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Updated: Jan 10, 2026

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts

Procedure to Evaluate the Efficiency of Flocculants for the Removal of Dispersed Particles from Plant Extracts

Published on: April 9, 2016

Predicting cyanobacteria removal efficiency in flocculation-DAF: Improving interpretable automated machine learning

Xiao Zhao¹, Zijun Yang², Jianjian Wei³

¹Key Laboratory of Integrated Regulation and Resources Development on Shallow Lakes of Ministry of Education, College of Environment, Hohai University, Nanjing 210098, China.

|November 25, 2025

Summary

This summary is machine-generated.

This study introduces an interpretable machine learning framework to optimize cyanobacteria removal using dissolved air flotation (DAF). The model accurately predicts efficiency and identifies key operational parameters for improved water treatment.

Keywords:

Automated machine learning Cyanobacteria separation Data augmentation Explainable analysis Flocculation-DAF

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Last Updated: Jan 10, 2026

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Published on: February 25, 2021

Area of Science:

Environmental Science
Water Treatment Technologies
Machine Learning Applications

Background:

Flocculation-dissolved air flotation (DAF) is crucial for cyanobacteria separation.
Optimizing DAF efficiency is complex due to interdependencies between water quality, cyanobacteria, and operational factors.
Data scarcity hinders accurate prediction and control of DAF processes.

Purpose of the Study:

To develop an interpretable machine learning framework for predicting cyanobacteria removal efficiency in DAF.
To identify critical operational parameters influencing DAF performance.
To enable accurate prediction and optimize DAF system strategies.

Main Methods:

Developed an interpretable machine learning framework integrating Conditional Variational Auto-Encoder (CVAE) with H2O AutoML.
Utilized CVAE for data augmentation to address data scarcity by generating synthetic samples.
Employed model interpretability analyses to determine influential variables.

Main Results:

The CVAE-AutoML model achieved high prediction accuracy (R² = 0.98), outperforming traditional and sole AutoML models.
Identified flotation time, flocculant dosage, and cyanobacteria density as key influential variables.
Quantified the impact of specific operational parameters on cyanobacteria removal efficiency.

Conclusions:

The developed framework accurately predicts cyanobacteria removal efficiency in DAF systems.
Provides valuable insights into optimizing DAF operational strategies through identification of critical parameters.
Offers a robust methodology for understanding and improving water treatment processes.