Work

From data-driven prediction to closed-loop intelligent bioconversion: A framework for artificial intelligence in organic solid waste treatment

Year: 2026

Type: article

Abstract: Organic solid waste (OSW) bioconversion has become a key strategy for converting biodegradable wastes into renewable energy, stabilized products, and recoverable nutrients. However, aerobic composting and anaerobic digestion are inherently dynamic and heterogeneous biological processes, where substrate variability, microbial succession, mass transfer, process instability, and greenhouse gas emissions complicate reliable operation. Current AI applications span methane-yield and compost-maturity prediction, soft sensing, process optimization, digital twins, and simulation-based control modeling. Nevertheless, most existing applications remain confined to prediction, optimization, or advisory functions and lack direct integration with on-site physical feedback. In this critical review, we systematically evaluate the current capability distribution and deployment maturity of AI-enabled OSW bioconversion, while distinguishing demonstrated implementations from a proposed pathway toward closed-loop operational intelligence. Among 85 direct AI/model implementation studies, 38 primarily addressed prediction, diagnosis, or soft sensing, 36 addressed optimization or advisory decision support, and 11 addressed simulation-based control modeling; none demonstrated supervisory/semi-autonomous or fully closed-loop operation on physical OSW-bioconversion equipment. Particular attention is paid to process-state representation, mechanism-informed interpretation, sensing infrastructure, predictive and optimization models, deployment maturity, and the persistent gap between model outputs and implementable control actions. We further propose a normative cyber–physical framework that integrates multi-source sensing, state reconstruction, world modeling, decision optimization, actuation, and feedback updating. This review reframes AI in OSW bioconversion as an engineering tool for carbon mitigation, nutrient recovery, and circular waste infrastructure rather than only a means of improving prediction accuracy.

Source: Renewable and Sustainable Energy Reviews

DOI: 10.1016/j.rser.2026.117607