Cutting-Edge Research: Artificial Intelligence Applications and Control Optimization in Advanced CO2 Cycles

In recent years, advanced CO2 cycles, including supercritical CO2 power cycles, transcritical CO2 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 CO2 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 CO2 cycles, demonstrating the superiority of the joint control strategy in CO2 cycles. It then delves into the potential of novel control technologies for CO2 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 CO2 cycles in new energy vehicles, solar power generation, aerospace, and other fields. It also provides an outlook on the development direction of CO2 cycle control strategies in light of the evolving trends in the energy sector and advancements in AI methodologies.