Optimization of Carbon Capture & Storage (CCS) Energy System Using Evolutionary Algorithms

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a novel MOEA framework for CCS optimization, demonstrating significant improvements in LCOE and energy consumption, useful for further research in carbon capture system design.

🏢実務担当者:Offers a decision-support tool for optimizing CCS operations, potentially reducing costs and improving carbon abatement efficiency for energy companies.

🏛政策担当者:Highlights the potential of evolutionary algorithms to lower CCS costs, supporting policy frameworks that incentivize CCS deployment as a climate mitigation strategy.

Abstract The integration of Carbon Capture and Storage (CCS) into existing energy infrastructures is essential for meeting global net-zero targets, yet it is often hindered by significant energy penalties and complex thermoeconomic trade-offs. This research presents a novel optimization framework for CCS energy systems utilizing a customized Multi-Objective Evolutionary Algorithm (MOEA) based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II). Unlike traditional gradient-based methods, which often struggle with the non-linear and non-convex nature of carbon capture chemistry and transport logistics, the proposed evolutionary approach utilizes a dynamic mutation strategy to explore a high-dimensional search space of system configurations. The framework treats the capture unit, compression chain, and geological storage as a singular, coupled system, optimizing for the simultaneous reduction of operational costs and carbon intensity. The optimization framework was validated using publicly available benchmark data from the IEA GHG Programme and the National Energy Technology Laboratory (NETL), supplemented by experimental solvent regeneration data from Knudsen et al. (2011) and Moser et al. (2011). By applying the evolutionary paradigm, the system achieved a 11.2% reduction in Levelized Cost of Electricity (LCOE) compared to a standard post-combustion capture baseline of 114.6 USD/MWh, yielding an optimized value of approximately 97.4 USD/MWh. From a thermodynamic perspective, the algorithm successfully lowered the specific energy consumption for solvent regeneration to 2.85 GJ/tCO2, representing an 11.2% improvement relative to the 3.21 GJ/tCO2 baseline reported for advanced solvent systems. Environmental metrics further validate the approach, showing a 13.9% increase in net carbon abatement through optimized scheduling of capture cycles during off-peak hours. Furthermore, the multi-objective Pareto front yielded a 15% increase in hypervolume diversity relative to a standard genetic algorithm baseline, providing a robust set of decision-making tools for balancing economic viability with aggressive decarbonization goals. These findings confirm that evolutionary algorithms offer a scalable and computationally efficient path for the large-scale deployment of optimized CCS technologies.

• openalex https://doi.org/10.2118/235113-msfirst seen 2026-08-11 04:37:52