Cutting-Edge Research: Artificial Intelligence Applications and Control Optimization in Advanced CO 2 Cycles

In recent years, advanced CO 2 cycles, including supercritical CO 2 power cycles, transcritical CO 2 power cycles and refrigeration cycles, have demonstrated significant potential for application across a broad spectrum of energy conversion processes, owing to their high efficiency and compact components that are environmentally benign and non-polluting. This study presents a comprehensive review of the dynamic performance and control strategies of these advanced CO 2 cycles. It details the selection of system configurations and various control strategies, detailing the principles behind different control strategies, their applicable scopes, and their respective advantages. Furthermore, this study conducts a comparison between the joint control strategy and single control strategies for CO 2 cycles, demonstrating the superiority of the joint control strategy in CO 2 cycles. It then delves into the potential of novel control technologies for CO 2 cycles, using model-based control technology powered by artificial intelligence as a case study. This study also offers an extensive overview of control theory, methodology, scope of application, and the pros and cons of various control strategies, with examples including extreme value-seeking control, model predictive control (MPC) based on an artificial neural network model, and MPC based on particle swarm optimization. Finally, it explores the application of AI-controlled CO 2 cycles in new energy vehicles, solar power generation, aerospace, and other fields. It also provides an outlook on the development direction of CO 2 cycle control strategies in light of the evolving trends in the energy sector and advancements in AI methodologies.