NIR Spectroscopy Model Switching for Fluid Analysis
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Solution Overview
Problem
NIR spectroscopy accuracy is reduced in fluctuating process conditions, particularly in real-time hydrocarbon analysis where standardization of test conditions is not possible, leading to inaccurate results due to the need for broad reference analytical models.
Innovation Solution
A method and system that allow for model switching and automatic model building during NIR spectroscopy, determining whether a sample is in a single or multi-state condition to select the appropriate model set for analysis, improving accuracy by using multiple models tailored to specific conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single broad reference analytical model is used to handle fluctuating process conditions, then the system can operate under varying conditions without manual standardization, but the accuracy of the analysis results deteriorates
Solution Approach 1:
The patent segments the single broad reference analytical model into multiple specific reference analytical models, each tailored to particular process conditions. The system divides the model space based on process condition parameters, creating specialized models for different operational states while maintaining the ability to handle fluctuating conditions through automated model selection.
Solution Approach 2:
The patent implements dynamic model selection where the system automatically chooses the appropriate reference analytical model based on real-time process condition monitoring. This dynamic approach allows the system to adapt to fluctuating conditions by selecting the most suitable model for current conditions, thereby maintaining both versatility and accuracy.
2Measurement precision
If multiple specific reference analytical models are created for different process conditions, then the accuracy of analysis results improves, but the complexity of the system increases
Solution Approach 1:
The patent implements self-service through automated model selection algorithms that monitor process conditions and automatically select the appropriate reference analytical model without manual intervention. This automation reduces the operational complexity of managing multiple models, as the system independently determines which model to apply based on current process parameters.
Solution Approach 2:
The system incorporates feedback mechanisms where process condition data is continuously monitored and fed back to the model selection algorithm. This feedback loop enables automatic adjustment of model selection based on actual process conditions, reducing the need for manual model management while maintaining high accuracy across varying conditions.
3Measurement precision
If manual standardization of test conditions is implemented, then the accuracy of analysis results improves, but the productivity and speed of real-time analysis decreases
Solution Approach 1:
The patent enables the system to self-standardize by automatically monitoring process conditions and selecting appropriate reference models without manual intervention. This automated approach maintains accuracy comparable to manual standardization while preserving real-time analysis speed and productivity.
Solution Approach 2:
The system performs preliminary actions by pre-establishing multiple reference analytical models for different process conditions before actual analysis begins. This preparation allows the system to quickly select the appropriate model during real-time analysis without requiring manual standardization steps, thereby maintaining both accuracy and speed.
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
Enhances the accuracy of fluid composition analysis by allowing dynamic model selection based on sample characteristics, improving results in fluctuating process conditions without the need for manual standardization.
Implementation Method 1
Near-infrared (NIR) spectroscopy is a nondestructive method that provides simple, fast multiconstituent analysis on virtually any fluid
Data Source
AI summary
An improved method and system for analyzing multistate fluids using NIR spectroscopy. If the sample to be tested resides in a single state condition, the configuration file used in spectroscopic analysis will only be applied against a single model. However, if the sample to be tested is in a multi-state environment, an algorithm determines which model set of a plurality of model sets should be utilized based on the sample characteristics, and the configuration file used in spectroscopic analysis will be applied against the selected model. Results are generated showing the designated parameters.