Machine learning in hydrogen production

Abstract

Global demand for clean fuels is growing due to pollution and global warming. Undoubtedly, hydrogen is one of the main options for producing clean energy, which has been receiving special attention for years and global demand for it is constantly increasing every year. The global hydrogen market is currently valued at billions of dollars a year. Hydrogen can be used for a wide range of applications such as chemicals, food, glass, metal, etc. But one of the challenges facing the development of hydrogen production industries is the production of hydrogen from economical and eco-friendly sources and technologies. This means being able to predict the amount of hydrogen production from technology before launching it, as well as optimizing existing technologies for further production. Machine learning is widely recognized as one of the most efficient and effective tools for predicting hydrogen production. At the same time, hydrogen production technologies present important challenges that can often be addressed only with innovative machine learning algorithms and methods.