AI-Driven Digital Twins for Optimization and Resilience of Bioenergy Systems

Bioenergy systems, including biomass, biogas, and waste-to-energy technologies, are increasingly recognized as key components of sustainable energy transitions. However, their operation remains inherently complex due to feedstock variability, nonlinear conversion processes, and challenges associated with integration into modern energy systems. Traditional control and optimization approaches often lack the flexibility required to handle dynamic operating conditions and multi-source energy interactions.

Recent advances in artificial intelligence (AI), digitalization, and data-driven modeling are creating new opportunities to enhance the performance, reliability, and scalability of bioenergy systems. In particular, digital twin technologies, combined with machine learning-based forecasting and real-time monitoring, enable improved system visibility, predictive maintenance, and adaptive decision-making. These developments support more efficient operation and facilitate the integration of bioenergy systems within smart grids and hybrid energy networks.

This Research Topic aims to promote the development of AI-driven and digital twin-enabled frameworks for optimizing and enhancing the resilience of bioenergy systems. The focus is on intelligent, data-driven approaches that support real-time monitoring, predictive analytics, and adaptive control across multiple spatial and temporal scales. Key challenges include managing feedstock uncertainty, improving conversion efficiency, ensuring reliable system operation, and enabling effective integration with broader energy infrastructures. Advances in machine learning, deep learning, reinforcement learning, and digital twin technologies provide promising pathways to address these challenges.

We welcome Original Research Articles, Review Articles, Mini-Reviews, and Perspectives addressing innovative methodologies and real-world applications in AI-enabled bioenergy systems. Topics of interest include, but are not limited to:
• Digital twin development and applications for bioenergy systems;
• AI-based modeling, forecasting, and optimization of bioenergy processes;
• Predictive maintenance and real-time monitoring of bioenergy facilities;
• Integration of bioenergy systems with smart grids and hybrid energy systems;
• Adaptive control strategies for biomass conversion, biogas, and waste-to-energy processes;
• Techno-economic and sustainability assessment of AI-enabled bioenergy systems;
• Scalability, industrial implementation, and deployment challenges.

Submissions that demonstrate scalable, data-driven, and real-world solutions are particularly encouraged. This Research Topic aims to support the transition toward intelligent, resilient, and integrated bioenergy systems that contribute to decarbonization and sustainable energy infrastructures.