Optimal fuel blends for ammonia fuelled thermal propulsion systems

Lead Research Organisation: University of Hertfordshire
Department Name: School of Engineering and Technology

Abstract

Renewable, carbon-free fuels as well as optimised combustion systems have recently drawn a lot of attention in engine research to further reduce emissions of criteria pollutants and greenhouse gases (GHGs) in transport. Whilst significant investment is being currently allocated to electric and Electrochemical energy systems, there remains significant doubts as to whether batteries will provide cost-effective national energy security over medium-duration periods. Moreover, the transport sector produces about 10% of the world's GHG emissions and replacing combustion engines with "zero emission" alternatives has only a limited potential in reducing global GHG emissions. Since around 80% of useful energy such as heat, propulsion energy and electricity is produced via combustion processes and due to the high energy density and storability of fuels, combustion will remain for several decade the technologically, economically and ecologically best solution for many applications in transport and power generation.

The UK's long-term emissions target is currently for a 100% reduction in GHG emissions by 2050 compared to 1990. Meeting future UK carbon budgets will require the grand challenge of carbon-free energy systems to be addressed. Ammonia (NH3) has been identified as one of the most promising hydrogen energy carriers, and will play an important role during the process of decarbonisation of power sector and transport. Ammonia is carbon-free, has no direct GHG effect, and can be synthesised with an entirely carbon-free process from renewable power sources. The greatest advantage of ammonia is its high energy density (comparable to that of fossil fuels), which makes it an effective fuel and energy storage option. Studies have shown that to make ammonia a viable fuel in combustion engines, it needs to be mixed with other fuels (e.g., hydrogen) as combustion promoters due to ammonia's low flame speed and high resistance to auto-ignition.

Gas turbines are high-efficiency candidates for use of ammonia and have the potential to reduce the cost per kWh produced whilst providing clean, green energy for power generation and propulsion systems. Although, recent studies have shown that ammonia/hydrogen blends could be burned efficiently with low emissions and high efficiencies in gas turbines, they require optimisation study in terms of choice of fuel composition and development of advanced injection strategies to achieve acceptable NOx levels while maintaining high thermal efficiencies.

The aim of the proposed project is to develop a computationally cost-effective numerical tool for Co-Optimisation of fuel blend and combustion system in a systematic way, and to examine how the conflicting requirements can be met by adding gaseous fuels (e.g., methane, hydrogen and syngas) to ammonia so that engine can be operated stably and reliably with improved thermal efficiency and minimal NOx emissions. This computational study requires development of a reduced reaction mechanism for fuel blends with the goal of further reduction in the number of reactions under engine relevant conditions. While computational fluid dynamics (CFD) simulations in combination with reaction mechanisms can capture complex phenomena with high accuracy, they have high computational cost and, therefore, are not efficient for the optimisation studies. Thus, a reliable and comprehensive 0D phenomenological model with reduced chemistry will be developed for combustion modelling of the engine fuelled with ammonia-based fuel blends. A genetic algorithm (GA) optimisation model coupled to the 0D model will be developed to simultaneously optimise fuel composition and engine input parameters(e.g., fuel injection strategy, inlet conditions, equivalence ratio). Finally, the performance of the optimal blend and optimised engine parameters will be experimentally studied in the relevant engine experiments at Gas Turbine Research Centre (GTRC), in Cardiff University.

Planned Impact

Nowadays, the need for carbon-free renewable energy to address some of the key challenges facing today's global society such as the cost of energy, energy security and climate change has motivated countries such as Japan, USA, UK, Canada and South Korea to re-start programs for the development of ammonia-fuelled power generation systems. This proposal addresses challenges within the RCUK themes of "Energy Storage" and "Combustion Engineering'", indicating the strategic need to develop knowledge and skills in this field of ammonia fuelled thermal propulsion systems.

Ammonia covers almost every end-use for power generation, in stationary and mobile applications, for energy storage, transportation, and electricity markets as well as other industrial uses. Works performed by Cardiff University, Siemens, Oxford University and UK Science and Technology Funding Council led to design and commissioning of the first "Green Ammonia Decoupled" device that shows how energy from wind can be converted to ammonia for its storage and further release of energy via an internal combustion engine. "Green ammonia" is central to the mission to reduce emissions in the heavy industry (specifically cement, steel, and plastics) and heavy-duty transport (road freight, shipping, and aviation) sectors. International shipping can meet its target of at least halving its emissions by 2050 and can unleash trillions of dollars of investment opportunities in sustainable industrial infrastructure by using "green ammonia". According to the UK Department of Transport report, the sector could also achieve deep decarbonisation by 2035 with a mixture of ammonia-hydrogen fuel.

The major use of ammonia is as a fertiliser, and thus agriculture is the main source of ammonia emissions in the UK. Mitigating ammonia emissions also benefits farmers and society. This can be achieved through use of fertiliser as the fuel in tractors to run the engine on a blend of diesel and ammonia. The carbon footprint of agriculture can be further reduced in the UK by making the ubiquitous ammonia fertiliser using wind energy. By switching to renewable electricity to make ammonia we could save over 40 million tonnes of CO2 each year in Europe alone, or over 360 million tonnes worldwide.

