ArticleACS omega2025
A Predictive Exergy-Adaptive Genetic Algorithm for the Optimization of the DMR Liquefaction Process.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
Against the backdrop of increasing global energy demands and the need for enhanced efficiency in industrial processes, the dual mixed-refrigerant (DMR) natural-gas liquefaction process involves strong coupling among numerous operating parameters, and conventional optimization methods often fail to reach the global optimum. To address this challenge, we propose an intelligent optimization strategythe Predictive Exergy-Adaptive Genetic Algorithm (PEA-GA)which integrates exergy analysis with a machine-learning surrogate model. A detailed Aspen HYSYS simulation of the DMR process is developed to examine how feed-gas pressure and temperature, as well as precooling and deep-cooling parameters affect specific energy consumption (SEC) and exergy efficiency. By embedding an exergy-loss evaluation mechanism and employing a radial basis function (RBF) neural network to accelerate the search, key operating variables are optimized. Relative to the base case, the optimized scheme lowers SEC from 0.281 kWh kg
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