Recent advances on first-principles modeling for the design of materials in CO2 capture technologies

Carbon capture, utilization and sequestration systems design and operation optimization: Assessment and perspectives of artificial intelligence opportunities

Carbon capture, utilization, and sequestration (CCUS) is a promising solution to decarbonize the energy and industrial sectors to mitigate climate change. An integrated assessment of technological options is required for the effective deployment of CCUS large-scale infrastructure between CO2 production and utilization/sequestration nodes. However, developing cost-effective strategies from engineering and operation perspectives to implement CCUS is challenging. This is due to the diversity of upstream emitting processes located in different geographical areas, available downstream utilization technologies, storage sites capacity/location, and current/future energy/emissions/economic conditions. This paper identifies the need to achieve a robust hybrid assessment tool for CCUS modeling, simulation, and optimization based mainly on artificial intelligence (AI) combined with mechanistic methods. Thus, a critical literature review is conducted to assess CCUS technologies and their related process modeling/simulation/optimization techniques, while evaluating the needs for improvements or new developments to reduce overall CCUS systems design and operation costs. These techniques include first principles- based and data-driven ones, i.e. AI and related machine learning (ML) methods. Besides, the paper gives an overview on the role of life cycle assessment (LCA) to evaluate CCUS systems where the combined LCA-AI approach is assessed. Other advanced methods based on the AI/ML capabilities/algorithms can be developed to optimize the whole CCUS value chain. Interpretable ML combined with explainable AI can accelerate optimum materials selection by giving strong rules which accelerates the design of capture/utilization plants afterwards. Besides, deep reinforcement learning (DRL) coupled with process simulations will accelerate process design/operation optimization through considering simultaneous optimization of equipment sizing and operating conditions. Moreover, generative deep learning (GDL) is a key solution to optimum capture/utilization materials design/discovery. The developed AI methods can be generalizable where the extracted knowledge can be transferred to future works to help cutting the costs of CCUS value chain.

CO2 capture and a route to transform it in formic acid: a theoretical approach

New organic frameworks (COFs) employing two coronene molecules forced to adopt a parallel conformation thus forming a molecular reactor are proposed. These COFs exhibit different distances between the coronene units, thus creating diverse electronic environments. The simulation of the trapping of CO₂ and H₂ molecules in the reactor hollow having distinct anchor fragments yields in the two cases formic acid. The analysis of the reaction profile allowed us to propose a thermodynamically favored process. The nature of the frontier molecular orbitals in the involved processes is also discussed. Reaction profile of CO₂ and H₂ process to yield formic acid.

Modification of Metal (Fe, Al) Doping on Reaction Properties of a NiO Oxygen Carrier with CO during Chemical Looping Combustion

Oxygen carriers can significantly enhance the performance of chemical looping combustion at low energy-cost CO₂ capture. Based on the density functional theory, a microscopic model of the metal Fe, Al-doped NiO oxygen carrier was established. The results indicate that the intermediate state energy and the reaction energy reduce due to electronic interaction of the Al-doped surface. With the progress of the reaction, the NiO-Al surface promotes the oxidation process of CO, indicating that the activity of the NiO surface enhanced, which is attributed to the electronic and steric effects of the Al-O structure. For the decomposition of CO on the OC surface, doping with other atoms is beneficial to suppress the carbon deposition, which is related to the steric hindrance caused by doping with other atoms. Besides, doping with iron and aluminum atoms is more conducive to the movement of OC bulk crystal lattice oxygen to the surface, thereby promoting subsequent reactions. Therefore, it is feasible to improve the reactivity of the Ni-based OC by doping metal Al, and its modification effect is closely related to the characteristics of the components.