• Proposed a novel LAES system integrating LNG cold energy and industrial waste heat. • Established a hybrid LSTM-NSGA-II framework to optimize multi-objective performance. • Maintains 1.8 MPa liquid air tank pressure using efficient pre-cooling techniques. • Achieves 52.1% exergy efficiency and 190.1% round-trip efficiency. • Utilizes waste energy to balance power supply and demand for sustainable operation. Liquefied natural gas (LNG) cold energy is often underutilized during regasification, leading to significant energy waste. While existing recovery systems have been proposed, they often struggle with the dynamic mismatch between energy supply and demand, lacking effective strategies for intelligent optimization under fluctuating conditions. This study introduces an integrated energy system that combines the LNG-Brayton cycle, Organic Rankine cycle, wastewater ice-making, and liquid air energy storage. The system effectively recovers LNG cold energy and exhaust gas, while addressing supply–demand imbalances through dynamic energy storage. Furthermore, a data-driven intelligent framework where a Long Short-Term Memory neural network is used for accurate load forecasting, the results of which are fed into a multi-objective optimization approach to synergistically improve system performance. Optimization results indicate that the system achieves an exergy efficiency of 52.1%, a net power output of 1371 kW, and a round-trip efficiency of 190.1%. The proposed system demonstrates a novel pathway for sustainable energy recovery by seamlessly combining multi-energy integration with intelligent dynamic control. Economic analysis confirms the system’s competitiveness under favorable conditions. This work provides a theoretical basis for the application of integrated energy systems in offshore and ocean engineering, where space and energy reliability are critical.
Zheng et al. (Mon,) studied this question.