An insight into the application and progress of artificial intelligence in the hydrogen production industry: A review

Abstract

The urgent need for low carbon emissions in hydrogen production has become increasingly critical as global energy demands rise, highlighting the inefficiencies in traditional methods and the industry's challenges in meeting evolving environmental standards. This review aims to provide a comprehensive overview of these challenges and opportunities. The current review discusses the use of artificial intelligence (AI) technologies, especially machine learning (ML) and deep learning (DL) algorithms, for process optimization in hydrogen production and associated power systems. The current study analyzes data from several important industry case studies and recently published studied evidence by using a review methodology in order to critically evaluate the effectiveness of AI applications. Key findings show how AI greatly improves operational efficiency through optimized production conditions and forecasted energy use. The review indicates that real-time data processing by AI helps to quickly detect anomalies for timely correction, minimizing downtimes and maximizing reliability. Integrating AI with energy management solutions not only optimizes hydrogen production but also supports a transition to sustainable energy systems. Thus, the current review recommends strategic investments in AI technologies and comprehensive training programs to harness their full potential, ultimately contributing to a more sustainable energy future.