{"id":6083,"date":"2025-07-17T14:55:08","date_gmt":"2025-07-17T05:55:08","guid":{"rendered":"https:\/\/aizoth.com\/?post_type=use_case&p=6083"},"modified":"2025-12-05T15:45:20","modified_gmt":"2025-12-05T06:45:20","slug":"contents-e013","status":"publish","type":"use_case","link":"https:\/\/aizoth.com\/en\/use_case\/contents-e013\/","title":{"rendered":"Multi-objective optimization of an advanced Lorenz cycle (ALC) using a surrogate model based on Multi-Sigma\u00ae"},"content":{"rendered":"\n

In pursuit of a carbon-neutral society, the development of highly efficient thermal energy conversion technologies for both heating and cooling is becoming increasingly important. Currently, to improve the energy efficiency of next-generation industrial complexes\u2014such as cold chains, data centers, and direct air capture (DAC) systems\u2014there is a pressing need for thermal management systems capable of operating under large temperature glides, ranging approximately from -30\u2103 to 150\u2103. To address this challenge, an advanced Lorenz cycle (ALC) utilizing the organic Rankine cycle has been proposed. In this study, we constructed a surrogate model for a heat pump based on the ALC and explored high-efficiency operating conditions through multi-objective optimization.<\/strong><\/p>\n\n\n\n

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1. Prediction of Temperature and Coefficient of Performance (COP) Using Multi-Sigma\u00ae<\/sup><\/strong><\/strong><\/h2>\n\n\n\n

Using Multi-Sigma\u00ae<\/sup>, a surrogate model was constructed based on cycle simulation data of a heat pump for water heating. This surrogate model demonstrated high prediction accuracy, with correlation coefficients of 0.925 for temperature and 0.888 for the coefficient of performance (COP), which represents energy efficiency.<\/p>\n\n\n\n

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Explanatory Variables:<\/strong><\/p>\n\n\n\n