Advances in Machine Learning-Driven Optimization and Applications in the Organic Solid Waste Composting Process

The global imperative for sustainable waste management has positioned composting as a critical technology for converting organic solid waste (OSW) into value-added resources, thereby playing a pivotal role in achieving carbon neutrality. However, the efficacy of conventional composting is frequently compromised by a reliance on empirical judgment, resulting in suboptimal process control, prolonged treatment durations, and inconsistent product quality that restricts high-value applications. This review presents a comprehensive synthesis of the transformative integration of machine learning (ML) across the entire OSW composting value chain, spanning from initial process intensification to final product valorization. Within the domain of process optimization, ML algorithms—including ensemble methods like Random Forest (RFs) and XGBoost, deep learning architectures such as Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs), and advanced time-series models—have demonstrated exceptional capabilities. These models achieve highly accurate predictions (R2 > 0.85) for dynamic critical parameters, including temperature, moisture content, and the C/N ratio, by effectively modeling complex, non-linear physicochemical interactions. Such predictive insight facilitates proactive, automated control strategies, such as dynamic aeration adjustment, which significantly outperform reactive, schedule-based approaches. Furthermore, ML enables data-driven microbial community engineering by analyzing metagenomic data to identify and promote key functional taxa essential for biodegradation. For compost maturity assessment, ML frameworks support rapid, non-destructive evaluation by integrating fused multi-sensor data from electronic noses and spectral sensors, or by interpreting visual features via computer vision, thus reducing the dependency on time-consuming laboratory assays. In the realm of product valorization, ML acts as a powerful enabler for precision resource recovery, facilitating the design of composts tailored for specific environmental remediation tasks, such as predicting heavy metal immobilization efficiency or modeling the degradation kinetics of organic pollutants. In sustainable agriculture, ML-driven decision support systems recommend optimal compost-soil blends based on local edaphic conditions, while simultaneously modeling mitigation pathways for biological risks, including antibiotic resistance genes (ARGs). Despite these advancements, widespread industrial implementation faces barriers, primarily the scarcity of high-quality, annotated datasets, which limits model generalizability and necessitates solutions like transfer learning. Additionally, deploying computationally intensive models on edge-computing hardware presents challenges regarding latency and sensor robustness. Finally, enhancing the interpretability of "black-box" models through Explainable AI (XAI) is essential for fostering practitioner trust. In conclusion, ML is driving a fundamental paradigm shift in OSW composting, evolving it from an artisanal practice into a data-intelligent, precision-engineering discipline. Future progress depends on developing integrated cyber-physical systems that combine robust sensing with adaptive online learning, promising to optimize the complex trade-offs inherent in waste management and enhance environmental sustainability at scale.