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<p>Microalgal biophotolysis has emerged as a promising green hydrogen production pathway due to its simultaneous potential for renewable energy generation, carbon sequestration, and wastewater remediation. However, practical implementation remains limited by oxygen sensitivity of hydrogenases, unstable hydrogenogenic phases, low photon utilization efficiency, and poor long-term operational stability. Previous studies have extensively investigated individual biophysical parameters influencing hydrogen production, including oxygen regulation, nutrient availability, light dynamics, hydrodynamics, and reactor configuration. However, these factors are often discussed independently despite their strong interdependence during real-time reactor operation. This review therefore critically integrates biological, reactor-engineering, scale-up, techno-economic, and AI/ML-assisted operational perspectives within a unified framework. The review additionally highlights that AI/ML- and simulation-assisted studies directly focused on hydrogenogenic optimization remain highly limited and largely fragmented, with very few studies integrating multiple operational and biological parameters simultaneously for predictive control of hydrogen production. Comparative analysis further indicates that co-culture-assisted oxygen scavenging, thin-layer and airlift photobioreactors, and electrochemical or bio-photovoltaic systems demonstrate comparatively improved hydrogen productivity and operational stability, although most systems remain restricted to laboratory- or pilot-scale demonstrations with comparatively low technology readiness levels. Overall, future advancement of microalgal biohydrogen will depend on integrating adaptive process control, intelligent reactor engineering, and real-time data-driven optimization to achieve scalable and commercially viable hydrogen production.</p> Graphical Abstract <p></p>

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“From Carbon Burden to Credit: Microalgae-Driven Green Hydrogen as a Climate-Positive Energy Pathway”

  • 期刊论文

  • 发表日期：

    2026年6月26日

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  • Priyanshu Prasad,
  • Balasubramanian Kandasubramanian

摘要

Microalgal biophotolysis has emerged as a promising green hydrogen production pathway due to its simultaneous potential for renewable energy generation, carbon sequestration, and wastewater remediation. However, practical implementation remains limited by oxygen sensitivity of hydrogenases, unstable hydrogenogenic phases, low photon utilization efficiency, and poor long-term operational stability. Previous studies have extensively investigated individual biophysical parameters influencing hydrogen production, including oxygen regulation, nutrient availability, light dynamics, hydrodynamics, and reactor configuration. However, these factors are often discussed independently despite their strong interdependence during real-time reactor operation. This review therefore critically integrates biological, reactor-engineering, scale-up, techno-economic, and AI/ML-assisted operational perspectives within a unified framework. The review additionally highlights that AI/ML- and simulation-assisted studies directly focused on hydrogenogenic optimization remain highly limited and largely fragmented, with very few studies integrating multiple operational and biological parameters simultaneously for predictive control of hydrogen production. Comparative analysis further indicates that co-culture-assisted oxygen scavenging, thin-layer and airlift photobioreactors, and electrochemical or bio-photovoltaic systems demonstrate comparatively improved hydrogen productivity and operational stability, although most systems remain restricted to laboratory- or pilot-scale demonstrations with comparatively low technology readiness levels. Overall, future advancement of microalgal biohydrogen will depend on integrating adaptive process control, intelligent reactor engineering, and real-time data-driven optimization to achieve scalable and commercially viable hydrogen production.

Graphical Abstract