Machine learning–enabled optimization of a direct air capture system integrated with enhanced oil recovery

Farzin Hosseinifard, Shahabeddin Ghasemzadeh, Mohsen Salimi, Majid Amidpour,
Machine learning–enabled optimization of a direct air capture system integrated with enhanced oil recovery,
Results in Chemistry,
2025,
102836,
ISSN 2211-7156,
https://doi.org/10.1016/j.rechem.2025.102836.
(https://www.sciencedirect.com/science/article/pii/S2211715625008203)
Abstract: The escalating levels of CO₂ in the atmosphere have heightened global environmental concerns, necessitating the deployment of efficient and scalable carbon extraction strategies. Among emerging methods, direct air capture (DAC) stands out as a viable approach. This research introduces a novel DAC configuration tailored to enhance the efficiency of enhanced oil recovery (EOR). The DAC system was modeled using Aspen Plus V11, employing a hydroxide-to‑carbonate conversion pathway for CO₂ absorption. As part of broader carbon management efforts, Carbon Capture, Utilization, and Storage (CCUS) plays a pivotal role in curbing emissions, particularly through its application in subsurface oil recovery processes. To assess and forecast the impact of DAC-sourced CO₂ on EOR performance in Abadan, a suite of Machine learning techniques was applied. These included XGBoost, Random Forest, Gradient Boosting, Support Vector Regression, Linear Regression, k-Nearest Neighbors, Bagging, and Stacking. Among the models tested, the Decision Tree algorithm demonstrated the highest predictive capability, yielding an R2 score of 0.87. It effectively estimated a growth in EOR efficiency from 19 % to approximately 21.3 %.
Keywords: Direct air capture (DAC); Enhanced oil recovery (EOR); Carbon capture utilization and storage (CCUS); Machine learning models; CO₂ absorption simulation