Uncertainty quantification in hydrogen production systems design: A review

The increasing demand for hydrogen as an energy carrier highlights the need for efficient and scalable production systems. The performance and feasibility of these technologies are, however, strongly influenced by uncertainties in system parameters, operational conditions, and model inputs, which often limit large-scale deployment. This review provides a critical evaluation of uncertainty quantification techniques applied to hydrogen production systems. We examine key computational modelling approaches that integrate uncertainty quantification methods to improve predictive accuracy, guide process optimization, and support informed decision-making. Despite these advances, it is clearly indicative that many studies continue to employ simplified probability distributions due to the scarcity of high-quality empirical data, particularly for emerging technologies such as solid oxide electrolysis cells, photoelectrochemical systems, and advanced biomass gasifiers. Also, a lack of standardized uncertainty protocols and inconsistencies in hydrogen production modeling assumptions present challenges to reproducibility and cross-study comparison. Addressing this limitation requires greater emphasis on experimental validation and systematic data collection, which will strengthen the credibility of model-based insights and accelerate the scale-up of hydrogen production technologies. Harmonizing modeling frameworks and reporting practices across the hydrogen research community will also be critical to improving benchmarking and ensuring that findings translate effectively into investment and policy strategies. To this end, we aim to highlight pathways toward more reliable, cost-effective, and sustainable hydrogen production systems.