AIMC Topic: Chlorine

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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 Water Purification Models, Chemical Chlorine Halogenation Environmental Monitoring Water Pollutants, Chemical
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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 Water Supply Trihalomethanes Chlorine Water Quality Drinking Water Models, Theoretical Water Pollutants, Chemical
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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...
Artificial Intelligence Chlorine Sentiment Analysis Machine Learning Data Mining
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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...
Deep Learning Models, Statistical Drinking Water Chlorine Machine Learning Algorithms
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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...
Refuse Disposal Garbage Chlorine Hyperspectral Imaging Deep Learning
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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...
Chlorine Smear Layer Humans Edetic Acid Sodium Hypochlorite Chitosan Root Canal Irrigants
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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...
Bromine Oxygen Sulfur Fluorine Nitrogen Chlorine Hydrogen Phosphorus Iodine Deep Learning Metals Carbon
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Sanitation of tomatoes based on a combined approach of washing process and pulsed light in conjunction with selected disinfectants.

Food research international (Ottawa, Ont.) Sep 10, 2018
In this study, we evaluated the performance of a large-scale decontamination system based on a washing process in combination with pulsed light (PL) exposure and HO/chlorine. In order to identify optimum processing condition, we first evaluated the e...
Fruit Chlorine Disinfectants Solanum lycopersicum Light Water Decontamination Hydrogen Peroxide Food Microbiology Salmonella enterica Salmonella Food Poisoning Sanitation Food Handling
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Designing and executing a functional exercise to test a novel informatics tool for mass casualty triage.

Journal of the American Medical Informatics Association : JAMIA Oct 1, 2019
OBJECTIVE: The testing of informatics tools designed for use during mass casualty incidents presents a unique problem as there is no readily available population of victims or identical exposure setting. The purpose of this article is to describe the...
Chlorine Disaster Planning Mass Casualty Incidents Chemical Hazard Release Disasters Mobile Applications Triage Emergency Service, Hospital South Carolina Humans Artificial Intelligence
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