AIMC Topic: Wastewater

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Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques.

Chemosphere
The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in ...

Predicting the Occurrence of Substituted and Unsubstituted, Polycyclic Aromatic Compounds in Coking Wastewater Treatment Plant Effluent using Machine Learning Regression.

Chemosphere
Organic contaminants such as polycyclic aromatic compounds (PACs) occurring in industrial effluents can not only persist in wastewater but transform into more toxic and mobile, substituted heterocyclic products during treatment. Thus, predicting the ...

Machine learning-enhanced molecular network reveals global exposure to hundreds of unknown PFAS.

Science advances
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
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
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
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...

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
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
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...