AIMC Topic: Wastewater
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Enhanced wastewater treatment by catalytic persulfate activation with protonated hydroxylamine-assisted iron: Insights from a deep learning-based numerical investigation.
Chemosphere
May 25, 2024
Machine learning-enhanced molecular network reveals global exposure to hundreds of unknown PFAS.
Science advances
May 23, 2024
Unknown forever chemicals like per- and polyfluoroalkyl substances (PFASs) are difficult to identify. Current platforms designed for metabolites and natural products cannot capture the diverse structural characteristics of PFAS. Here, we report an au...
Optimizing removal of antiretroviral drugs from tertiary wastewater using chlorination and AI-based prediction with response surface methodology.
The Science of the total environment
May 3, 2024
Chemical and pharmaceutical chemicals found in water sources create substantial risks to human health and the environment. The presence of pharmaceutical contaminants in water can cause antibiotic resistance development, toxicity to aquatic organisms...
Spectral fusion-based machine learning classifiers for discriminating membrane breakage in multiple scenarios.
Water research
May 2, 2024
Membrane breakage can lead to filtration failure, which allows harmful substances to enter the effluent, posing potential hazards to human health and the environment. This study is an innovative combination of fluorescence and ultraviolet-visible (UV...
Application of machine learning for antibiotic resistance in water and wastewater: A systematic review.
Chemosphere
May 2, 2024
Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept...
Expression of Concern: Assessment in carbon-based layered double hydroxides for water and wastewater: Application of artificial intelligence and recent progress.
Chemosphere
Apr 11, 2024
A data-driven approach for revealing the linkages between differences in electrochemical properties of biochar during anaerobic digestion using automated machine learning.
The Science of the total environment
Apr 7, 2024
Biochar is commonly used to enhance the anaerobic digestion of organic waste solids and wastewater, due to its electrochemical properties, which intensify the electron transfer of microorganisms attached to its large surface area. However, it is diff...
Machine learning modeling of fluorescence spectral data for prediction of trace organic contaminant removal during UV/HO treatment of wastewater.
Water research
Mar 18, 2024
Dynamic feedback of the removal performance of trace organic contaminants (TrOCs) is essential towards economical advanced oxidation processes (AOPs), whereas the corresponding quick-response feedback methods have long been desired. Herein, machine l...
Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges.
Chemosphere
Feb 19, 2024
Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident ...
Artificial intelligence-enabled optimization of Fe/Zn@biochar photocatalyst for 2,6-dichlorophenol removal from petrochemical wastewater: A techno-economic perspective.
Chemosphere
Feb 19, 2024
While numerous studies have addressed the photocatalytic degradation of 2,6-dichlorophenol (2,6-DCP) in wastewater, an existing research gap pertains to operational factors' optimization by non-linear prediction models to ensure a cost-effective and ...
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