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Soft Computing Tools (Intelligent Techniques) for Nano-enhanced PCM

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Nano Enhanced Phase Change Materials

Part of the book series: Materials Horizons: From Nature to Nanomaterials ((MHFNN))

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Abstract

The application of the machine learning (ML) approach in the development of nanophase change materials (PCMs) signifies a promising step forward in the development of energy storage technologies. The nuptials of machine learning with nanotechnology has resulted in a new paradigm for developing, creating, and synthesizing high-performance PCM materials. ML algorithms have established their ability to forecast PCM material qualities, optimize synthesis conditions, and decrease the cost and time involved with traditional trial-and-error techniques. Material processing and synthesis optimization is another possible use of machine learning in nano PCM research. Machine learning algorithms may analyze data from multiple manufacturing steps and optimize elements such as pressure, temperature, and reaction time to produce desired material properties. This might lead to more effective and affordable synthesis techniques, as well as more exact control over material attributes. Machine learning may also improve the accuracy of micro PCM simulations and models. This will enable more complete and precise simulations of the behavior and features of micro PCMs, leading to improved comprehension and design of these types of materials. The combination of nanotechnologies, artificial intelligence, and other new technologies such as 3D printing and microfluidics creates new opportunities for inventing and producing complex materials with unparalleled performance. To fully realize the promise of nano PCMs and ML in addressing the pressing challenges of energy sustainability and climate change, it is critical to continue investing in development and research in this sector.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Delhi Skill and Entrepreneurship University, Delhi, India

    Prabhakar Sharma

  2. School of Engineering and Technology, Research Centre for Nano-Materials and Energy Technology (RCNMET), Sunway University, No. 5, Jalan Universiti, 47500, Bandar Sunway, Petaling Jaya, Selangor Darul Ehsan, Malaysia

    A. K. Pandey

  3. CoE for Energy and Eco-Sustainability Research, Uttaranchal University, Dehradun, India

    A. K. Pandey

  4. Department of Sustainable Renewable Energy Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Zafar Said

  5. Department of Industrial and Mechanical Engineering, Lebanese American University (LAU), Byblos, Lebanon

    Zafar Said

  6. U.S.-Pakistan Center for Advanced Studies in Energy (USPCAS-E), National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Zafar Said

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  1. Department of Sustainable and Renewable Energy Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Zafar Said

  2. 3Research Centre for Nano-Materials and Energy Technology (RCNMET), Sunway University, Petaling Jaya, Selangor, Malaysia

    Adarsh Kumar Pandey

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Sharma, P., Pandey, A.K., Said, Z. (2023). Soft Computing Tools (Intelligent Techniques) for Nano-enhanced PCM. In: Said, Z., Pandey, A.K. (eds) Nano Enhanced Phase Change Materials. Materials Horizons: From Nature to Nanomaterials. Springer, Singapore. https://doi.org/10.1007/978-981-99-5475-9_11

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  • DOI: https://doi.org/10.1007/978-981-99-5475-9_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-5474-2

  • Online ISBN: 978-981-99-5475-9

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