Applications of Computer Intelligence in Hydrogen Production

In response to environmental degradation and diminishing fossil fuel reserves, there is an urgent global shift toward sustainable and cleaner energy solutions. Hydrogen has gained importance as an alternative fuel due to its low carbon emissions and high combustion energy, in addition to its capacity for efficient renewable energy storage and transport. This paper presents a comprehensive review of various hydrogen production methods, including water splitting, hydrocarbon reforming, and biological decomposition, and evaluates the integration of machine learning techniques into these processes. By applying intelligent algorithms, the study examines key performance indicators, such as hydrogen yield, gas quality, production cost, and overall efficiency. By leveraging predictive modeling, real-time monitoring, and adaptive control systems, computer intelligence enables the optimization of operational parameters and improvement of energy conversion efficiencies. The findings underscore the pivotal role of machine learning in optimizing production processes, thereby enhancing both the sustainability and the economic viability of hydrogen as a clean energy source.