Liquefied Natural Gas and Hydrogen Regasification Terminal Design through Neural Network Estimated Demand for the Canary Islands

This publication explores how the existing synergies between conventional liquefied natural gas regasification and hydrogen hydrogenation dehydrogenation processes can be exploited. Liquid Organic Hydrogen Carrier methodology has been analyzed for from a thermodynamic point of view to propose an energy integration system improve efficiency during hybridization periods. The proposed neural network acceptably predict power demand using daily average temperature as single predictor, with mean relative error 0.25%, while simulation results based on estimated peak show that high-pressure compression is most energy-demanding process in (with more than 98% total consumption). In such scenario, exceeding liquid organic carrier have used Rankine’s cycle input produce both compressors heat exchangers, generating savings up 77%. designed terminal securely 158,036 kg/h 11,829 hydrogen.