Physico-chemical aspects determine the efficiency and competitiveness of hydrogen production technologies. The indicator of water consumption is especially relevant, since water is one of the main sources of hydrogen in almost all methods of its production. The article analyzes comparative water consumption indicators for various technologies based on published research and actual data from production plants. The volume of water consumption depends on the quality of the source water, which should be taken into account when implementing hydrogen projects in order to minimize the negative impact on the environment. Based on the operating industrial plant, the material balance of hydrogen production by steam reforming was demonstrated, which made it possible to determine the proportion of hydrogen (48.88 %) obtained from water. Currently, the carbon footprint indicator is becoming more important, reflecting greenhouse gas emissions throughout the production chain. According to the results of the total greenhouse gas emissions assessment for hydrogen production by steam reforming (about 10.03 kg CO2-eq/kg H2), the carbon footprint of hydrogen from water (4.2-4.5 kg CO2-eq/kg H2) and hydrogen from methane (15.4-15.7 kg CO2-eq/kg H2) has been determined. Consequently, almost half of the hydrogen produced by steam reforming is produced from water, corresponds to the indicators of “low-carbon” hydrogen and can be considered as “renewable” hydrogen. To make management decisions, an objective assessment in terms of energy and water costs is necessary based on a system analysis by the development of hydrogen energy and the growth of global hydrogen production. The impact of these indicators on the water cycle and global water resources will increase.
This study argues that human error has an effect on occupational injury risks in mining companies. It shows through an analysis of existing approaches to occupational risk assessment that it is necessary to develop a quantitative assessment method factoring in individual psychophysiological attributes in order to analyze injury risks posed to miners. The article presents the results of a comprehensive analysis of how workers’ psychophysiological attributes influence their susceptibility to occupational injuries in underground mining conditions. By utilizing statistical data processing methods, such as discriminant and regression analysis, the study develops models to forecast personal injury risks among miners. These quantitative models underlie the proposed method for assessing miners’ susceptibility to injuries. The study outlines an algorithm for the practical application of this method and shows how the method was validated using a training sample. It provides recommendations for managing the human factor, incorporating the results of the proposed method, and emphasizes the importance of implementing a series of protective measures to mitigate the risk of occupational injuries in underground mining operations.
