ArticleFrontiers in chemistry2026
Artificial intelligence driven microalgae based green fabrication and bioenergy systems for sustainable energy materials and biowaste valorization.
Article in Frontiers in chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
In recent years, the bioenergy domain has experienced substantial advancement, largely driven by the integration of advanced technologies such as artificial intelligence (AI) and machine learning (ML), particularly in optimizing microalgae-based systems for biofuel production and sustainable biowaste conversion. AI techniques, including support vector machines (SVM) and artificial neural networks (ANN), have demonstrated strong capabilities in modelling complex nonlinear relationships, enabling improved prediction of process parameters and enhanced system performance. In microalgal bioenergy systems, ANN-based models have achieved high predictive accuracy, with coefficients of determination exceeding 0.93, facilitating efficient biomass production, pollutant removal, and resource optimization. Beyond biofuel generation, microalgal biomass represents a promising renewable feedstock for the green fabrication of advanced energy materials, including carbon-based nanostructures and bio-derived electrodes applicable in energy storage systems such as batteries and supercapacitors. Techniques such as genetic algorithms and ANN-based control systems enable real-time optimization of photobioreactor operations, improving energy recovery efficiency and reducing operational costs. Furthermore, AI-assisted catalytic and thermochemical process have contributed to higher conversion efficiencies and improved sustainability outcomes. The integration of AI with microalgae-based bioenergy and material fabrication systems supports circular economy principles by enabling the conversion of biowaste into high value energy products and functional materials. Despite these advancements, challenges such as computational complexity, data availability, and feedstock variability remain. Addressing these issues through interdisciplinary research is essential for scaling AI-enabled bioenergy platforms. Overall, this study highlights the transformative potential of AI in advancing sustainable bioenergy systems and eco-friendly material fabrication, contributing to global decarbonization and zero-waste goals.
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