Hosseinifard et al.(2025): Machine learning–enabled optimization of a direct air capture system integrated with enhanced oil recovery.

Hosseinifard, F., Ghasemzadeh, S., Salimi, M. & Amidpour, M IN: Resources, Conservation & Recycling Energy, https://doi.org/10.1016/j.rechem.2025.102836

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.