AIMC Topic: Biological Oxygen Demand Analysis
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Machine learning-based optimization of biogas and methane yields in UASB reactors for treating domestic wastewater.
Biodegradation
Jun 26, 2025
This study aimed to optimize biogas and methane production from Up-flow anaerobic sludge blanket reactors for treating domestic wastewater using advanced machine learning models-namely, eXtreme Gradient Boosting (XGBoost) and its hybridized form, XGB...
Enhanced nitrogen prediction and mechanistic process analysis in high-salinity wastewater treatment using interpretable machine learning approach.
Bioresource technology
Mar 11, 2025
This study introduces an interpretable machine learning framework to predict nitrogen removal in membrane bioreactor (MBR) treating high-salinity wastewater. By integrating Shapley additive explanations (SHAP) with Categorical Boosting (CatBoost), we...
Predicting biomass conversion and COD removal in wastewater treatment by phototrophic bacteria with interpretable machine learning.
Journal of environmental management
Jan 24, 2025
Photosynthetic bacteria (PSB) excel in wastewater treatment by removing pollutants and generating biomass but are challenging to optimize due to complex operational and environmental interactions. Neural Ordinary Differential Equations, Elastic Net, ...
Optimizing papermaking wastewater treatment by predicting effluent quality with node-level capsule graph neural networks.
Environmental monitoring and assessment
Jan 18, 2025
Papermaking wastewater consists of a sizable amount of industrial wastewater; hence, real-time access to precise and trustworthy effluent indices is crucial. Because wastewater treatment processes are complicated, nonlinear, and time-varying, it is e...
A hybrid deep learning model based on signal decomposition and dynamic feature selection for forecasting the influent parameters of wastewater treatment plants.
Environmental research
Dec 12, 2024
Accurate prediction of influent parameters such as chemical oxygen demand (COD) and biochemical oxygen demand over five days (BOD) is crucial for optimizing wastewater treatment processes, enhancing efficiency, and reducing costs. Traditional predict...
Hybrid modeling techniques for predicting chemical oxygen demand in wastewater treatment: a stacking ensemble learning approach with neural networks.
Environmental monitoring and assessment
Nov 27, 2024
To ensure operational efficiency, promote sustainable wastewater treatment practices, and maintain compliance with environmental regulations, it is crucial to evaluate the parameters of treated effluent in wastewater treatment plants (WWTPs). Artific...
Interpretable causal machine learning optimization tool for improving efficiency of internal carbon source-biological denitrification.
Bioresource technology
Nov 8, 2024
Interpretable causal machine learning (ICML) was used to predict the performance of denitrification and clarify the relationships between influencing factors and denitrification. Multiple models were examined, and XG-Boost model provided the best pre...
Machine learning for enhancing prediction of biogas production and building a VFA/ALK soft sensor in full-scale dry anaerobic digestion of kitchen food waste.
Journal of environmental management
Nov 5, 2024
Based on operational data collected over 1.5 years from four full-scale dry anaerobic digesters used for kitchen food waste treatment, this study adopted eight typical machine learning algorithms to distinguish the best biogas prediction model and to...
Predicting photosynthetic bacteria-derived protein synthesis from wastewater using machine learning and causal inference.
Bioresource technology
Oct 15, 2024
Causal inference-assisted machine learning was used to predict photosynthetic bacterial (PSB) protein production capacity and identify key factors. The extreme gradient boosting algorithm effectively predicted protein content, while the gradient boos...
Application of machine learning in ultrasonic pretreatment of sewage sludge: Prediction and optimization.
Environmental research
Oct 5, 2024
In this research, typical industrial scenarios were analyzed optimized by machine learning algorithms, which fills the gap of massive data and industrial requirements in ultrasonic sludge treatment. Principal component analysis showed that the ultras...
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