Capital costs for ammonia technologies need to be competitive against other forms of energy storage. Estimates of the capital costs ($/kW) for ammonia energy storage (between 1350 and 1590 $/kW) indicate it will be competitive compared to battery storage technologies such as Li-ion, but with the advantage of considerably cheaper capacity costs inherent in a liquid fuel.

It is expected that development of fast tools for computational optimisation of ammonia based thermal propulsion systems will set the foundations for the creation of a strong consortium among academia and industries like Ricardo in the UK in future. According to Ricardo, the potential to develop green ammonia could provide long-term revenue and unlock investments in renewable plants in developing nations. The project results will enable the companies to employ simpler mechanisms for the study of complex reactions, thus support the research topics on renewable carbon-free fuels, including engineering an effective ammonia combustion engine solution. Moreover, successful results will enable the company to identify and support opportunities to significantly reduce combustion engine emissions impact, relevant to transportation market sector's, thereby helping to meet global climate change goals. In addition, companies like MAN solutions, Yara and Siemens already engaged in other ammonia projects could benefit from the proposed project contributions through the research findings and developed models/tools.

We believe that development of numerical tools with low computational cost to study green ammonia combustion for power generation could be a way to help the UK meet its net-zero carbon emissions targets.

Publications

10 25 50
 
Description The work has resulted in significant progress in the fundamental combustion study of ammonia based fuel blends. Machine learning models can analyse large amounts of data and provide predictions faster and more accurately than traditional models. This can help researchers and engineers design and optimise combustion systems more efficiently, saving time and resources.
Exploitation Route The outcomes of funding can be taken forward and put to use by others in various ways:

Cost savings: The machine learning models developed through the funding can be used by others to improve combustion efficiency in various industries, such as power generation, and transportation.

Further research: The outcomes of the funding can serve as a foundation for further research into combustion machine learning models, potentially leading to new discoveries and advancements in the field.

Collaboration: The funding outcomes can foster collaboration between researchers, engineers, and industry experts to share knowledge and resources, and work towards a common goal of improving combustion processes in zer-carbon fuel blends.
Sectors Energy

Transport

 
Title Chemiluminescense and Spectral Spectrum Technologies 
Description Use of various filters and spectral measurement tools to identify novel radicals such as CH, OH, NH, NH2, CO, etc. which have been employed for combustion efficiency and flame features with various ammonia-hydrogen blends. 
Type Of Material Improvements to research infrastructure 
Year Produced 2021 
Provided To Others? Yes  
Impact Use of combined chemiluminescence and spectral analyses to determine flame stability for the implementation of the technique for its use in simpler combustion stability tools. Research that has been presented in the Combustion Symposium (waiting for reviewers' feedback). 
URL https://repository.kaust.edu.sa/handle/10754/669448
 
Title Hybrid machine Learning model for prediciton of laminar flame speeds of Ammonia/Hydrogen mixtures 
Description A comprehensive experimental database was collected for laminar flame speeds of NH3/H2/air mixtures. A hybrid machine learning (ML) approach was implemented based on a training dataset consisting of both the experimental values and additional data obtained from numerical simulations with a detailed kinetic model. 
Type Of Material Computer model/algorithm 
Year Produced 2023 
Provided To Others? No  
Impact The developed hybrid ML model is seen as a promising alternative to time-consuming experimental measurements or numerical calculations of laminar flame speeds. The final optimised ML model can be integrated into an open-source CFD code to speed up the combustion modelling. 
 
Description Collaboration HU, CU and CULouvaine 
Organisation Catholic University of Louvain
Country Belgium 
Sector Academic/University 
PI Contribution Collaboration for the analysis of Tertiary Blends and their combustion features. Novel reaction mechanisms are under development as part of this award. Cardiff University is contributing with experimental data generated through the use of various ammonia/hydrogen/methane blends. The results include chemiluminescence, spectral data, emissions and thermal features of swirling flows running on a great variety of blends.
Collaborator Contribution UC Louvaine is currently working on the definition of Experimental Methodologies for the analyses of the tertiary blends, whilst they are also developing tertiary maps for emissions and spectral analyses that are expected to contribute considerably to the outcomes of the project. Hertfordshire University is currently working on novel reaction mechanisms that will be calibrated with recent data generated by Cardiff. The validated mechanism will be used for prediction of emissions and operability states of gas turbine engines running on these tertiary blends.
Impact Still to come. It is expected we can generate several papers (conferences and journals) through this partnership.
Start Year 2021
 
Description Collaboration QMUL and TU Bergakademie Freiberg 
Organisation Freiberg University Of Mining And Technology
Country Germany 
Sector Academic/University 
PI Contribution Providing our numerical dataset and ML models for prediction of laminar flame speed of binary and ternary mixtures of ammonia fuel blends
Collaborator Contribution Providing experimental dataset of binary and ternary mixtures of ammonia fuel blends for training of our ML models
Impact Data-Driven Prediction of Laminar Burning Velocity for Ternary Ammonia/Hydrogen/Methane/Air Premixed Flames, Fuel 2024
Start Year 2023