Shipboard CO 2 capture (SCC) processes face significant challenges, including high costs and the need for extra heating energy to capture 90% of the CO 2 . Therefore, this study proposes advanced designs and an integration framework using correlation analysis and machine learning-based optimization to achieve the energy-and cost-effective SCC process. Specifically, we develop CO 2 capture and ship engine simulators, which are validated and then applied to develop conventional and four advanced designs for the SCC process. Next, a first deep neural network (DNN) model is developed as a surrogate model to precisely predict the performance of the conventional design at low computation cost, serving as the basis for formulating two optimization problems. The optimization results reveal that capturing 90% of CO 2 by using the conventional design requires an additional 1.369 MW of heating energy, costing 108.583 $/tCO 2 . Then, the four advanced designs are analyzed to exhibit their potential for reducing the CO 2 capture cost and heating energy, with correlation methods identifying SCC using lean vapor compression (LVC-SCC) design as the most feasible design. Finally, a second DNN-based surrogate model is developed for the LVC-SCC design before being used to formulate the third optimization problem. The optimization results confirm that the LVC-SCC design leverages available heating energy sources to capture 90% of CO 2 (approximately 8.89 tCO 2 /h) at 53.54 $/tCO 2 , emitting only 0.46 ppm monoethanolamine. Moreover, compared to the conventional design, the LVC-SCC design significantly reduces the cost, heating energy, and cooling energy by approximately 49.8%, 15%, and 12%, respectively. The proposed designs, the machine learning-based optimization approach, and the resulting findings provide valuable solutions for driving the international shipping industry toward achieving netzero greenhouse gas emissions by 2050.
