Fluid Property Sensor Using Electrochemical Impedance Spectroscopy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current fluid analysis methods, such as laboratory-based ICP-AES, gas chromatography, and FTIR, are time-consuming, prone to human error, and provide incomplete insights due to reliance on small, representative samples and inference-based decision-making, while sensors require frequent updates and do not offer comprehensive fluid analysis.
Innovation Solution
The development of fluid property sensors using electrochemical impedance spectroscopy (EIS) and fluid particle sensors employing magnetic induction spectroscopy (MIS) to provide high-resolution, non-destructive analysis of fluids, utilizing machine learning and AI to correlate electrochemical properties and detect metallic particles, offering a complete and precise fingerprint of fluid properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laboratory analysis methods (ICP-AES, gas chromatography, FTIR) are used to analyze fluids, then measurement precision is improved, but analysis time increases significantly (several days to several weeks)
Solution Approach 1:
The patent replaces traditional laboratory analysis methods (ICP-AES, gas chromatography, FTIR) with an electrochemical sensor system that uses electrical impedance spectroscopy to detect fluid properties. This substitution eliminates the need for complex laboratory equipment and manual sampling processes, enabling real-time analysis while maintaining measurement precision through automated electrochemical detection.
Solution Approach 2:
The sensor system performs self-contained fluid analysis directly in the field without requiring external laboratory facilities. The device automatically samples, analyzes, and reports fluid properties using integrated electrochemical cells and processing units, eliminating the need for manual sampling and transportation to laboratories.
2Ease of operation
If manual sampling methods are used to collect fluid samples, then ease of operation is improved, but reliability deteriorates due to human error in sampling, labeling, and analysis
Solution Approach 1:
The sensor system automatically performs sampling, analysis, and data reporting without human intervention. The device includes automated sampling mechanisms that collect fluid samples directly from the process stream, eliminates manual labeling errors, and provides automatic data processing and transmission, thereby ensuring consistent reliability while maintaining operational ease.
Solution Approach 2:
The system continuously monitors fluid properties and provides real-time feedback on fluid condition. This automated feedback loop eliminates the need for manual sample collection and analysis scheduling, ensuring consistent and reliable measurements through continuous electrochemical detection without human error.
3Ease of operation
If small manual samples (e.g., 100 ml from 140 L tank) are analyzed, then ease of operation is improved, but measurement precision deteriorates as the sample may represent only 0.001% of the fluid and may not be representative
Solution Approach 1:
The sensor system continuously analyzes fluid directly from the process stream without requiring manual sampling. The electrochemical cells are positioned within the fluid flow path and automatically detect fluid properties in real-time, ensuring that the measurement represents the actual fluid conditions rather than a potentially unrepresentative manual sample.
Solution Approach 2:
The system provides continuous real-time analysis of fluid properties as fluid passes through the sensor. This continuous measurement approach ensures that the analysis always reflects current fluid conditions, eliminating the representativeness issues associated with discrete manual samples taken at specific times.
4Device complexity
If laboratory analysis is performed on small samples, then device complexity is reduced, but loss of information increases as the analysis may miss transient or developing issues
Solution Approach 1:
The sensor system performs complete fluid property analysis directly in the field without requiring transportation to laboratories. The integrated device includes all necessary components for sampling, electrochemical analysis, data processing, and communication, providing comprehensive fluid condition information while maintaining manageable device complexity through integration.
Solution Approach 2:
The system continuously monitors fluid properties over time, capturing transient and developing issues that would be missed in discrete laboratory sampling. This continuous electrochemical detection provides complete temporal information about fluid condition changes, preventing information loss while the device remains relatively simple.
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
These sensors enable real-time, high-resolution analysis of fluid properties and particles, reducing the need for laboratory testing, minimizing human error, and providing continuous, accurate insights into fluid conditions, thus enhancing decision-making in industrial and biomedical applications.
Implementation Method 1
The fluid property sensor may be configured to analyze a fluid and to provide a high-resolution signal by using electrochemical impedance spectroscopy (EIS)
Implementation Method 2
The fluid particle sensor may be configured to use magnetic induction spectroscopy (MIS) to detect and analyze microscopic metallic particles within a fluid
Data Source
AI summary
A method, system and apparatus for sensing fluids. A fluid sensor is configured to analyze a fluid utilizing impedance spectroscopy. Capacitive impedance of fluids is sensed and measured. Inductive impedance of suspended particles in fluids is measured. An electrochemical fingerprint of the properties of the fluid or of the particles within the fluid is generated. Fluid analytics data is generated from sensor signal data of the fluids under test. Trainable artificial intelligence algorithms are used to generate fluid analytics data.