Using AI to Increase Heat Exchanger Efficiency: An Extensive Analysis of Innovations and Uses
DOI:
10.47709/ijmdsa.v3i4.4617Keywords:
AI, heat exchangers, machine learning, deep learning, expert systems, advanced materials, smart systems, predictive maintenance, performance optimization, sustainability, data quality, integration, computational costs, waste heat recoveryDimension Badge Record
Abstract
Artificial intelligence (AI) has made significant strides toward cost reduction and performance optimization in heat exchanger technologies. Artificial intelligence (AI) methods in machine learning, deep learning, and expert systems provide significant advancements in diagnostics, performance optimization, and predictive maintenance. While deep learning is superior at recognizing intricate patterns, machine learning offers flexibility through data analysis. Expert systems use domain expertise to make decisions, although they might not be as flexible as data-driven methods. Hybrid approaches integrate these strategies to improve flexibility and performance. New developments include smart heat exchangers with IoT capabilities for real-time monitoring, compact designs for a variety of applications, and new materials and coatings that improve durability and efficiency. Reducing environmental effect is also reflected in sustainable solutions like waste heat recovery. Nevertheless, issues like computing costs, data quality, and interaction with current systems still need to be resolved. Optimized computational methodologies, modular integration, and sophisticated sensor technology are required to address these problems. AI has the power to completely transform heat exchanger technology by enhancing sustainability and efficiency. Future breakthroughs will be fueled by ongoing improvements in materials, designs, and AI approaches, offering more complex solutions to satisfy changing environmental and performance requirements.
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