ArticleEnvironmental science & technology2025
Biochar-Augmented Anaerobic Digestion System: Insights from an Interpretable Stacking Ensemble Deep Learning.
Article in Environmental science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
This study presents a comprehensive approach for optimizing biochar-augmented anaerobic digestion (AD) system through an interpretable stacking ensemble deep learning model. Extensive experimental data were compiled, incorporating feedstock characteristics, operational conditions, and biochar properties, alongside stability indicators such as pH, volatile fatty acid concentrations, alkalinity, and total ammonia nitrogen levels. The proposed model integrates different configurations of convolutional neural networks and long short-term memory networks within a stacking ensemble framework, effectively capturing complex interdependencies within the AD process and improving methane yield predictions. Optimized through advanced hyperparameter tuning, the model achieved high internal predictive accuracy (mean
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