<ul><li>Shipboard carbon capture is a promising solution to reduce carbon emissions in international shipping.</li><li>A data-driven dynamic modeling and economic predictive control approach is proposed within the Koopman framework for shipboard post-combustion carbon capture plants.</li><li>A deep neural Koopman operator modeling approach is used to establish a time-varying model predicting economic operational cost and system outputs based on accessible state measurements.</li><li>The proposed method improves economic operational performance, carbon capture rate, and ensures safe operation by satisfying hard constraints on system outputs.</li></ul>

Deep Neural Koopman Operator-based Economic Model Predictive Control of Shipboard Carbon Capture System

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