Proactive detection, prediction, and control of instabilities in anaerobic digestion systems

Anaerobic digestion (AD) plays a crucial role in converting organic waste into renewable energy. However, system instability often occurs when co-digesting food wastes with animal manure or sewage sludge, disrupting the digestion process. Early detection, prediction, and mitigation of these instabilities are essential for the efficient operation of commercial AD systems. This review provides comprehensive insights into methods for detecting instability in AD, covering chemical, biological, and physical approaches. Various mitigation strategies, including biological enhancements, biochemical environment control, and physical intervention techniques are discussed. Additionally, the review explores the mechanisms behind AD instability, such as acidification and ammonia nitrogen inhibition, along with strategies for predicting and managing these disruptions. The potential of machine learning (ML) and artificial intelligence (AI) in enhancing instability management is also highlighted, focusing on rapid response to early warning signals, precise predictions, and targeted interventions. These approaches are expected to reduce system failures in AD and advance the development of automated industrial AD systems.