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ML-driven search for zero-emissions ammonia production materials

The International Conference on Machine Learning, Workshop on Climate Change (2019)

Abstract

Ammonia (NH3) production is an industrial process that consumes between 1-2% of global energy annually and is responsible for 2-3% of greenhouse gas emissions. Ammonia is primarily used for agricultural fertilizers, but it also conforms to the US DOE targets for hydrogen storage materials. Modern industrial facilities use the century-old Haber-Bosch process, whose energy usage and carbon emissions are strongly dominated by the use of methane as the combined energy source and hydrogen feedstock, not by the energy used to maintain elevated temperatures and pressures. Generating the hydrogen feedstock with renewable electricity through water electrolysis is an option that would allow retrofitting the billions of dollars of invested capital in Haber-Bosch production capacity. Economic viability is however strongly dependent on the relative regional prices of methane and renewable energy; renewables have been trending lower in cost but forecasting methane prices is difficult. Electrochemical ammonia production, which can use aqueous or steam H2O as its hydrogen source (first demonstrated ˜20 years ago) is a promising means of emissions-free ammonia production. Its viability is also linked to the relative price of renewable energy versus methane, but in principle it can be significantly more cost-effective than Haber-Bosch and also downscale to developing areas lacking ammonia transport infrastructure. However to date it has only been demonstrated at laboratory scales with yields and Faradaic efficiencies insufficient to be economically competitive. Promising machine-learning approaches to fix this are discussed.

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