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 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