AIMC Topic: Waste Disposal, Fluid

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Attention-based deep learning models for predicting anomalous shock of wastewater treatment plants.

Water research
Quickly grasping the time-consuming water quality indicators (WQIs) such as total nitrogen (TN) and total phosphorus (TP) of influent is an essential prerequisite for wastewater treatment plants (WWTPs) to prompt respond to sudden shock loads. Soft d...

Optimizing papermaking wastewater treatment by predicting effluent quality with node-level capsule graph neural networks.

Environmental monitoring and assessment
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...

Application of supervised learning models for enhanced lead (II) removal from wastewater via modified cellulose nanocrystals (CNCs).

Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Heavy metal ions are acknowledged to impact the environment and human health adversely. CNCs are effective materials for removing heavy metal ions in industrial applications and process innovations since they can be used in static and dynamic adsorpt...

Enhanced prediction of partial nitrification-anammox process in wastewater treatment by developing an attention-based deep learning network.

Journal of environmental management
In the process of partial nitrification and anaerobic ammonia oxidation (anammox) for nitrogen removal, the process offers simple metabolic pathways, low operating costs, and high nitrogenous loading rates. However, since the partial nitrification-an...

How small is big enough? Big data-driven machine learning predictions for a full-scale wastewater treatment plant.

Water research
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
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
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
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
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
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...