Hydrogen production via water electrolysis and renewable electricity is expected to play a pivotal role as an energy carrier in the energy transition. This fuel emerges as the most environmentally sustainable energy vector for non-electric applications and is devoid of CO₂ emissions. However, an electrolyzer’s infrastructure relies on scarce and energy-intensive metals such as platinum, palladium, iridium (PGM), silicon, rare earth elements, and silver. Under this context, this paper explores the exergy cost, i.e., the exergy destroyed to obtain one kW of hydrogen. We disaggregated it into non-renewable and renewable contributions to assess its renewability. We analyzed four types of electrolyzers, alkaline water electrolysis (AWE), proton exchange membrane (PEM), solid oxide electrolysis cells (SOEC), and anion exchange membrane (AEM), in several exergy cost electricity scenarios based on different technologies, namely hydro (HYD), wind (WIND), and solar photovoltaic (PV), as well as the different International Energy Agency projections up to 2050. Electricity sources account for the largest share of the exergy cost. Between 2025 and 2050, for each kW of hydrogen generated, between $1.38$ and 1.22 kW will be required for the SOEC-hydro combination, while between $2.9$ and 1.4 kW will be required for the PV-PEM combination. A Grassmann diagram describes how non-renewable and renewable exergy costs are split up between all processes. Although the hybridization between renewables and the electricity grid allows for stable hydrogen production, there are higher non-renewable exergy costs from fossil fuel contributions to the grid. This paper highlights the importance of non-renewable exergy cost in infrastructure, which is required for hydrogen production via electrolysis and the necessity for cleaner production methods and material recycling to increase the renewability of this crucial fuel in the energy transition.

Chemical kinetics analysis of NO and CO formation based on thermodynamic data from an ethanol-fueled engine computational model / Análise de cinética química da formação de NO e CO baseada em dados termodinâmicos de um modelo computacional de um motor alimentado a etanol