Closed loop precision fermentation of circular bio-substrates: Integrating multi-omics and machine learning-assisted volatilomics

The convergence of artificial intelligence (AI), multi-omics and advanced sensing technologies and systems biology is transforming fermentation from an empirically operated process into an intelligent, closed-loop precision biomanufacturing platform. This review critically examines recent advances in AI-driven fermentation, integrating digital twins, soft sensors, volatilomics and multi-omics within a closed-loop fermentation framework. The integration of multi-modal biological and process data enables predictive modelling, adaptive feedback control and dynamic metabolic steering, resulting in improved product yield, process robustness and operational consistency. Recent advances in smart bioreactors incorporating edge computing, hybrid mechanistic-machine learning models and explainable AI enhance real-time decision making, model transferability and industrial deployment across diverse fermentation systems. The integration of circular bio-substrates, carbon-intelligent biorefineries and resource-efficient microbial conversion pathways positions fermentation as a key enabling technology for sustainable biomanufacturing and circular bioeconomy development. However, widespread industrial deployment remains constrained by heterogeneous datasets, sensor instability, limited model generalization, interoperability challenges, cybersecurity considerations, regulatory requirements and insufficient industrial validation. Overall, the convergence of AI, advanced sensing and system biology is expected to enable autonomous, adaptive and intelligent cyber-physical production systems that support resilient, carbon-efficient and economically sustainable circular biorefineries.