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

TL;DR

This paper presents a hybrid dynamic modeling and economic predictive control framework for shipboard carbon capture processes, integrating physical knowledge and neural networks to optimize energy efficiency and carbon capture performance.

Contribution

It introduces a novel hybrid modeling approach combined with economic model predictive control for shipboard carbon capture, addressing dynamic behavior and energy efficiency.

Findings

The hybrid model accurately captures process dynamics.

The control scheme reduces energy consumption.

Simulations demonstrate improved carbon capture efficiency.

Abstract

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.