Multi-Component Rare Earth Prediction via HSI Spectral Analysis
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Solution Overview
Problem
Current methods fail to accurately predict the content of multiple components in rare earth extraction processes where rare earth ions with and without characteristic colors coexist, limiting the effectiveness of automatic control and detection in rare earth separation processes.
Innovation Solution
A method and system utilizing machine vision technology to obtain color characteristic values, determining HSI color feature components, establishing an extreme learning machine-based multi-component content soft measurement model, and optimizing it with a genetic algorithm to predict the content of cerium, praseodymium, and neodymium in rare earth solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine vision technology is used to detect rare earth components with characteristic colors, then detection speed is improved, but detection accuracy deteriorates when rare earth ions with and without characteristic colors coexist
Solution Approach 1:
The patent transforms the detection problem from direct color-based component identification to a spectral parameter analysis approach. By converting image data into spectral curves and extracting spectral parameters (such as absorbance at specific wavelengths, slope parameters, and characteristic ratio parameters), the system can distinguish between different rare earth components even when their colors are not directly observable. This parameter transformation enables accurate detection in mixed solutions where traditional color-based methods fail.
Solution Approach 2:
The patent introduces spectral parameters as an intermediary between the raw image data and the final component content determination. Instead of directly correlating image color values with component concentrations, the system first extracts spectral parameters from the absorption spectrum, then uses these parameters as intermediate variables to predict component contents through established mathematical models. This intermediary approach decouples the detection process from the limitations of direct color observation.
2Device complexity
If traditional color-based detection methods are used, then simple component detection is possible, but multi-component content prediction accuracy deteriorates in mixed rare earth solutions
Solution Approach 1:
The patent segments the detection process into distinct stages: image acquisition, spectral curve generation, parameter extraction, and content prediction. By dividing the complex multi-component detection task into these sequential segments, each handling a specific aspect of the analysis, the system maintains relative simplicity while achieving high accuracy. The segmentation allows each module to be optimized independently and facilitates the integration of multiple rare earth component analyses.
Solution Approach 2:
The patent transitions from two-dimensional color space analysis to spectral dimension analysis by generating absorption spectral curves. This dimensional transformation from simple color values to full spectral profiles provides additional information dimensions that enable differentiation between multiple rare earth components. The spectral parameters extracted from these curves serve as new dimensional features that resolve the ambiguity present in traditional color-based methods.
3Loss of information
If spectral parameters are extracted for all HSI components, then comprehensive analysis is achieved, but computational complexity increases
Solution Approach 1:
The patent extracts only the most relevant spectral parameters from the complete HSI color space data. Instead of utilizing all possible color features, the system identifies and extracts specific spectral parameters (such as absorbance at key wavelengths, slope parameters, and characteristic ratios) that have the strongest correlation with rare earth component contents. This selective extraction reduces computational complexity while preserving the essential information needed for accurate detection.
Solution Approach 2:
The patent applies partial action by focusing on the most critical spectral parameters rather than processing the entire HSI color space comprehensively. By selecting a subset of parameters that provide the maximum detection value, the system achieves sufficient analytical depth without the computational burden of complete parameter analysis. This partial approach is optimized to deliver the necessary detection accuracy with reduced computational resources.
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 accurate on-site detection of multiple components, overcoming the limitations of prior methods and significantly improving prediction accuracy in rare earth separation processes.
Implementation Method 1
obtaining color characteristic values, in different color space, of an image of a cerium praseodymium/neodymium mixed solution
Implementation Method 2
establishing an extreme learning machine based multi-component content soft measurement model by using the H and S component first-order moment as input and by using the known content values of the cerium praseodymium/neodymium components as output
Data Source
AI summary
Described is a method for predicting multiple components' content in a case that rare earth ions with and without color feature coexist, and relates to component content prediction in rare earth extraction process. It is difficult to quickly/accurately detect component's content in rare earth extraction process. Because of relatively large difference between images' color features of CePr/Nd mixed solution with colorless Ce ions and Pr/Nd solution, detecting content method of single rare earth element based on color feature is no longer applicable. The method includes: first searching for H and S components with maximum correlation with component content in HSI color space; establishing ELM based multi-component content soft measurement model using H and S component first-order moment as input; and for uncertainty of initial weight and ELM (extreme learning machine) model's threshold, optimizing model parameters using genetic algorithm GA to optimize ELM model for component content prediction higher precision.