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Association analysis between floc image information and coagulation efficiency based on partial least squares.

Shuaishuai Li¹, Yuling Liu¹, Zhixiao Wang¹

¹State Key Laboratory of Water Engineering Ecology and Environment in Arid Area, Xi'an University of Technology, Xi'an, People's Republic of China.

Environmental Technology

|June 12, 2026

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This summary is machine-generated.

Floc image analysis effectively monitors water treatment coagulation. Key image features predict floc behavior, settling, and effluent turbidity, aiding process optimization for clean water.

Area of Science:

Environmental Science
Water Treatment Technologies
Image Analysis

Background:

Coagulation monitoring in water treatment is crucial for efficiency.
A systematic understanding of floc image features and their link to coagulation efficiency is lacking.
Current methods may not fully capture the complex dynamics of flocculation.

Purpose of the Study:

To systematically investigate the relationship between floc image features and coagulation efficiency.
To characterize floc population behavior using morphological and textural features.
To provide a theoretical basis for optimizing water treatment processes.

Main Methods:

Non-invasive image acquisition system for capturing floc images.
Extraction of morphological and textural features using Python-OpenCV.

Keywords:

Drinking water treatment floc settling behaviour fractal dimension image texture multivariate regression

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Partial least squares regression to analyze associations between image features, settling, and turbidity.

Main Results:

Image features effectively characterize floc population behavior.
Floc number removal is influenced by 3D fractal dimension (D3), small particle proportion (100-150 μm), and textural correlation.
Effluent turbidity is governed by initial floc number, D3, and small particle proportion, correlating with residual floc count.

Conclusions:

Floc image analysis offers a promising approach for real-time coagulation monitoring.
Image-derived features like fractal dimension and textural correlation provide insights into floc compactness and uniformity.
Optimized coagulation monitoring supports efficient water treatment and Sustainable Development Goal 6 (Clean Water and Sanitation).