Phase Analysis System for Fluid Boundary Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for determining phase information in fluid samples, such as those involving immiscible compounds, suffer from high miss rates and imprecise measurements due to reliance on human visual inspection and computer vision methods, especially when dealing with emulsions and weak boundaries, which are difficult to recognize.
Innovation Solution
A phase analysis system utilizing a data-driven model with output channels for classifying boundaries between phases, allowing for improved computer vision inspection that reduces miss rates and provides precise measurements by deriving phase information from images, including properties like boundary height, strength, and type, without manual inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human visual inspection is used to determine phase information, then flexibility and adaptability are maintained, but measurement precision and reliability deteriorate due to subjectivity and time consumption
Solution Approach 1:
The patent replaces human visual inspection with an automated computer vision system that uses image processing algorithms to detect phase boundaries and characteristics. The system captures images of fluid samples and automatically analyzes phase separation, eliminating subjectivity and improving measurement precision while reducing time consumption.
Solution Approach 2:
The system creates digital copies (images) of the fluid samples and analyzes these representations rather than requiring direct human observation. The images serve as data copies that can be processed repeatedly by algorithms, improving consistency and precision in phase information determination.
2Reliability
If conventional computer vision methods are used, then automation is achieved, but measurement precision deteriorates due to high miss rates in detecting emulsions and weak boundaries
Solution Approach 1:
The patent employs multiple image processing parameters and thresholds that can be adjusted to optimize detection sensitivity. By changing parameters such as contrast enhancement, edge detection thresholds, and image processing algorithms, the system improves its ability to detect weak boundaries and emulsions that conventional methods miss.
Solution Approach 2:
The system segments the image analysis into multiple stages: initial image capture, preprocessing, edge detection, phase boundary identification, and verification. This segmentation allows each stage to be optimized independently, improving overall reliability in detecting difficult-to-recognize features like emulsions and weak boundaries.
3Productivity
If manual data analysis is performed, then flexibility is maintained, but productivity and efficiency deteriorate due to time consumption
Solution Approach 1:
The patent replaces manual data analysis with automated image processing and machine learning algorithms that rapidly analyze phase separation characteristics. The system processes multiple images and extracts phase information much faster than human analysts, significantly improving productivity and reducing time loss in phase stability monitoring.
Solution Approach 2:
The system enables continuous automated monitoring of phase separation processes, capturing images at multiple time points and continuously analyzing phase stability. This continuous action eliminates the interruptions and time losses associated with manual inspection, maintaining constant surveillance of the fluid sample throughout the entire process.
Data Source
AI summary
The present invention relates to determining phase information of a fluid sample. At least one image of the fluid sample including the phase information of the fluid sample is provided. Additionally, a data driven model which comprises at least one output channel for the phase information is provided. The at least one output channel includes at least one output channel for classifying a boundary between two phases of the fluid sample, such that the phase information includes information about a property of the boundary between the two phases, such as a height, a volume, a type, or a strength of the boundary. The phase information of the fluid sample is derived based on the data driven model and the at least one image of the fluid sample including the phase information of the fluid sample.