DESC0025082

The Department of Energy seeks solutions for carbon capture and storage operations as there is currently a lack of advanced, real-time predictive tools capable of accurately forecasting amine-based emissions, especially under dynamic, non-steady state conditions. To progress towards a net-zero carbon economy by 2050, technology that enables effective carbon capture, conversion, transportation, storage, and utilization is critical. Current solvent-based carbon capture technologies face challenges due to solvent degradation, resulting in varied emissions that are hard to manage and predict. This unpredictability hampers effective emissions control and utilization of carbon capture technologies, posing risks to human health and environmental safety. VISIMO will develop an advanced artificial intelligence-based forecasting tool, EmiFor, designed to accurately predict amine emissions from carbon capture processes in real-time. EmiFor leverages machine learning techniques to analyze and forecast emissions under varying operating conditions, providing critical data for emissions management and mitigation. The Phase I objective focuses on demonstrating the feasibility of this approach, ensuring EmiFor can accurately predict emissions and aid in the effective management of carbon capture technologies. The initial phase will involve developing EmiFor using test data from a CESAR1 solvent test campaign, training machine learning models to forecast emissions accurately, and incorporating mechanisms for causal impact analysis and counterfactual scenario evaluation. This will enable plant managers to understand and plan for emissions mitigation effectively. The work aims to validate the tool's predictive capabilities and establish a foundation for further development and commercialization. The successful development and commercialization of this forecasting tool will advance emissions management in the carbon capture and storage industry. By enabling more efficient and compliant operations, the tool has the potential to significantly reduce the cost of carbon capture technologies, facilitating broader adoption and contributing to global carbon reduction efforts. Additionally, the tool's adaptability allows for application across various sectors, further expanding its market potential and societal benefits.