Holographic Fluid Quality Monitoring for Real-Time Particle Classification
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Solution Overview
Problem
Existing methods for monitoring fluid quality, such as turbidity meters and laser particle counters, provide limited information about the nature and concentration of microscopic particles in fluids, and real-time monitoring is hindered by the need for laboratory analysis, which lacks temporal and geographical coverage.
Innovation Solution
A fluid quality measurement device using holographic imaging and autoencoder neural networks to classify and quantify microscopic objects in fluids, providing a fluid sample fingerprint for continuous, real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If turbidity meters are used for real-time monitoring of suspended solid particles, then continuous monitoring capability is improved, but the ability to identify and differentiate particle types deteriorates (only total amount is provided)
Solution Approach 1:
The patent segments the particle analysis by dividing particles into different size fractions (e.g., 2-10 μm, 10-20 μm, 20-50 μm, >50 μm) using multiple laser particle counters with different detection thresholds. This segmentation allows continuous monitoring while providing differentiated information about particle types and sizes, resolving the contradiction between continuous monitoring capability and particle type identification.
2Measurement precision
If laser particle counters are used to provide particle size information and monitor particle concentration in several size fractions, then particle differentiation capability is improved, but cost increases
Solution Approach 1:
The patent implements a multi-functional particle monitoring system where laser particle counters serve multiple purposes: they detect particle concentration, determine particle size distribution across multiple fractions, and provide data for both continuous monitoring and detailed particle characterization. This multi-functionality achieves precise particle differentiation while optimizing resource utilization and reducing overall system complexity compared to having separate specialized devices for each function.
3Measurement precision
If sample collection and laboratory analysis are used for monitoring water quality, then detailed analysis capability is improved, but temporal and geographical coverage deteriorates
Solution Approach 1:
The patent replaces the mechanical system of manual sample collection, transport, and laboratory analysis with an automated in-situ optical detection system using laser particle counters. This substitution enables continuous real-time monitoring at multiple locations within the water distribution system, achieving both detailed particle analysis capability and comprehensive temporal-geographical coverage simultaneously by eliminating the limitations of periodic manual sampling.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate classification and concentration analysis of microscopic objects in fluids, detecting anomalies and contaminants in real-time, enhancing the reliability of fluid quality monitoring systems.
Implementation Method 1
Illuminating the microscopic objects with coherent light and recording scattered and non-scattered light by an imaging unit
Implementation Method 2
Each hologram in the plurality of holograms representing a microscopic object in a fluid sample
Data Source
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AI summary
It is an objective to provide a fluid quality measurement device. According to an embodiment, a fluid quality measurement device is configured to: obtain a plurality of holograms, wherein each hologram in the plurality of holograms represents a microscopic object in a fluid sample; produce a latent space representation of each hologram using a trained autoencoder neural network; assign each hologram in the plurality of holograms to a class based on the latent space representation of the hologram; and produce a fluid sample fingerprint based on the assignment of the plurality of holograms into the plurality of classes.