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

Abstract

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

Keywords

• LNG regasification terminal
• hydrogen
• LOHC
• neural network
• modeling
• energy demand forecast