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

José Ignacio García-Lajara and Miguel Ángel Reyes-Belmonte ()
Additional contact information
José Ignacio García-Lajara: Department of Chemical and Energy Technology, School of Experimental Sciences and Technology (ESCET), Rey Juan Carlos University, 28933 Madrid, Spain
Miguel Ángel Reyes-Belmonte: Department of Chemical and Energy Technology, School of Experimental Sciences and Technology (ESCET), Rey Juan Carlos University, 28933 Madrid, Spain

Energies, 2022, vol. 15, issue 22, 1-24

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/22/8682/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/22/8682/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:22:p:8682-:d:977554

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().