AIMC Topic: Chlorine

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Proof-of-concept evaluation at Cox's Bazar of the Safe Water Optimization Tool: water quality modelling for safe water supply in humanitarian emergencies.

BMJ global health Aug 20, 2025
INTRODUCTION: Waterborne diseases are leading concerns in emergencies. Humanitarian guidelines stipulate universal water chlorination targets, but these fail to reliably protect water as postdistribution chlorine decay can leave water vulnerable to p...
Water Quality Water Supply Proof of Concept Study Halogenation Waterborne Diseases Water Purification Emergencies Chlorine Humans
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A framework predicting removal efficacy of antibiotic resistance genes during disinfection processes with machine learning.

Journal of hazardous materials Apr 3, 2025
Disinfection has been applied widely for the removal of antibiotic resistance genes (ARGs) to curb the spread of antibiotic resistance. Quantitative polymerase chain reaction (qPCR) is the most used method to quantify the damage of DNA thus calculati...
Genes, Bacterial Ozone Drug Resistance, Microbial Chlorine Disinfectants Ultraviolet Rays Hydrogen Peroxide Disinfection Machine Learning Water Purification
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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...
Machine Learning Models, Chemical Water Purification Environmental Monitoring Water Pollutants, Chemical Chlorine Halogenation
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Enhancing long-term water quality modeling by addressing base demand, demand patterns, and temperature uncertainty using unsupervised machine learning techniques.

Water research Oct 25, 2024
Water quality modelling in Water Distribution systems (WDS) is frequently affected by uncertainties in input variables such as base demand and decay constants. When utilizing simulation tools like EPANET, which necessitate exact numerical inputs, the...
Uncertainty Chlorine Water Quality Models, Theoretical Unsupervised Machine Learning Temperature
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The insightful water quality analysis and predictive model establishment via machine learning in dual-source drinking water distribution system.

Environmental research Feb 16, 2024
Dual-source drinking water distribution systems (DWDS) over single-source water supply systems are becoming more practical in providing water for megacities. However, the more complex water supply problems are also generated, especially at the hydrau...
Machine Learning Models, Theoretical Water Supply Trihalomethanes Water Quality Drinking Water Water Pollutants, Chemical Chlorine
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Development of benchmark datasets for text mining and sentiment analysis to accelerate regulatory literature review.

Regulatory toxicology and pharmacology : RTP Nov 11, 2022
In the field of regulatory science, reviewing literature is an essential and important step, which most of the time is conducted by manually reading hundreds of articles. Although this process is highly time-consuming and labor-intensive, most output...
Data Mining Chlorine Sentiment Analysis Artificial Intelligence Machine Learning
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Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms.

Journal of environmental and public health Sep 28, 2022
Applicability of statistical models in predicting chlorine decay remains minimally explored. This study predicted residual chlorine using six deep learning and nine machine learning techniques. Suitability of multimodel ensembles (MMEs) including ari...
Models, Statistical Chlorine Algorithms Deep Learning Drinking Water Machine Learning
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Implementation of an early warning system with hyperspectral imaging combined with deep learning model for chlorine in refuse derived fuels.

Waste management (New York, N.Y.) Feb 21, 2022
Chlorine content is one of the most important parameters in Refuse Derived Fuels (RDFs) used as a fuel in cement kilns. The main problem with the use of RDF is that chlorine in the waste weakens the cement, increases the risk of corrosion in the kiln...
Deep Learning Chlorine Garbage Hyperspectral Imaging Refuse Disposal
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Evaluation of Free Available Chlorine of Sodium Hypochlorite When Admixed with 0.2% Chitosan: A Preliminary Study.

The journal of contemporary dental practice Oct 1, 2021
AIM AND OBJECTIVE: The aim of the study was to evaluate the changes in free available chlorine (FAC) when 6% sodium hypochlorite (NaOCl) is admixed with irrigants 17% ethylenediaminetetraacetic acid (EDTA), 2% chlorhexidine (CHX), and 0.2% chitosan i...
Root Canal Irrigants Smear Layer Sodium Hypochlorite Chlorine Humans Edetic Acid Chitosan
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Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks.

Physical chemistry chemical physics : PCCP Jun 1, 2020
Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation. Currently, this methodology has three major limitations - the cost of representation generation, risk of inh...
Oxygen Nitrogen Metals Carbon Hydrogen Sulfur Fluorine Phosphorus Chlorine Deep Learning Iodine Bromine
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