AIMC Topic: Flocculation

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A Robust Gaussian Process Paradigm for Predictive Modeling on Small Data sets in Environmental Science: A Case Study in Ballasted Flocculation.

Environmental science & technology
Environmental processes including ballasted flocculation (BF) present significant optimization challenges due to complex multicomponent interactions and small, heterogeneous experimental data sets that frequently lead to overfitted machine learning (...

Predicting cyanobacteria removal efficiency in flocculation-DAF: Improving interpretable automated machine learning with CVAE data augmentation.

Water research
Flocculation-dissolved air flotation (DAF) is an efficient and widely adopted technique for cyanobacteria separation. However, optimizing its removal efficiency remains challenging due to complex interdependencies among water quality, cyanobacterial ...

A methodology for coagulant virtual testing to improve dissolved organic matter removal in surface water treatment.

The Science of the total environment
Coagulation is one of the most crucial steps in a Drinking Water Treatment Plant (DWTP). The coagulant dose required for the removal of particles and natural organic matter (NOM) is typically determined through jar tests. However, this method is time...

Evaluation of plant-based coagulants for turbidity removal and coagulant dosage prediction using machine learning.

Environmental technology
This study investigates the use of six plant-based coagulants - , , , , , and for the removal of turbidity from wastewater effluent. The coagulants were characterized using Scanning Electron Microscopy (SEM) to determine morphological structure, X-r...

Constructing a visual detection model for floc settling velocity using machine learning.

Journal of environmental management
Optimizing the dosage of coagulant is a time-consuming process, and real-time evaluation of floc settling velocity can quickly predict the coagulation effect and optimize the dosage. This study used a convolutional neural network (CNN) model to analy...

Evaluation of enhanced chemical coagulation method for a case study on colloidal liquid particle in wastewater treatment: Statistical optimization analysis and implementation of machine learning.

Journal of environmental management
Coal mines are one of the largest sources of energy supply and generate significant volumes of wastewater. Chemical coagulation is one of the most effective methods for wastewater treatment. In this research, ferric and aluminum-based coagulants, alo...

Real-time monitoring of activated sludge flocs via enhanced mask region-based Convolutional Neural networks.

Environmental research
The functionality of activated sludge in wastewater treatment processes depends largely on the structural and microbial composition of its flocs, which are complex assemblages of microorganisms and their secretions. However, monitoring these flocs in...

Deep learning-based flocculation sensor for automatic control of flocculant dose in sludge dewatering processes during wastewater treatment.

Water research
In sludge dewatering of most wastewater treatment plants (WWTPs), the dose of polymer flocculant is manually adjusted through direct visual inspection of the flocs without the aid of any instruments. Although there is a demand for the development of ...

Applications of artificial intelligence (AI) in drinking water treatment processes: Possibilities.

Chemosphere
In water treatment processes (WTPs), artificial intelligence (AI) based techniques, particularly machine learning (ML) models have been increasingly applied in decision-making activities, process control and optimization, and cost management. At leas...

Modelling and optimisation of electrocoagulation/flocculation recovery of effluent from land-based aquaculture by artificial intelligence (AI) approaches.

Environmental science and pollution research international
This study examined the modelling and optimisation of the electrocoagulation-flocculation (ECF) recovery of aquaculture effluent (AQE) using aluminium electrodes. The response surface methodology (RSM), artificial neural network (ANN), and adaptive n...