AI Particle Classifier for Real-Time Water Quality Monitoring
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Solution Overview
Problem
Current water quality monitoring systems lack real-time capabilities, require manual sampling, are expensive, and cannot effectively analyze a wide range of suspended particles in water, including organic and inorganic species, due to reliance on bulky, expensive equipment and reagents, and are limited in their ability to handle turbulent or moving fluids.
Innovation Solution
A sensor system that uses a camera to capture images of suspended particles and an artificial intelligence image classifier to analyze morphology, size, color, and movement, minimizing the need for reagents and pre-processing, allowing for real-time, remote, and continuous monitoring of particles in flowing fluids without the need for concentration or purification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional flow cytometers or imaging systems are used to analyze suspended particles, then measurement precision can be achieved, but device complexity and cost increase significantly
Solution Approach 1:
The system segments the particle analysis task into distinct phases: hydrodynamic focusing creates a single-file particle stream, the imaging system captures individual particle images, and the AI classifier processes images to identify particle types. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining high measurement precision.
Solution Approach 2:
The patent introduces an AI-based image classifier as an intermediary between the imaging system and the final particle identification. This intermediary processes particle images to determine particle types, replacing complex traditional optical classification systems and significantly reducing device complexity while maintaining or improving classification accuracy.
2Measurement precision
If manual sampling and laboratory analysis are performed, then measurement precision can be maintained, but loss of time increases due to sample collection, transport, and processing delays
Solution Approach 1:
The system enables self-service monitoring by deploying the imaging and AI classification components directly at the water source or distribution point. The system autonomously captures images of particles in the water stream and immediately classifies them using the AI model, eliminating the need for manual sampling, transport, and laboratory processing while maintaining measurement precision through continuous in-situ analysis.
3Measurement precision
If fluorescent labeling is used to identify specific organisms, then measurement precision for specific particle types improves, but loss of substance increases due to reagent consumption
Solution Approach 1:
The patent replaces the chemical fluorescent labeling system with a mechanical/optical imaging and AI classification system. The AI classifier identifies particle types based on morphological features extracted from images, eliminating the need for fluorescent reagents and significantly reducing substance consumption while maintaining or improving species identification accuracy through pattern recognition.
4Measurement precision
If pre-processing steps such as concentration, purification, and cleaning are performed, then measurement precision improves, but productivity decreases due to additional processing steps
Solution Approach 1:
The system implements continuous particle analysis by maintaining a constant flow of water through the imaging system. The AI classifier processes images in real-time as particles pass through the focal plane, enabling continuous detection and classification without interruption for concentration, purification, or other pre-processing steps. This continuous operation significantly increases productivity while maintaining detection accuracy through consistent image quality.
Data Source
AI summary
A fluid suspended particle classifier system detects particles of one or more classes from a predetermined recognized set of classes based on multiple images of groups of particles. An artificial intelligence classifier combines subtle clues in the images including particle morphology, size, spectral response, fluorescence, movement, density, or aggregation. Class-specific concentration estimations have a low limit of detection, and a high dynamic range. There is a tolerance for mixtures of classes of particles having widely differing sizes and concentrations. Selected particle images, histories and analysis are communicated and stored. Consumable use and manual procedures are minimized or avoided, allowing remote, unattended, real-time operation. In remote water quality monitoring, the appearance of particles resembling the Escherichia coli bacterium, an occasional human pathogen, causes an alarm.