Artificial intelligence-driven and prediction of green biohydrogen derived from microalgae biorefinery: A review

As the global energy transition progresses, sustainable biohydrogen production is essential to reducing greenhouse gas emissions and mitigating climate change. Microalgae-based biohydrogen production is a promising renewable source that can serve as an environmentally friendly alternative to conventional hydrogen production methods. However, the challenges of microalgae-based biohydrogen production remain in improving hydrogen yield and process efficiency. This review explores both traditional and innovative approaches to biohydrogen production from microalgae. It examines biophotolysis, dark fermentation, and the use of stress conditions (e.g., sulfur deprivation) to enhance hydrogen yields. Additionally, the role of genetic engineering and microbial co-cultivation in improving hydrogen production is discussed. Artificial intelligence (AI) applications are highlighted for their role in optimizing hydrogen production by adjusting key variables in real-time. The review outlines the significant advancements in genetic modification, metabolic engineering, and AI-driven optimization, demonstrating how these techniques could be used to enhance the biohydrogen output. AI models have demonstrated the ability to predict and optimize factors such as light intensity, temperature, and nutrient levels to maximize hydrogen yield. In addition, simulation techniques using computational fluid dynamics (CFD) with Aspen Plus are utilized to assess the feasibility of large-scale biohydrogen production. AI-driven optimization and advanced biotechnological methods are crucial to overcoming current limitations and improving efficiency. Scaling up biohydrogen production will require continued innovation in reactor design, energy efficiency, and process automation. Further research is essential to fully realize the potential of microalgae as a viable source of renewable hydrogen.