Machine learning-based hybrid dynamic modeling and economic predictive control of carbon capture process for ship decarbonization

• Formulated a ship decarbonization design integrating PCC with the ship energy system. • Developed a hybrid model to capture the PCC dynamics under varying ship engine loads. • Designed an EMPC for energy-efficient PCC operation with high carbon capture rate. • Employed cross-entropy to efficiently solve the complex EMPC optimization problem. • Conducted extensive simulations verifying superior modeling and control performance. Implementing carbon capture technology on-board ships holds promise as a solution to facilitate the reduction of carbon intensity in international shipping, as mandated by the International Maritime Organization. In this work, we address the energy-efficient operation of shipboard carbon capture processes by proposing a hybrid modeling-based economic predictive control scheme. Specifically, we consider a comprehensive shipboard carbon capture process that encompasses the ship engine system and the shipboard post-combustion carbon capture plant. To accurately and robustly characterize the dynamic behaviors of this shipboard plant, we develop a hybrid dynamic process model that integrates available imperfect physical knowledge with neural networks trained using process operation data. An economic model predictive control approach is proposed based on the hybrid model to ensure carbon capture efficiency while minimizing energy consumption required for the carbon capture process operation. The cross-entropy method is employed to efficiently solve the complex non-convex optimization problem associated with the proposed hybrid model-based economic model predictive control method. Extensive simulations, analyses, and comparisons are conducted to verify the effectiveness and illustrate the superiority of the proposed framework. The proposed hybrid model-based economic model predictive control reduced the overall economic cost by 8.07% compared to conventional optimal set-point tracking nonlinear model predictive control and achieved a 4.20% lower economic cost with a 9.10% higher carbon capture rate than the imperfect first-principles model-based economic model predictive control.