Directory Open Access Journal |
| issn | 2666-0164 |
| language | English |
| last_indexed | 2024-03-09T14:03:54Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Chemical and Environmental Engineering |
| spelling | doaj.art-683d586e78dd4f75883b1f8cc679d8c02023-11-30T05:11:17ZengElsevierCase Studies in Chemical and Environmental Engineering2666-01642023-12-018100509Mixed MDEA-PZ amine solutions for CO2 capture: Modeling and optimization using RSM and ANN approachesPedram Zafari0Ahad Ghaemi1School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, IranCorresponding author.; School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, IranThe process of capturing and reducing carbon dioxide (CO2) emissions through chemical absorption is widely acknowledged as the most effective technique, especially in dealing with natural gas streams or flue gases produced by fossil fuel power plants. In this research, we delve into the modeling and optimization of CO2 mass transfer flux (NCO₂). To accomplish this, employed a combination of Piperazine (PZ) and Methyldiethanolamine (MDEA) amines for CO2 absorption. The approach utilized artificial neural networks (ANN) and response surface methodology (RSM). We used Pi-Buckingham theory to derive dimensionless numbers for the input variables in both ANNs and RSM. The resulting models offer satisfactory outcomes by effectively capturing the influence of independent variables and their interactions on the objective function, thereby optimizing the CO2 capture process. The RSM approach employs a quadratic model. Through optimization, neural networks were fine-tuned to achieve the lowest error and the closest fit to experimental data. Both ANNs and RSM models demonstrated acceptable performance in predicting experimental data, with maximum R2 values of 0.99924 and 0.9663, respectively. Considering the mean squared error of 5.2 × 10−4 obtained from the simulations, the ANN is recommended as the preferred method for developing absorption simulation models.http://www.sciencedirect.com/science/article/pii/S2666016423002141CO2 captureANNRSMMDEA-PZPi-Buckingham |
| spellingShingle | Pedram Zafari Ahad Ghaemi Mixed MDEA-PZ amine solutions for CO2 capture: Modeling and optimization using RSM and ANN approaches Case Studies in Chemical and Environmental Engineering CO2 capture ANN RSM MDEA-PZ Pi-Buckingham |
| title | Mixed MDEA-PZ amine solutions for CO2 capture: Modeling and optimization using RSM and ANN approaches |
| title_full | Mixed MDEA-PZ amine solutions for CO2 capture: Modeling and optimization using RSM and ANN approaches |
| title_fullStr | Mixed MDEA-PZ amine solutions for CO2 capture: Modeling and optimization using RSM and ANN approaches |
| title_full_unstemmed | Mixed MDEA-PZ amine solutions for CO2 capture: Modeling and optimization using RSM and ANN approaches |
| title_short | Mixed MDEA-PZ amine solutions for CO2 capture: Modeling and optimization using RSM and ANN approaches |
| title_sort | mixed mdea pz amine solutions for co2 capture modeling and optimization using rsm and ann approaches |
| topic | CO2 capture ANN RSM MDEA-PZ Pi-Buckingham |
| url | http://www.sciencedirect.com/science/article/pii/S2666016423002141 |
| work_keys_str_mv | AT pedramzafari mixedmdeapzaminesolutionsforco2capturemodelingandoptimizationusingrsmandannapproaches AT ahadghaemi mixedmdeapzaminesolutionsforco2capturemodelingandoptimizationusingrsmandannapproaches |