AIMC Topic: Waste Disposal, Fluid
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Bayesian Optimization-Enhanced Reinforcement learning for Self-adaptive and multi-objective control of wastewater treatment.
Bioresource technology
Feb 9, 2025
Controllers of wastewater treatment plants (WWTPs) often struggle to maintain optimal performance due to dynamic influent characteristics and the need to balance multiple operational objectives. In this study, Reinforcement Learning (RL) algorithms a...
Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector - a review.
Journal of environmental science and health. Part A, Toxic/hazardous substances & environmental engineering
Feb 2, 2025
There are several uses for biomass-derived materials (BDMs) in the irrigation and farming industries. To solve problems with material, process, and supply chain design, BDM systems have started to use machine learning (ML), a new technique approach. ...
Machine learning-assisted prediction of engineered carbon systems' capacity to treat textile dyeing wastewater via adsorption technology.
Environmental monitoring and assessment
Feb 1, 2025
Dyes are widely used in industries like printing, cosmetics, paper, leather processing, textiles, and manufacturing to add color to products. However, improper disposal of dyes into wastewater has raised major concerns due to their harmful effects on...
Semi-supervised learning-based identification of the attachment between sludge and microparticles in wastewater treatment.
Journal of environmental management
Jan 31, 2025
Monitoring the microparticle transfer process in wastewater treatment systems is crucial for improving treatment performance. Supervised deep learning methods show high performance to automatically detect particles, but they rely on vast amounts of l...
Design optimization and predictive modeling for TSS in mega surface WWTPs: A machine learning approach.
Journal of environmental management
Jan 29, 2025
Surface wastewater originates from various sources, including domestic, commercial, and industrial activities, and contains a mix of organic and inorganic wastes along with suspended and dissolved solids that require effective treatment. This study p...
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, ...
Attention-based deep learning models for predicting anomalous shock of wastewater treatment plants.
Water research
Jan 23, 2025
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
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...
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
Jan 16, 2025
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
Jan 9, 2025
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...
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