Systems and methods for boosting coal quality measurement statement of related cases

Patent metadata

Field	Value
Publication number	US-2016018378-A1
Application number	US-201414772088-A
Country	US
Kind code	A1
Filing date	Feb 13, 2014
Priority date	Mar 7, 2013
Publication date	Jan 21, 2016
Grant date	—

Abstract

Properties of coal are determined from samples processed by a near-infrared spectroscopy (NIR) device that generates wavelengths dependent spectra. Target values of the properties are associated with the NIR spectra by a kernel based regression model generated from training data based on an anisotropic kernel function that is extended by defining the kernel parameters as a smooth function over the wavelengths associated with a spectrum. Like the anisotropic case each wavelength related dimension has its own kernel parameter. Adjacent dimensions are restricted to have similar kernel parameters. Measured spectra with a limited number of features are reconstructed by applying a regression model based on training data of spectra having an extended number of features. Training data are pruned based on a regression model by removing outliers.

First claim

1 . A method for determining a property of a material from data generated by a near-infrared spectroscopy device, comprising:
obtaining wavelength based training data related to the material; a processor using the wavelength based training data to learn an anisotropic Gaussian kernel function with a wavelength based kernel parameter that is defined by a smooth function over the wavelength determined by at least one parameter; and the processor applying the anisotropic Gaussian kernel function to wavelength based test data of one or more samples of the material generated by the near-infrared spectroscopy device to determine the property.

2 . The method of claim 1 , wherein the smooth function is a smooth Gaussian function and the at least one parameter is a decay parameter.

3 . The method of claim 1 , wherein the material is coal.

4 . The method of claim 1 , wherein the property is heatan.

5 . The method of claim 2 , wherein the wavelength based kernel parameter that is defined by a smooth Gaussian function over the wavelength, is expressed as:
γ(d)=γ 0 exp(−β(l(d)−l 0 ) 2 ), wherein: d is an index value related to the wavelength; γ(d) is the wavelength based parameter; γ 0 is a maximum value of the wavelength based parameter; β is the decay parameter; l(d) is the wavelength at index value d; and l 0 a wavelength value for which the wavelength based parameter reaches the maximum value.

6 . The method of claim 5 , further comprising:
the processor learning a kernel ridge regression for an isotropic kernel from the training data; the processor determining a regularization factor and γ 0 ; the processor applying an initialization value for β and determining l 0 ; and the processor determining an operational value for β.

7 . The method of claim 6 , further comprising:
the processor applying the kernel ridge regression to the wavelength based training data to determine a first plurality of target values; the processor determining a standard deviation from the first plurality of target values; the processor identifying a reduced plurality of sets of training data by removing at least one set of training data from the wavelength based training data based on the standard deviation; and the processor applying the kernel ridge regression to the reduced plurality of sets of training data to determine a second plurality of target values.

8 . A method to reconstruct a feature in test data related to a material obtained with a near-infrared spectroscopy device, comprising:
storing on a memory near-infrared spectroscopy training data from the material including data of a first and a second set of features which do not overlap; creating with a processor a predictive feature model to predict features appearing in the second set of features in the training data from the first set of features in the training data by using the first and second set of features in the training data; obtaining with the near infra-red spectroscopy device test data from the material including test data related to the first set of features; and predicting a second set of features related to the test data of the material by applying the predictive feature model.

9 . The method of claim 8 , further comprising:
combining the first set of features and the predicted second set of features related to the test data to create a predictive model for a property of the material.

10 . The method of claim 8 , wherein each first set of features relates to a first range of wavelengths in NIR spectroscopy and each second set of features relates to a second range of wavelengths in NIR spectroscopy.

11 . The method of claim 8 , wherein the first range of wavelengths includes wavelengths shorter than 2300 nm and the second range of wavelengths includes wavelengths greater than 2300 nm.

12 . The method of claim 8 , wherein the predictive feature model is based on a multivariate statistical method.

13 . The method of claim 12 , wherein the multivariate statistical method is a kernel ridge regression method.

14 . The method of claim 9 , wherein the material is coal and the property is a calorific value.

15 . A method for determining a property of a material with data generated by a spectroscopy device, comprising:
a processor receiving a first plurality of sets of training data generated by the spectroscopy device; the processor generating a regression model from the first plurality of sets of training data to determine a first plurality of target values, which is representative of the property of the material; the processor determining a standard deviation from the first plurality of target values; the processor identifying a second plurality of sets of training data by removing at least one set of training data from the first plurality of sets of training data based on the standard deviation; and the processor generating a regression model from the second plurality of sets of training data to determine a second plurality of target values.

16 . The method of claim 15 , further comprising:
the processor generating a regression model from a remaining plurality of sets of training data to determine a remaining plurality of target values; the processor determining a new standard deviation from the remaining plurality of target values; and the processor determining if any of the sets of training data of the remaining plurality of sets of training data should be removed based on the new standard deviation.

17 . The method of claim 16 , wherein none of the sets of training data is removed from the remaining plurality of sets of training data and the regression model based on the remaining plurality of sets of training data is applied by the processor to determine a target value from a set of test data generated by the spectroscopy device.

18 . The method of claim 15 , wherein the material is coal and the spectroscopy device is a near-infrared spectroscopy device.

19 . The method of claim 15 , wherein the removing of at least one set of training data from the first plurality of sets of training data is based on a 3σ range.

20 . The method of claim 15 , wherein the property is a calorific value of coal.

Assignees

• Siemens Ag

Inventors

• Yuan Chao
• Chakraborty Amit
• Hackstein Holger
• Zhang Ping
• Lan Liang

Classifications

• G01N33/222 Primary

  Solid fuels, e.g. coal · CPC title

• G01N21/359

  using near infrared light · CPC title

• G01N21/3563

  for analysing solids; Preparation of samples therefor · CPC title

• G01J3/28

  Investigating the spectrum (using colour filters G01J3/51) · CPC title

• G01N2201/129

  Using chemometrical methods · CPC title