AI-Driven Circular Biorefineries: Multi-Scale Integration for Bioenergy and Bioproducts

The depletion of fossil-based resources and the environmental impacts associated with their use represent critical challenges for both scientific research and industrial development. In this context, the transition toward a sustainable and circular bioeconomy has emerged as a key pathway, requiring innovative strategies to optimize resource utilization while minimizing waste generation and emissions.This Research Topic focuses on the development of circular biorefineries that enable the integrated production of bioenergy and high-value bioproducts from scalable and industry-relevant feedstocks, including agricultural residues, municipal organic waste, agro-industrial by-products, and lignocellulosic biomass. In particular, it highlights the role of artificial intelligence (AI) as an enabling technology for managing the complexity of such systems. AI-based approaches, including machine learning and data-driven modeling, provide powerful tools for process optimization, system integration, and real-time decision-making across multiple spatial and temporal scales.Recent advances in digitalization and intelligent process control offer new opportunities for the design, optimization, and operation of interconnected biorefinery systems. This Research Topic aims to bring together contributions addressing the integration of multiple bioconversion pathways, including anaerobic digestion, dark fermentation, biopolymer production, and other coupled conversion routes, within circular and resource-efficient frameworks. Particular emphasis is placed on process integration and cascading utilization strategies that maximize resource recovery and system efficiency.We welcome Original Research Articles, Review Articles, Mini-Reviews, and Perspectives addressing, but not limited to:• AI-driven modeling, optimization, and control of biorefinery processes;• Multi-scale system integration and digital twin applications in circular bioenergy systems;• Co-production strategies for bioenergy and high-value bioproducts;• Valorization of organic waste and scalable biomass feedstocks;• Integration of multiple bioconversion processes and cascading conversion systems;• Life cycle assessment (LCA) and techno-economic analysis (TEA) integrated with AI-driven optimization;• Development of circular value chains and integrated bio-based systems;• Scale-up challenges, pilot-scale demonstrations, and industrial case studies of biorefinery systems;• AI-enabled integration with existing industrial infrastructure and process retrofitting strategies.Particular emphasis is placed on scalable, integrated, and data-driven solutions that enhance system efficiency and sustainability. By combining advances in artificial intelligence, process engineering, and circular bioeconomy concepts, this Research Topic aims to support the development of industrially relevant and practically deployable next-generation biorefineries capable of maximizing resource efficiency while minimizing environmental impact. Topic editors: RUFIS FREGUE TAGNE TIEGAM, Julio Cesar Santos Source: https://www.frontiersin.org/research-topics/80739/ai-driven-circular-biorefineries-multi-scale-integration-for-bioenergy-and-bioproducts