Anaerobic digestion (AD) is a triple-benefit biotechnology for organic waste treatment, renewable production, and carbon emission reduction. In the process of anaerobic digestion, pH, temperature, organic load, ammonia nitrogen, VFAs, and other factors affect fermentation efficiency and stability. The balance between the generation and consumption of volatile fatty acids (VFAs) in the anaerobic digestion process is the key to stable AD operation. However, the accumulation of VFAs frequently occurs, especially propionate, because its oxidation has the highest Gibbs free energy when compared to other VFAs. In order to solve this problem, some strategies, including buffering addition, suspension of feeding, decreased organic loading rate, and so on, have been proposed. Emerging methods, such as bioaugmentation, supplementary trace elements, the addition of electronic receptors, conductive materials, and the degasification of dissolved hydrogen, have been recently researched, presenting promising results. But the efficacy of these methods still requires further studies and tests regarding full-scale application. The main objective of this paper is to provide a comprehensive review of the mechanisms of propionate generation, the metabolic pathways and the influencing factors during the AD process, and the recent literature regarding the experimental research related to the efficacy of various strategies for enhancing propionate biodegradation. In addition, the issues that must be addressed in the future and the focus of future research are identified, and the potential directions for future development are predicted.

Biochar's potential as a sustainable solution for agricultural and environmental management depends on its capacity to retain nutrients and sequester carbon. However, accurately predicting biochar yield and nutrient content, particularly nitrogen (N), phosphorus (P), and potassium (K), remains a significant challenge. This study addressed this issue by applying advanced machine learning models to predict biochar properties based on biomass characteristics and pyrolysis conditions. The models included Support Vector Regression (SVR), Random Forest (RF), Back Propagation Artificial Neural Network (BP-ANN), and Extreme Gradient Boosting (XGBoost). Analysis of 271 datasets, augmented with random noise injection for data augmentation, revealed that XGBoost was the most reliable model, achieving an average R² of 0.97 for predicting biochar yield and elemental compositions. Key findings indicate that pyrolysis temperature is the primary determinant of biochar yield, while feedstock composition plays a critical role in nutrient retention. Additionally, a novel graphical user interface (GUI) was developed to translate these computational insights into practical applications, bridging the gap between complex data analysis and real-world agricultural and environmental management. This research offers a robust, data-driven framework for optimizing biochar production and enhancing its role in sustainable agriculture and environmental conservation. Graphical Abstract