Applications of Computer Intelligence in Hydrogen Production.

Area of Science:

• Energy Science
• Chemical Engineering
• Artificial Intelligence

Background:

• Growing demand for sustainable energy solutions due to environmental concerns and fossil fuel depletion.
• Hydrogen is a key alternative fuel due to its low carbon emissions, high energy density, and role in renewable energy storage.
• Current hydrogen production methods require optimization for efficiency and cost-effectiveness.

Purpose of the Study:

• To review various hydrogen production methods.
• To evaluate the integration of machine learning (ML) in hydrogen production processes.
• To analyze the impact of ML on key performance indicators (KPIs) of hydrogen production.

Main Methods:

• Comprehensive literature review of hydrogen production techniques (water splitting, hydrocarbon reforming, biological decomposition).
• Analysis of machine learning algorithms applied to optimize production parameters.
• Examination of ML's role in predictive modeling, real-time monitoring, and adaptive control systems.

Main Results:

• Machine learning significantly improves key performance indicators such as hydrogen yield, gas quality, and production efficiency.
• Intelligent algorithms enable optimization of operational parameters for enhanced energy conversion.
• ML integration leads to more sustainable and economically viable hydrogen production.

Conclusions:

• Machine learning is crucial for optimizing hydrogen production processes.
• ML enhances both the sustainability and economic feasibility of hydrogen as a clean energy source.
• The study highlights the transformative potential of AI in advancing clean energy technologies.