Boosting CO2 capture efficiency of the exhausted RFCC flue gas by using intercooler exchangers: Leveraging ANN in MDEA-based approach
This study investigates the simultaneous capture of carbon dioxide (CO₂) and sulfur dioxide (SO₂) from residue fluid catalytic cracking (RFCC) flue gas using Methyldiethanolamine (MDEA) as an absorbent in a post-combustion capture system. The proposed system offers an effective solution for refineri...
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Elsevier
2025-05-01
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| Series: | Journal of CO2 Utilization |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2212982025000757 |
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| author | S. Masoud Hosseini Roja P. Moghadam Ali Afshar Ebrahimi |
| author_facet | S. Masoud Hosseini Roja P. Moghadam Ali Afshar Ebrahimi |
| author_sort | S. Masoud Hosseini |
| collection | DOAJ |
| description | This study investigates the simultaneous capture of carbon dioxide (CO₂) and sulfur dioxide (SO₂) from residue fluid catalytic cracking (RFCC) flue gas using Methyldiethanolamine (MDEA) as an absorbent in a post-combustion capture system. The proposed system offers an effective solution for refineries aiming to reduce greenhouse gas emissions and comply with environmental regulations. The system captures approximately 97 % of CO₂ and completely removes SO₂ from the RFCC flue gas. The integration of two inter-coolers significantly enhanced CO₂ capture efficiency by dissipating the heat generated during absorption, resulting in an 82 % reduction in CO₂ emissions compared to systems without inter-coolers. A comprehensive analysis of absorbent operating parameters—including MDEA flow rate (1100–1300 m³/h), temperature (40–50 °C), concentration (20–30 wt%), and absorption pressure (25–28 bar)—revealed that increasing all factors except temperature improved CO₂ capture performance. Notably, MDEA achieved complete SO₂ absorption under all tested conditions. An artificial neural network (ANN) model was developed to predict CO₂ emissions accurately, enabling real-time process control. The model demonstrated excellent performance, with an R² value of 0.9974 and a mean absolute error (MAE) of 0.0045 on the test dataset, indicating that operational conditions can reliably predict CO₂ emissions. This study contributes to enhancing the efficiency of practical post-combustion CO₂ capture systems. |
| format | Article |
| id | doaj-art-c25ea27e0baf4c00b35686a876c8a7d2 |
| institution | OA Journals |
| issn | 2212-9839 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of CO2 Utilization |
| spelling | doaj-art-c25ea27e0baf4c00b35686a876c8a7d22025-08-20T02:29:43ZengElsevierJournal of CO2 Utilization2212-98392025-05-019510309110.1016/j.jcou.2025.103091Boosting CO2 capture efficiency of the exhausted RFCC flue gas by using intercooler exchangers: Leveraging ANN in MDEA-based approachS. Masoud Hosseini0Roja P. Moghadam1Ali Afshar Ebrahimi2Process Engineering Department of the RFCC Unit, Shazand Oil Refining Company, Arak, IranMultiphase Reactors and Process Intensification Group, Bernal Institute, University of Limerick, IrelandDepartment of Petrochemicals, Iran Polymer and Petrochemical Institute, Tehran, Iran; Corresponding author.This study investigates the simultaneous capture of carbon dioxide (CO₂) and sulfur dioxide (SO₂) from residue fluid catalytic cracking (RFCC) flue gas using Methyldiethanolamine (MDEA) as an absorbent in a post-combustion capture system. The proposed system offers an effective solution for refineries aiming to reduce greenhouse gas emissions and comply with environmental regulations. The system captures approximately 97 % of CO₂ and completely removes SO₂ from the RFCC flue gas. The integration of two inter-coolers significantly enhanced CO₂ capture efficiency by dissipating the heat generated during absorption, resulting in an 82 % reduction in CO₂ emissions compared to systems without inter-coolers. A comprehensive analysis of absorbent operating parameters—including MDEA flow rate (1100–1300 m³/h), temperature (40–50 °C), concentration (20–30 wt%), and absorption pressure (25–28 bar)—revealed that increasing all factors except temperature improved CO₂ capture performance. Notably, MDEA achieved complete SO₂ absorption under all tested conditions. An artificial neural network (ANN) model was developed to predict CO₂ emissions accurately, enabling real-time process control. The model demonstrated excellent performance, with an R² value of 0.9974 and a mean absolute error (MAE) of 0.0045 on the test dataset, indicating that operational conditions can reliably predict CO₂ emissions. This study contributes to enhancing the efficiency of practical post-combustion CO₂ capture systems.http://www.sciencedirect.com/science/article/pii/S2212982025000757Carbon captureResidue fluid catalytic cracking (RFCC)Methyldiethanolamine (MDEA)Artificial Neural Network (ANN) |
| spellingShingle | S. Masoud Hosseini Roja P. Moghadam Ali Afshar Ebrahimi Boosting CO2 capture efficiency of the exhausted RFCC flue gas by using intercooler exchangers: Leveraging ANN in MDEA-based approach Journal of CO2 Utilization Carbon capture Residue fluid catalytic cracking (RFCC) Methyldiethanolamine (MDEA) Artificial Neural Network (ANN) |
| title | Boosting CO2 capture efficiency of the exhausted RFCC flue gas by using intercooler exchangers: Leveraging ANN in MDEA-based approach |
| title_full | Boosting CO2 capture efficiency of the exhausted RFCC flue gas by using intercooler exchangers: Leveraging ANN in MDEA-based approach |
| title_fullStr | Boosting CO2 capture efficiency of the exhausted RFCC flue gas by using intercooler exchangers: Leveraging ANN in MDEA-based approach |
| title_full_unstemmed | Boosting CO2 capture efficiency of the exhausted RFCC flue gas by using intercooler exchangers: Leveraging ANN in MDEA-based approach |
| title_short | Boosting CO2 capture efficiency of the exhausted RFCC flue gas by using intercooler exchangers: Leveraging ANN in MDEA-based approach |
| title_sort | boosting co2 capture efficiency of the exhausted rfcc flue gas by using intercooler exchangers leveraging ann in mdea based approach |
| topic | Carbon capture Residue fluid catalytic cracking (RFCC) Methyldiethanolamine (MDEA) Artificial Neural Network (ANN) |
| url | http://www.sciencedirect.com/science/article/pii/S2212982025000757 |
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