The transition to sustainable combustion: Hydrogen- and carbon-based future fuels and methods for dealing with their challenges

Abstract

While the world is already facing substantial impacts of global warming, the transition towards a sustainable-energy future is slow because of the sheer scale of global energy needs that are presently satisfied mostly by the combustion of fossil fuels. Chemical energy carriers are likely to play an essential role in future energy systems, where harvesting and utilization of renewable energy occur not necessarily at the same time or place, hence long-time storage and long-range transport of energy are needed. For this, hydrogen-based chemical energy carriers, such as hydrogen and ammonia, will play a very important role in future energy systems. Furthermore, there is significant promise in carbon-based fuels made from upgrading CO2, lignocellulosic biomass, or a combination of both with renewable electricity-derived hydrogen, yielding electro-fuels, biofuels, and bio-hybrid fuels, respectively. The utilization of these future fuels by combustion-based energy conversion has many advantages, e.g., versatile use for heat and power, robust and flexible technologies, and suitability for a continuous energy transition. However, there are also challenges, which need to be addressed and will be discussed in the present paper. Hydrogen-based fuels are well known to possess combustion properties very different from conventional fuels, such as the occurrence of intrinsic flame instabilities for lean premixed flames, which can lead to a several-fold increase of the consumption speeds for a wide range of conditions. Bio-hybrid fuels show enormous molecular diversity allowing for a task-specific optimization of the fuel structure, which however, call for a fuel-design methodology based on quantitative fuel-structure/property relationships. The energy transition requires substantial adjustments of combustion devices and processes for future fuels to ensure clean, safe, efficient, and fuel-flexible combustion, which will have to be accomplished relatively quickly. Computational methods are a vital element of modern design and optimization with particular importance when rapid developments are required or complex objectives are pursued. Yet, because of the highly non-linear nature and the complexities associated with combustion and the resulting difficulties in the development of predictive models, this also requires a transition to new methods. Recently, machine-learning-based methods have been embraced as an important new pillar in modeling, especially for situations where physics-based approaches have reached maturity, but are still limited in accuracy and applicability. Some interesting examples of machine-learning approaches in combustion model development will be discussed.

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