Water Quality AI Recognition System

AI-powered water quality analysis system using industrial cameras for SV30 sludge settling ratio and floc morphology recognition.

Background

In wastewater treatment and water purification processes, SV30 (sludge settling ratio) and floc morphology are critical indicators for evaluating treatment effectiveness. Traditional manual observation methods suffer from low efficiency, high subjectivity, and inability to provide 24/7 monitoring. This project uses industrial cameras for real-time image/video capture, combined with OpenCV image processing and deep learning technology, to achieve automated intelligent recognition and analysis of water quality indicators.

Technical Architecture

Hardware System

• High-precision industrial cameras (GigE/USB3.0 protocol support)
• Programmable LED ring light for stable lighting conditions
• Edge computing devices (NVIDIA Jetson / Industrial PC)
• Standardized sampling containers and mounting systems

Software Stack

• Image Capture: OpenCV + Industrial Camera SDK
• Image Preprocessing: Noise reduction, enhancement, ROI extraction, perspective correction
• SV30 Recognition: Edge detection + liquid surface segmentation algorithms
• Floc Analysis: YOLO/U-Net deep learning models
• Data Storage: Time-series database + image archival
• Visualization: Real-time web dashboard

Core Features

SV30 Settling Ratio Recognition

• Real-time tracking of sludge settling process
• Automatic identification of sludge-water interface
• Precise calculation of 30-minute settling ratio
• Generation of settling curves and trend analysis

Floc Morphology Recognition

• Floc size distribution statistics
• Floc density and uniformity analysis
• Real-time flocculation effectiveness evaluation
• Automatic alerts for abnormal floc conditions

Intelligent Analysis & Alerts

• Historical data comparison analysis
• Water quality trend prediction
• Automatic alerts for abnormal conditions
• Dosing optimization recommendations

Results

• Recognition accuracy over 95%
• 24/7 unattended monitoring capability
• 60% reduction in manual inspection costs
• 15% savings in chemical dosing costs through optimization