AIMC Topic: Waste Disposal, Fluid
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How small is big enough? Big data-driven machine learning predictions for a full-scale wastewater treatment plant.
Water research
Dec 25, 2024
Wastewater treatment plants (WWTPs) generate vast amounts of water quality, operational, and biological data. The potential of these big data, particularly through machine learning (ML), to improve WWTP management is increasingly recognized. However,...
Alternative assessment of machine learning to polynomial regression in response surface methodology for predicting decolorization efficiency in textile wastewater treatment.
Chemosphere
Dec 20, 2024
This study investigated the potential of machine learning (ML) as a substitute for polynomial regression in conventional response surface methodology (RSM) for decolorizing textile wastewater via a UV/HO process. While polynomial regression offers li...
Optimizing carbon source addition to control surplus sludge yield via machine learning-based interpretable ensemble model.
Environmental research
Dec 17, 2024
Appropriate carbon source addition can save operational costs and reduce surplus sludge yield in the wastewater treatment plant (WWTP). However, the link between carbon source and surplus sludge yield remains neglected although machine learning (ML) ...
Wastewater treatment plant site selection using advanced decision tree machine learning and remote sensing techniques.
Environmental science and pollution research international
Dec 16, 2024
Wastewater treatment plants in Coimbatore South are under pressure from rapid urbanization, inadequate infrastructure, and industrial pollution, leading to environmental and public health concerns. This study aimed to identify suitable locations for ...
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...
Evaluation of plant-based coagulants for turbidity removal and coagulant dosage prediction using machine learning.
Environmental technology
Dec 11, 2024
This study investigates the use of six plant-based coagulants - , , , , , and for the removal of turbidity from wastewater effluent. The coagulants were characterized using Scanning Electron Microscopy (SEM) to determine morphological structure, X-r...
Machine learning prediction of fundamental sewage sludge biochar properties based on sludge characteristics and pyrolysis conditions.
Chemosphere
Nov 29, 2024
Sewage sludge biochar (SSBC) has significant potential for resource recovery from sewage sludge (SS) and has been widely studied and applied across various fields. However, the variability in SSBC properties, resulting from the diverse nature of SS a...
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...
Artificial intelligence-driven assessment of critical inputs for lead adsorption by agro-food wastes in wastewater treatment.
Chemosphere
Nov 25, 2024
Due to environmental concerns and economic value, the adsorption process using agricultural wastes is one of the promising methods to remove lead (Pb) from contaminated water. The relationships between agricultural waste properties, adsorption condit...
A novel hybrid variable cross layer-based machine learning model improves the accuracy and interpretation of energy intensity prediction of wastewater treatment plant.
Journal of environmental management
Nov 15, 2024
Energy intensity (EI) prediction in wastewater treatment plants (WWTPs) suffers from inaccuracy and non-interpretability due to poor data quality, complex mechanisms and various confounding variables. In this study, the novel hybrid variable cross la...
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