Towards the integration of mechanistic, microbial, and machine learning approaches in anaerobic digestion modelling and control: a review of current progress and implementation strategies.

Anaerobic digestion (AD) plays a pivotal role in renewable energy generation and waste valorisation, yet its optimisation is constrained by the limitations of conventional modelling and control strategies. The Anaerobic Digestion Model No.1 (ADM1) remains the most widely adopted mechanistic model. However, its high structural complexity, defined by 26 state variables and over 100 parameters, presents significant calibration challenges. These rigid biological assumptions severely constrain the applicability of the model for real-time prediction, dynamic optimisation, and process control under transient loading. This review synthesises developments across three converging research domains: (i) ADM1 extensions, modifications, and reductions, including substrate‑specific kinetics, mass‑balanced formulations, and surrogate‑based simplifications; (ii) emerging MC modelling approaches, spanning genome‑scale metabolic reconstructions, and hybrid mechanistic-data‑driven frameworks; and (iii) advanced control strategies, including model‑predictive control, soft sensing, and AI‑driven or reinforcement learning for dynamic, uncertainty‑aware operation. The critical analysis highlights that future AD optimisation depends on integrating microbial ecological insight with computationally efficient models and data‑driven intelligence to produce robust, site‑adaptive decision‑support tools. We identify key knowledge gaps and propose how to move towards a unified modelling-microbial-control framework to accelerate deployment of intelligent AD systems to improve biogas yield, stability, and resilience at industrial scale.