Automated Fluid Mixing System for ML Training Data Generation
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Solution Overview
Problem
Current methods for generating training data for machine learning in fluid composition monitoring are inadequate due to limited representativeness, logistical challenges, and biases in sample collection, particularly in surface water pollution monitoring, which results in inaccurate predictions for complex contaminant mixtures.
Innovation Solution
A system that mixes two or more fluid samples with known concentrations to create homogenously mixed samples, allowing for precise measurement and data collection, which is then used to generate machine learning training data sets, including a fluidic controller, mixer, and sensor to determine relative ratios and concentrations, and a processor to store and analyze the data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual sample collection methods are used, then logistical complexity is reduced, but training data representativeness and accuracy deteriorate
Solution Approach 1:
The patent creates virtual copies of real-world fluid samples through automated mixing of standard solutions. Instead of collecting actual environmental samples which are logistically complex and biased, the system generates synthetic samples that mathematically represent all possible contaminant combinations, achieving complete representativeness without field collection complexity
Solution Approach 2:
The system varies concentration parameters of standard solutions in a controlled manner to generate training data across the entire parameter space. By systematically changing concentrations of multiple contaminants simultaneously, the system creates comprehensive training sets that cover all possible scenarios without the logistical constraints of manual sampling
2Measurement precision
If comprehensive sample coverage is achieved through extensive field collection, then training data representativeness is improved, but collection time and resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-mixing standard solutions with known concentrations before actual measurement. This preparation phase creates a complete library of reference samples that can be instantly accessed for training, eliminating the need for time-consuming field collection and sample processing during the training phase
Solution Approach 2:
The system replaces mechanical field collection operations with automated computational mixing and data generation. Instead of physically traveling to sampling locations, collecting bottles, and processing samples, the system uses computer-controlled mixing and automated analysis to generate identical training data faster and more comprehensively
3Ease of operation
If manual sample processing is used, then operational simplicity is maintained, but data accuracy and consistency deteriorate
Solution Approach 1:
The system performs self-service through automated control of the entire sampling and analysis process. The fluidic controller automatically dispenses precise volumes of standard solutions, the mixer automatically combines them in controlled ratios, and sensors automatically measure the resulting mixtures, eliminating human error and ensuring consistent, high-precision data without requiring skilled operators
4Measurement precision
If automated mixing and measurement systems are implemented, then data accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal system where a single automated platform can handle multiple functions: storing standard solutions, controlling fluid delivery, mixing samples, measuring concentrations, and generating training data. This multi-functional approach consolidates what would otherwise require separate devices into one integrated system, reducing overall complexity while maintaining high precision
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
This approach enables the creation of comprehensive and accurate machine learning training data sets that can predict chemical parameter values and relative ratios in fluid samples, improving the accuracy of fluid monitoring applications by covering a broader range of potential contaminant scenarios.
Implementation Method 1
A mixer homogenizes the mixture of the two or more fluid samples in the mixing unit to create a homogenously mixed sample
Implementation Method 2
A first sensor performs one or more measurements on the homogenously mixed sample
Data Source
AI summary
A system for generating machine learning training data sets for fluid monitoring applications is provided. The system includes two or more containers, each container for storing a fluid sample with a known concentration of one or more additive chemical parameters. A fluidic controller independently controls the flow of the two or more fluid samples, through fluidic conduits, from their respective containers to a mixing unit, in such a way that the relative ratios of the two or more fluid samples delivered to the mixing unit is monitored. A mixer homogenizes the mixture of the two or more fluid samples in the mixing unit to create a homogenously mixed sample. A first sensor performs one or more measurements on the homogenously mixed sample. The system further includes a database and a processor. The processor is configured to: receive as an input, for each fluid sample, the concentration of the one or more additive chemical parameters: instruct the fluidic controller and mixer so as to generate the homogenously mixed sample of the fluid samples; determine a concentration of the one or more additive chemical parameters of the homogenously mixed sample from a combination of the relative ratios of the two or more fluid samples and of the known concentration of their respective additive chemical parameters: obtain results of one or more measurements performed by the first sensor on the homogenously mixed sample; and store in the database the result(s) of the one or more measurements performed by the first sensor, as feature(s), and the concentration of the one or more additive chemical parameter(s) of the homogenously mixed sample and/or the relative ratios of the two or more fluid samples, as label(s).