Techno-Economic Assessment of Carbon-Neutral Ammonia Fuel for Ships from Renewable Wind Energy

Climate change is fuelled by the continued growth of global carbon emissions, with the widespread use of fossil fuels being the main driver. To achieve a decarbonisation transition of the energy mix, the development of clean and renewable fuels has become crucial. Ammonia is seen as an important option for decarbonisation in the transport and energy sectors due to its zero-carbon emission potential and renewable energy compatibility. However, the high energy consumption and carbon emissions of the conventional Haber–Bosch method limit its sustainability. A green ammonia synthesis system was designed using ECLIPSE and Excel simulations in the study. Results show that at a recirculation ratio of 70%, the system’s annual total energy consumption is 426.22 GWh, with annual ammonia production reaching 8342.78 t. The optimal system configuration comprises seven 12 MW offshore wind turbines, integrated with a 460 MWh lithium battery and 240 t of hydrogen storage capacity. At this configuration, the LCOE is approximately £5956.58/t. It shows that incorporating renewable energy can significantly reduce greenhouse gas emissions, but further optimisation of energy storage configurations and reaction conditions is needed to lower costs. This research provides a reference for the industrial application of green ammonia in the transportation sector.

Authors

Institutions

Publication Details

Journal
Energies
Published
2025-10-17
DOI
https://doi.org/10.3390/en18205485
Primary Topic
Maritime Transport Emissions and Efficiency
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Techno-Economic Assessment of Carbon-Neutral Ammonia Fuel for Ships from Renewable Wind Energy

Yunjing Qi, Ye Huang, Yaodong Wang
Energies
Maritime Transport Emissions and Efficiency
article

Techno-Economic Assessment of Carbon-Neutral Ammonia Fuel for Ships from Renewable Wind Energy

Yunjing Qi, Ye Huang, Yaodong Wang
article en

Abstract

Climate change is fuelled by the continued growth of global carbon emissions, with the widespread use of fossil fuels being the main driver. To achieve a decarbonisation transition of the energy mix, the development of clean and renewable fuels has become crucial. Ammonia is seen as an important option for decarbonisation in the transport and energy sectors due to its zero-carbon emission potential and renewable energy compatibility. However, the high energy consumption and carbon emissions of the conventional Haber–Bosch method limit its sustainability. A green ammonia synthesis system was designed using ECLIPSE and Excel simulations in the study. Results show that at a recirculation ratio of 70%, the system’s annual total energy consumption is 426.22 GWh, with annual ammonia production reaching 8342.78 t. The optimal system configuration comprises seven 12 MW offshore wind turbines, integrated with a 460 MWh lithium battery and 240 t of hydrogen storage capacity. At this configuration, the LCOE is approximately £5956.58/t. It shows that incorporating renewable energy can significantly reduce greenhouse gas emissions, but further optimisation of energy storage configurations and reaction conditions is needed to lower costs. This research provides a reference for the industrial application of green ammonia in the transportation sector.

EnergiesVol. 18(20)
Queen's University Belfast (GB), University of Ulster (GB), Durham University (GB)
Durham University
Openalex Percentile: Top 73%
Maritime Transport Emissions and Efficiency
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.