首页|Researchers from Yangtze Normal University Report Findings in Machine Learning ( A New Programmed Method for Retrofitting Heat Exchanger Networks Using Graph Mac hine Learning)
Researchers from Yangtze Normal University Report Findings in Machine Learning ( A New Programmed Method for Retrofitting Heat Exchanger Networks Using Graph Mac hine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on Ma chine Learning. According to news originating from Chongqing, People's Republic of China, by NewsRx correspondents, research stated, "Unsupervised graph machine learning provides a powerful framework for modeling and analyzing heat exchange r networks (HENs). This paper proposes a graph-based thermally guided path searc h (TGPS) method that systematically identifies and evaluates retrofit options to enhance the thermal performance of HENs." Our news journalists obtained a quote from the research from Yangtze Normal Univ ersity, "The method represents the HEN as a bipartite graph and uses automated a lgorithms to search for feasible heat integration paths. Thermodynamically incon sistent paths are filtered out based on temperature feasibility rules. The resul ting retrofit options are evaluated using graphical metrics like betweenness cen trality and cluster coefficients, as well as thermal performance indicators. A r e-routing technique is introduced to address temperature mismatch issues for ser ial heat exchanger connections. When applied to a Kalina power cycle, the therma l efficiency of the optimum configuration is increased by 9.7%. Thi s method is compared with both pinch analysis and the Energy Transfer Diagram ap proach, and it is thoroughly tested and verified for an ammonia-water absorption refrigeration cycle as well."
ChongqingPeople's Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningYangtze Normal University
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