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A reduction in greenhouse gases has become an inescapable requirement. An effective scenario for achieving carbon neutrality is to develop a hydrogen economy. Its success, however, requires strict control of the different processes involved in planned hydrogen chains. The energy chain considered in this paper is a stationary application which involves the production of hydrogen by electrolysis (a power-to gas process) and its combustion in gas turbine combined cycles to generate electricity (a gas-to-power process). In such applications, the need is twofold: (i) to control the risk of explosive atmospheres by performing safe gas detection in the presence of hydrogen and (ii) to secure the reliability of all chain processes using hydrogen-rich gases by achieving reliable analyses of these gases. This paper is dedicated to the development of hydrogen energy to decarbonize the thermal production of electricity. We will first describe the hydrogen chain that would best suit the power generation sector. Then, we will highlight the properties of hydrogen that are critical for its reliable operation. Finally, we will review the sensing technologies suitable for hydrogen-containing fuels. This review paper was published as part of a Joint Industrial Project (JIP) aimed at enabling the safe and reliable deployment of hydrogen energy. [ABSTRACT FROM AUTHOR]

Hydrogen-compatible gas turbines are one way to decarbonize electricity production. However, burning and handling hydrogen is not trivial because of its high reactivity and tendency to detonate. Mandatory safety parameters, such as auto-ignition delay times, can be estimated thanks to predictive detailed kinetic models, but with significant calculation times that limit coupling with fluid mechanic codes. An auto-ignition prediction tool was developed based on an artificial intelligence (AI) model for fast computations and an implementation into an explosion model. A dataset of ignition delay times (IDTs) was generated automatically using a recent detailed kinetic model from National University of Galway (NUIG) selected from the literature. Generated data cover a wide operating range and different compositions of fuels. Clustering problems in sample points were avoided by a quasi-random Sobol sequence, which covers uniformly the entire input parameter space. The different algorithms were trained, cross-validated, and tested using a database of more than 70,000 ignitions cases of natural gas/hydrogen blends calculated with the full kinetic model by using a common split of 70/30 for training, testing. The AI model shows a high degree of robustness. For both the training and testing datasets, the average value of the correlation coefficient was above 99.91%, and the mean absolute error (MAE) and the mean square error (MSE) were around 0.03 and lower than 0.04, respectively. Tests showed the robustness of the AI model outside the ranges of pressure, temperature, and equivalence ratio of the dataset. A deterioration is, however, observed with increasing amounts of large alkanes in the natural gas. [ABSTRACT FROM AUTHOR]