Key Takeaways & Executive Findings
- •• • The stacking ensemble model (XGBoost + SVR base learners, ElasticNet meta-learner) achieved R² = 0.928, RMSE = 0.958, and MAE = 0.691 on the test set, significantly outperforming single models, indicating robust generalization for AP prediction in composting. • • SHAP global importance ranked feedstock type, bulking agent type, turning interval, pH, EC, and C/N as the top six factors; livestock manure and straw-based bulking agents showed significant positive contributions to AP, guiding feedstock selection for phosphorus-rich compost. • • PDP analysis revealed that maintaining pH and C/N at relatively low levels throughout composting favors AP accumulation; specifically, during the cooling/maturation phase, moisture <48% and C/N <14 with composting duration >43 days enhanced AP, providing operational thresholds for process optimization. • • The model's predictive accuracy (R² > 0.92) enables rapid estimation of AP without lengthy experiments, reducing time and cost in composting process design and enabling real-time adjustments to maximize phosphorus availability.
Abstract
Organic waste is a potential phosphorus reservoir, and understanding the dynamics of available phosphorus (AP) during its resource utilization is critical for efficient phosphorus recovery. Composting, a key route for organic waste valorization, involves complex transformations of phosphorus alongside organic matter degradation and humification. However, the long duration and high cost of composting experiments, coupled with multifactorial influences, hinder efficient elucidation of AP dynamics via conventional methods. This study compiled data from 33 publications, constructing a dataset of 647 samples. Data preprocessing included iterative imputation, one-hot encoding, and standardization. A stacking ensemble learning model was developed to predict AP generation during composting. The optimal ensemble comprised XGBoost and SVR as base learners and ElasticNet as the meta-learner, achieving R² values of 0.954 and 0.928 on training and test sets, respectively, with low overall error. SHAP analysis revealed that key factors influencing AP content, in descending order of importance, were feedstock type, bulking agent type, turning interval, pH, electrical conductivity (EC), and C/N ratio. Notably, livestock manure as feedstock and straw-based bulking agents contributed positively to AP predictions. Partial dependence plots indicated that lower pH and C/N ratios generally favored AP accumulation throughout composting. During the initial stage, higher moisture content and lower EC enhanced AP; in the thermophilic phase, higher temperatures corresponded to higher AP; and during cooling and maturation, maintaining moisture below 48% and C/N below 14, while extending composting beyond 43 days, promoted AP accumulation. This study demonstrates accurate AP prediction via stacking ensemble learning and identifies critical factors, offering support for optimizing phosphorus management in composting engineering.
1. Introduction
Phosphorus is a finite and non-substitutable resource, yet substantial quantities are embedded in organic waste streams, which are often underutilized. Composting offers a sustainable pathway to stabilize organic matter and recycle nutrients, but the transformation of phosphorus into plant-available forms (available phosphorus, AP) is governed by a complex interplay of feedstock characteristics, process conditions, and microbial activity. Traditional experimental approaches to map AP dynamics are constrained by long cycle times (often weeks to months) and high operational costs, limiting the number of conditions that can be practically tested. Consequently, there is a pressing need for predictive tools that can rapidly estimate AP outcomes across diverse composting scenarios, enabling process optimization and phosphorus recovery efficiency.
This study addresses this bottleneck by leveraging a stacking ensemble learning framework, which integrates multiple machine learning algorithms to capture nonlinear relationships in a compiled dataset of 647 samples from 33 published studies. Unlike conventional single-model approaches, stacking combines the strengths of diverse base learners (XGBoost and SVR) with a meta-learner (ElasticNet) to enhance predictive accuracy and robustness. Furthermore, model interpretability is provided through SHAP and partial dependence plots, which not only rank the importance of process variables but also reveal the direction and magnitude of their effects. This dual focus on prediction and interpretation offers a practical tool for engineers to identify optimal composting conditions for maximizing AP, thereby advancing phosphorus recovery from organic waste.
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DAI Xu, YANG Xiaofan, QU Jing, ZHANG Meng, GAO Xiaozhong, CHENG Xiang, YANG Tianxue, SUN Dezhi (2026). Prediction of Available Phosphorus Formation and Analysis of Key Influencing Factors during Organic Waste Composting Using Stacking Ensemble Learning. Journal of Environmental Engineering Technology. https://doi.org/10.13205/j.hjgc.202608006
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Frequently Asked Questions
How does the stacking model handle the heterogeneity of data from different literature sources, and what measures were taken to ensure data comparability?
The study compiled data from 33 publications, which inherently vary in experimental protocols and measurement methods. To mitigate heterogeneity, iterative imputation was used for missing values, and categorical variables such as feedstock and bulking agent types were one-hot encoded. Standardization was applied to continuous features to ensure uniform scaling. However, the authors acknowledge that residual heterogeneity may affect model robustness, and they recommend future work to incorporate raw experimental data with complete process information and to perform external validation with independent datasets.
What are the specific operational thresholds for maximizing available phosphorus during the cooling and maturation phase, and how were these derived?
Partial dependence analysis indicated that during the cooling and maturation phase, maintaining moisture content below 48% and C/N ratio below 14, while extending composting duration beyond 43 days, promotes AP accumulation. These thresholds were derived from the PDP plots, which show the marginal effect of each variable on AP predictions while holding other variables constant. The results suggest that drier conditions and lower nitrogen availability relative to carbon favor phosphorus availability, possibly due to reduced immobilization and enhanced mineralization.
Why were XGBoost and SVR chosen as base learners, and what advantage does the ElasticNet meta-learner provide?
XGBoost is a gradient boosting algorithm that excels at capturing complex nonlinear interactions and handling mixed data types, while SVR is effective in high-dimensional spaces and robust to outliers. Their combination as base learners provides diversity in model predictions. ElasticNet, as a meta-learner, performs linear regression with both L1 and L2 regularization, which helps prevent overfitting and selects relevant features from the base learners' outputs. This stacking architecture improved test set R² to 0.928, compared to individual models, demonstrating enhanced generalization.
How can the SHAP analysis results be used to guide feedstock selection in composting operations?
SHAP global importance ranked feedstock type as the most influential factor on AP. Specifically, livestock manure as feedstock showed a significant positive contribution to AP predictions, while straw-based bulking agents also positively influenced AP. This suggests that co-composting manure with straw can enhance phosphorus availability. Operators can prioritize these materials to produce compost with higher agronomic value, potentially reducing the need for synthetic phosphorus fertilizers.
What are the limitations of the model in terms of external validity, and what steps are suggested for future research?
The model was trained on literature data, which may not fully represent all composting conditions, and lacks external validation with independent experimental data. The authors note that differences in measurement methods and experimental scales across studies could affect data comparability and model robustness. Future research should focus on obtaining raw experimental data with detailed process information and conducting external validation experiments to confirm the model's predictive performance and enhance its applicability to real-world composting systems.
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