Applications of AI/ML Techniques for Global Optimization of Thermal Energy Conversion Systems

Energy conversion systems, including cooling, refrigeration, air-conditioning, heat pumping, water harvesting, power generation, energy storage, thermoelectric systems, carbon capture, hydrogen storage, water desalination, and water purification, are vital for a wide range of industries. These include sectors like food preservation, pharmaceuticals, industrial cooling, and climate control, where efficient energy use is essential to meet both operational and environmental goals. Optimizing the performance of these systems is crucial for improving energy efficiency, reducing operational costs, and minimizing environmental impact. Traditionally, optimization techniques have relied on conventional engineering methods, but the increasing complexity and demand for more sustainable solutions have driven the need for advanced methodologies. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools to address the challenges of global optimization in refrigeration and thermal energy systems. By leveraging AI/ML algorithms, systems can be dynamically controlled, predictive models can be developed, and data-driven solutions can be applied to optimize design, operation, and maintenance, leading to more sustainable and cost-effective energy management.



The goal of this Research Topic is to explore the applications of AI and ML techniques in the global optimization of energy conversion systems for cooling, refrigeration, air-conditioning, heat pumping, water harvesting, power generation, energy storage, carbon capture, hydrogen storage, water desalination, and water purification applications. The research will focus on how AI/ML models can enhance system efficiency by improving decision-making processes in system design, operation, and fault detection. The problem lies in the inherent complexity and variability of these systems, which makes traditional optimization techniques less effective in achieving global solutions. AI/ML can offer personalized, adaptive models capable of optimizing performance across diverse conditions and improving system flexibility and resilience. The key challenge is integrating AI/ML algorithms with existing systems, ensuring the models are both accurate and efficient for real-time operation. This Research Topic seeks to develop AI-driven optimization strategies that can lead to energy savings, reduced emissions, and improved reliability, contributing to the global push for more sustainable energy systems.



This Research Topic aims to address the application of AI/ML techniques in optimizing energy conversion systems across various industrial and commercial settings. Potential authors are encouraged to contribute manuscripts that explore, but are not limited to, the following themes: advanced AI/ML algorithms for system modeling and simulation, real-time optimization techniques, predictive maintenance, fault detection and diagnostics, energy consumption forecasting, and multi-objective optimization approaches. Contributions may also cover hybrid models that combine AI/ML with traditional engineering methods or address challenges in the scalability and integration of AI/ML solutions in existing infrastructures. We invite original research articles, case studies, and review papers that provide insights into cutting-edge AI/ML applications, as well as discussions on practical implementation and the future potential of these technologies in optimizing energy conversion systems for cooling, refrigeration, air-conditioning, heat pumping, water harvesting, power generation, energy storage, carbon capture, hydrogen storage, water desalination, water purification, etc.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence, Machine Learning, Multi-Objective Optimization, Thermodynamic Analysis, Energy Conversion Systems

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.