AIMC Topic: Water Purification
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Data assimilation for prediction of ammonium in wastewater treatment plant: From physical to data driven models.
Water research
Apr 29, 2025
This study compares various modeling approaches to predict ammonium concentration in wastewater treatment plants (WWTPs), with a focus on integrating data assimilation techniques. It explores white-box, grey-box, and black-box models, evaluating thei...
Machine learning-guided performance prediction of forward osmosis polymeric membranes for boron recovery.
Water research
Apr 22, 2025
Efficient recovery of boron is one of the crucial strategies of sustainably extracting valuable resource from water. It however still remains a key technological challenge to efficiently predict boron recovery from unconventional water resources such...
Novel PVDF mixed matrix membranes incorporated with green synthesized magnesium oxide nanoparticles for enhanced dye removal: Optimization using RSM, SOLVER, and ANN approach.
Environmental research
Apr 16, 2025
The application of nanofiltration membrane technology for removing pollutant dyes from industrial wastewater represents a significant advance in environmental remediation. This research explores the development and performance evaluation of a novel P...
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...
Treatment options of nitrogen heterocyclic compounds in industrial wastewater: From fundamental technologies to energy valorization applications and future process design strategies.
Water research
Mar 29, 2025
Nitrogen heterocyclic compounds (NHCs) widely exist in industrial wastewater and presented significant environmental and health risks due to their toxicity and persistence. This review addressed the challenges in treating NHCs in industrial wastewate...
Machine learning strategy secures urban smart drinking water treatment plant through incremental advances.
Water research
Mar 24, 2025
The integration of machine learning into urban drinking water treatment plants (DWTPs) offers a transformative pathway to ensure drinking water safety while promoting the development of smart, low-carbon cities. However, the effectiveness of these sy...
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...
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