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Thermal intelligence: exploring AI’s role in optimizing thermal systems – a review

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Abstract

Artificial Intelligence (AI) is profoundly transforming multiple industries by enhancing efficiency, decision-making, and problem-solving capabilities. This impact is particularly notable in sectors such as thermal engineering, industrial processing, and solar thermal systems. In thermal engineering, AI has addressed challenges in modeling, prediction, control, and optimization, leading to improvements in energy efficiency, system reliability, and environmental sustainability. Innovations in AI-driven models, such as gradient-boosted regression trees and deep reinforcement learning, have advanced the design and management of thermal systems, including energy storage devices, power plants, and HVAC systems. AI is also revolutionizing industrial processing and solar thermal systems. In industrial settings, AI applications such as quality control, process optimization, and predictive maintenance are enhancing operational efficiency and supporting the transition towards Industry 5.0. Despite these advancements, challenges remain in aligning AI solutions with real-world industrial requirements. Similarly, in solar thermal systems, AI is optimizing performance through advanced tracking systems, predictive maintenance, and energy storage strategies. The integration of AI with technologies like the Internet of Things (IoT) and advanced materials is proving effective in boosting system efficiency. Overall, AI is providing innovative solutions that improve efficiency, reliability, and sustainability across various applications. However, ongoing research is essential to address limitations in AI models, enhance their generalizability, and integrate emerging technologies. Continued development in AI methodologies will be crucial for optimizing energy efficiency, system performance, and environmental impact in these sectors.

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Authors and Affiliations

  1. Department of Mechanical Engineering, AMET University, Chennai, 603112, Tamil Nadu, India

    Madhesh Devasenan

  2. Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, 603110, Tamil Nadu, India

    Saritha Madhavan

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  1. Madhesh Devasenan
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  2. Saritha Madhavan
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Author 1: made substantial contributions to the conception Author 2: drafted the work.

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Correspondence to Madhesh Devasenan.

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Devasenan, M., Madhavan, S. Thermal intelligence: exploring AI’s role in optimizing thermal systems – a review. Interactions 245, 282 (2024). https://doi.org/10.1007/s10751-024-02122-6

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