AIMC Topic: Water Purification
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Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste.
Scientific reports
Aug 30, 2024
The presence of toxic chemicals in water, including heavy metals like mercury and lead, organic pollutants such as pesticides, and industrial chemicals from runoff and discharges, poses critical public health and environmental risks leading to severe...
Synthesis and characterization of Fe(III)-doped beta-cyclodextrin-grafted chitosan cryogel beads for adsorption of diclofenac in aqueous solutions: Adsorption experiments and deep-learning modeling.
International journal of biological macromolecules
Aug 28, 2024
Diclofenac (DCF) is frequently detected in aquatic environments, emphasizing the critical need for its efficient removal globally. Here, we present the synthesis of Fe(III)-doped β-CD-grafted chitosan (Fe/β-CD@CS) cryogel beads designed for adsorbing...
Determinants of adoption of household water treatment in Haiti using two analysis methods: logistic regression and machine learning.
Journal of water and health
Aug 21, 2024
Household water treatment (HWT) is recommended when safe drinking water is limited. To understand determinants of HWT adoption, we conducted a cross-sectional survey with 650 households across different regions in Haiti. Data were collected on 71 dem...
Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system.
The Science of the total environment
Aug 15, 2024
Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution system (DWDS) remains challenging. Predicting DBPs using readily available water quality parameters can help to understand DBPs associated risks and capture t...
Machine learning based-model to predict catalytic performance on removal of hazardous nitrophenols and azo dyes pollutants from wastewater.
International journal of biological macromolecules
Aug 14, 2024
To maintain human health and purity of drinking water, it is crucial to eliminate harmful chemicals such as nitrophenols and azo dyes, considering their natural presence in the surroundings. In this particular research study, the application of machi...
Supporting data-enhanced hybrid ordinary differential equation model for phosphate dynamics in municipal wastewater treatment.
Bioresource technology
Aug 6, 2024
A parallel hybrid ordinary differential equation (ODE) integrating the Activated Sludge Model No. 2d (ASM2d) and an artificial neural network (ANN) was developed to simulate biological phosphorus removal (BPR) with high accuracy and interpretability....
Analyzing the impact of artificial intelligence on operational efficiency in wastewater treatment: a comprehensive neutrosophic AHP-based SWOT analysis.
Environmental science and pollution research international
Aug 6, 2024
The escalating global challenges of population growth, climate crisis, and resource depletion have intensified water scarcity, emphasizing the critical role of wastewater treatment (WWT) in environmental preservation. While discharging untreated wast...
Low-carbon wastewater treatment and resource recovery of recirculating aquaculture system by immobilized chlorella vulgaris based on machine learning optimization.
Bioresource technology
Aug 3, 2024
Immobilized microalgae biotechnologies can conserve water and space by low-carbon wastewater treatment and resource recovery in a recirculating aquaculture system (RAS). However, technical process parameters have been unoptimized considering the mutu...
Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects.
Water science and technology : a journal of the International Association on Water Pollution Research
Jul 26, 2024
Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI m...
Learning a neural network-based soft sensor with double-errors parallel optimization towards effluent variable prediction in wastewater treatment plants.
Journal of environmental management
Jul 24, 2024
With the development of machine learning and artificial intelligence (ML/AI) models, data-driven soft sensors, especially the neural network-based, have widespread utilization for the prediction of key water quality indicators in wastewater treatment...
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