{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:05:41Z","timestamp":1760058341692,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Secretar\u00eda de Ciencia Humanidades, Tecnolog\u00eda e Innovaci\u00f3n","award":["CVU 9391109"],"award-info":[{"award-number":["CVU 9391109"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Waste heat recovery is a critical strategy for optimizing energy consumption and reducing greenhouse gas emissions. In this context, the circular economy highlights the importance of this practice as a key tool to enhance energy efficiency, minimize waste, and decrease environmental impact. Artificial neural networks are particularly well-suited for managing nonlinearities and complex interactions among multiple variables, making them ideal for controlling a double-stage absorption heat transformer. This study aims to simultaneously optimize both user-defined parameters. Levenberg\u2013Marquardt and scaled conjugated gradient algorithms were compared from five to twenty-five neurons to determine the optimal operating conditions while the coefficient of performance and the gross temperature lift were simultaneously maximized. The methodology includes R2024a MATLAB\u00a9 programming, real-time data acquisition, visual engineering environment software, and flow control hardware. The results show that applying the Levenberg\u2013Marquardt algorithm resulted in an increase in the correlation coefficient (R) at 20 neurons, improving the thermodynamic performance and enabling greater energy recovery from waste heat.<\/jats:p>","DOI":"10.3390\/make7020029","type":"journal-article","created":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T04:11:40Z","timestamp":1743135100000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimizing a Double Stage Heat Transformer Performance by Levenberg\u2013Marquardt Artificial Neural Network"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6576-427X","authenticated-orcid":false,"given":"Suset","family":"V\u00e1zquez-Aveledo","sequence":"first","affiliation":[{"name":"Postgraduate in Science, Autonomous University of Morelos State, Av. Universidad 1001, Cuernavaca 62209, Morelos, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2625-7464","authenticated-orcid":false,"given":"Rosenberg J.","family":"Romero","sequence":"additional","affiliation":[{"name":"Engineering and Applied Research Centre, CIICAp, Autonomous University of Morelos State, Av. Universidad 1001, Cuernavaca 62209, Morelos, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1577-5629","authenticated-orcid":false,"given":"Lorena","family":"D\u00edaz-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Research Center in Sciences, Autonomous University of Morelos State, Av. Universidad 1001, Cuernavaca 62209, Morelos, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6726-9344","authenticated-orcid":false,"given":"Mois\u00e9s","family":"Montiel-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Faculty of Chemical Sciences and Engineering, Autonomous University of Morelos State, Av. Universidad 1001, Cuernavaca 62209, Morelos, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0172-4583","authenticated-orcid":false,"given":"Jes\u00fas","family":"Cerezo","sequence":"additional","affiliation":[{"name":"Engineering and Applied Research Centre, CIICAp, Autonomous University of Morelos State, Av. Universidad 1001, Cuernavaca 62209, Morelos, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Yuan, M., Mathiesen, B.V., Schneider, N., Xia, J., Zheng, W., Sorkn\u00e6s, P., Lund, H., and Zhang, L. (2024). Renewable energy and waste heat recovery in district heating systems in China: A systematic review. Energy, 294.","DOI":"10.1016\/j.energy.2024.130788"},{"key":"ref_2","unstructured":"IEA (2024, November 05). International Energy Agency. 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