Optimal multi-period source-sink matching in carbon capture, utilization, and storage (CCUS) systems under neutrosophic environment

Abstract

Carbon capture, utilization, and storage (CCUS) is an important technology for mitigating the effect of climate change. It involves the capture of CO2 from a point source, transport it through pipelines and store it in geological reservoirs for permanent storage. CCUS enables the use of fossil fuel-based power plant sources that produce a steady amount of energy and the reduction of CO2 emissions from these sources simultaneously. While CO2 sources are easy to characterize, the storage parameters of geological reservoirs are more difficult to measure, thus, estimates are provided and used for planning large-scale CCUS systems. CO2 utilization can provide a means to delay CO2 emissions by converting it highly valuable products, however, its sequestration potential is highly uncertain. A systematic mathematical approach is needed to effectively plan CCUS systems considering the risks associated with the estimates in the characteristics of the geological reservoir and the sequestration benefits from CO2 utilization. In this study, an optimization model is developed for planning multi-period CCUS systems under neutrosophic environment. The model considers managing storage risk by maximizing satisfaction from minimizing risks, minimizing dissatisfaction from consequences brought by these risks, and minimizing indeterminacy from inaccuracy of storage characteristics estimates. For utilization, the model considers the satisfaction for CO2 utilization benefits, dissatisfaction for opportunity losses, and indeterminacy in sequestration potential. Two case studies are used to illustrate the model where a sensitivity analysis is performed to illustrate the effect of the different risk appetite on the optimal solution. These cases studies reveal important insights on scheduling operations between CO2 sources and sinks and the allocation of captured CO2 for storage and utilization. The model can be used to develop policies for considering the risks associated with these uncertainties and how it will be managed.