Artificial intelligence driven microalgae based green fabrication and bioenergy systems for sustainable energy materials and biowaste valorization

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:This paper provides a comprehensive overview of AI techniques for microalgae bioenergy systems, highlighting predictive modeling and control strategies that can guide future research.

🏢実務担当者:Companies in biofuel or biowaste valorization can leverage AI optimization to improve process efficiency and reduce costs in photobioreactor operations.

🏛政策担当者:Policymakers can note the potential of AI-enabled algae systems to contribute to renewable energy and waste management targets, supporting circular economy policies.

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.

• semanticscholar https://doi.org/10.3389/fchem.2026.1858141first seen 2026-06-27 05:26:38