Review of anaerobic digestion models for organic solid waste treatment with a focus on the fates of C, N, and P

Abstract Anaerobic digestion (AD) is a widely applied technology for renewable energy generation, environmental impact mitigation, and nutrient recycling. Despite its potential, critical gaps exist in modeling AD processes, particularly in understanding and predicting the fates of carbon (C), nitrogen (N), and phosphorus (P)—essential elements for advancing circular nutrient management. This review addresses two key questions: What are the limitations of current AD models in simulating nutrient fates, and how can future models improve these predictions? Our findings indicate that most AD models emphasize methane production, while models addressing nutrient transformations remain limited due to the complex biochemical interactions in AD systems. Mechanistic models, such as the Anaerobic Digestion Model No. 1 (ADM1), provide a foundational framework but are constrained by their complexity and the need for precise calibration, which limits scalability in larger applications. Emerging advances in artificial intelligence, particularly machine learning, offer promising solutions by enhancing model accuracy and predictive capabilities. AI-driven models enable real-time optimization and adaptive decision-making, which can expand AD applications at industrial scales. Future research should focus on integrating nutrient fate predictions with AI-driven methods to address these challenges, enhancing the role of AD in sustainable waste treatment systems.