Mineral Wool Identification via NIR Spectroscopy
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Solution Overview
Problem
Inorganic fibers such as glass wool and rock wool are difficult to distinguish visually, posing challenges in the construction industry due to different disposal requirements, and existing methods lack accuracy in differentiation.
Innovation Solution
A method utilizing near-infrared (NIR) spectroscopy and multivariate data analysis to create calibration spectra for glass wool and rock wool, allowing for computer-aided determination of decision criteria to differentiate between the two materials based on their unique molecular absorption properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual inspection is used to distinguish mineral wools, then the method is simple and quick, but the differentiation accuracy is insufficient
Solution Approach 1:
The patent replaces manual visual inspection with an automated optical measurement system. A near-infrared spectrometer objectively measures absorption spectra of mineral wool samples, and a computer automatically analyzes the data to distinguish between glass wool and rock wool. This substitution of mechanical/visual methods with optical measurement and computational analysis resolves the contradiction by providing both automated operation and high differentiation accuracy.
2Measurement precision
If near-infrared spectroscopy with multivariate analysis is used, then the differentiation accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent employs a multivariate analysis system that simultaneously evaluates multiple spectral parameters (absorption intensities at different wavelengths, ratios between wavelengths) to perform differentiation. This multi-functional approach allows a single measurement system to capture comprehensive material characteristics and use computational algorithms to distinguish between mineral wool types, achieving high accuracy without requiring multiple separate measurement devices.
3Measurement precision
If complex multivariate analysis is applied to NIR spectra, then the classification accuracy is improved, but the data processing time increases
Solution Approach 1:
The patent pre-calculates and stores reference absorption spectra for pure components (glass, rock, binder materials) before actual sample analysis. During measurement, the system compares the sample spectrum against these pre-established references using predetermined evaluation criteria. This preliminary preparation of reference data enables rapid classification without requiring complex real-time computations, thus maintaining high accuracy while reducing processing time.
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
Enables accurate on-site identification of glass wool and rock wool, reducing measurement inaccuracies and allowing for proper disposal, with a system comprising a near-infrared spectrometer, computing unit, and memory for visualization of results.
Implementation Method 1
The principle of a near-infrared spectrometer is well understood. Excitation induces molecular vibrations in a sample. Depending on the molecular groups present in the sample, radiation is reflected or absorbed to varying degrees.
Implementation Method 2
Excitation induces molecular vibrations in a sample. Depending on the molecular groups present in the sample, radiation is reflected or absorbed to varying degrees.
Data Source
AI summary
Method for distinguishing between glass wool and rock wool with the following steps: providing near-infrared (NIR) spectra of glass wool and rock wool as calibration spectra; providing a sample containing either glass wool or rock wool; creating an NIR spectrum of the sample; defining a decision criterion for classifying the sample as glass wool or rock wool; determining the decision criterion using a multivariate method with the entries in the NIR spectrum of the sample and the NIR spectra as calibration spectra.