AIMC Topic: Water Purification
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From Model Development to Mitigation: Machine Learning for Predicting and Minimizing Iodinated Trihalomethanes in Water Treatment.
Environmental science & technology
Jun 3, 2025
Disinfection processes in water treatment produce disinfection byproducts (DBPs), such as iodinated trihalomethanes (I-THMs), which pose significant health risks. Mitigating I-THMs remains challenging due to the complex interactions among water quali...
Comparative study on antibacterial activities and removal of iron ions from water using novel modified sand with silver through the hydrothermal technique.
Scientific reports
May 8, 2025
The hydrothermal-calcination technique was used to modify raw sand with silver (Ag) at different weight percentages: 2%, 5%, and 10% using silver nitrate. The raw and sand-coated Ag nanoparticle samples were analyzed using various techniques, includi...
Novel approach for AI-based NO emission reduction in biological wastewater treatment relying on genetic algorithms and neural networks.
Water science and technology : a journal of the International Association on Water Pollution Research
May 6, 2025
The potential of measurement-based control strategies for achieving lower NO emissions in biological wastewater treatment is limited due to strong temporal variations in NO emissions and a lack of measurement data regarding influencing parameters. To...
Electrochemical activation of alum sludge for the adsorption of lead (Pb(II)) and arsenic (As): Mechanistic insights and machine learning (ML) analysis.
Bioresource technology
Apr 20, 2025
Alum sludge (AlS) has emerged as an effective adsorbent for anionic contaminants, with traditional activation methods like acid/base treatments and calcination employed to enhance its adsorption capacity. However, these approaches encounter significa...
Biohybrid microrobots with a Spirulina skeleton and MOF skin for efficient organic pollutant adsorption.
Nanoscale
Mar 24, 2025
Wastewater treatment is a key component in maintaining environmental health and sustainable urban life, and the rapid development of micro/nanotechnology has opened up new avenues for more efficient treatment processes. This work developed a novel bi...
Multi-agent large language model frameworks: Unlocking new possibilities for optimizing wastewater treatment operation.
Environmental research
Mar 14, 2025
Wastewater treatment plants (WWTPs) are highly complex systems where biological, chemical, and physical processes interact dynamically, creating significant operational challenges. Traditional modeling approaches, such as Activated Sludge Models (ASM...
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...
Prediction of chlorination degradation rate of emerging contaminants based on machine learning models.
Environmental pollution (Barking, Essex : 1987)
Mar 4, 2025
Assessing the degradation of emerging contaminants in water through chlorination is crucial for regulatory monitoring of these contaminants. In this study, we developed a machine learning model to predict the apparent second-order reaction rate const...
Data-Driven Insights into Resin Screening for Targeted Per- and Polyfluoroalkyl Substances Removal Using Machine Learning.
Environmental science & technology
Feb 11, 2025
In this study, we address the challenge of screening resins and optimizing operation conditions for the removal of 43 perfluoroalkyl and polyfluoroalkyl substances (PFASs), spanning both long- and short-chain fluorocarbon variants, across diverse wat...
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...
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