Method for predicting flue gas CO content of flue-cured tobacco slices based on robust regression modeling

A robust regression and carbon monoxide technology, applied in forecasting, technical management, data processing applications, etc., can solve the problems of high cost, laborious, time-consuming, etc., to achieve the effect of ensuring robustness, reducing detection cost, and improving detection efficiency

Active Publication Date: 2015-04-29
CHINA TOBACCO YUNNAN IND
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] In order to solve the problems of time-consuming, laborious, and extremely high cost in the process of detecting CO data in flue gas from grilled sl...

Method used

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  • Method for predicting flue gas CO content of flue-cured tobacco slices based on robust regression modeling
  • Method for predicting flue gas CO content of flue-cured tobacco slices based on robust regression modeling
  • Method for predicting flue gas CO content of flue-cured tobacco slices based on robust regression modeling

Examples

Experimental program
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Effect test

Embodiment 1

[0050] (1) List the physical and chemical data of the known baked slices and the CO data of the flue gas correspondingly, and establish a data sample set, in which the physical and chemical data include total sugar, reducing sugar, nicotine, total volatile alkali, total nitrogen, nicotine nitrogen, protein , Shimuke value, nitrogen-alkaline ratio, chlorine, potassium, sugar-alkaline ratio and ammoniacal alkali, as shown in the following table:

[0051]

[0052] (2) Calculate the column vector x of each physical and chemical data in the data sample set obtained in step (1) 1 ~x n and the column vector y of flue gas CO data, the linear correlation coefficient r between each physical and chemical data and flue gas CO is calculated by the following formula:

[0053] (1)

[0054] In the formula: x is a column vector of some physical and chemical data, y is the column vector of flue gas CO data; the linear correlation coefficient r between the physical...

Embodiment 2

[0098] Same as steps (1) to (3) of Example 1, only replace other roasted slices to be tested, step (4) is as follows:

[0099] According to the characteristic index items selected in step (2), the corresponding physical and chemical data of the grilled slices to be tested, that is, chlorine = 0.23, potassium = 1.91, are applied as input variables to the prediction model in step (3), and the roasted slices to be tested can be calculated. The model prediction value of flue gas CO in the slice Y=13.25193+1.35784*chlorine-0.53972*potassium=12.533. In order to verify the reliability of the prediction results of the model, the traditional detection method was used to measure the CO value of the flue gas of the baked sheet: 12.2.

Embodiment 3

[0101] Same as steps (1) to (3) of Example 1, only replace other roasted slices to be tested, step (4) is as follows:

[0102] According to the characteristic index items selected in step (2), the corresponding physical and chemical data of the grilled slices to be tested, that is, chlorine = 0.37, potassium = 2.03, are applied as input variables to the prediction model in step (3), and the roasted slices to be tested can be calculated. The model prediction value of flue gas CO in the slice Y=13.25193+1.35784*chlorine-0.53972*potassium=12.659. In order to verify the reliability of the prediction results of the model, the traditional detection method was used to measure the CO value of the flue gas of the baked sheet: 12.8.

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Abstract

The invention provides a method for predicting the flue gas CO content of flue-cured tobacco slices based on robust regression modeling. A model from physicochemical indexes to flue gas CO is created according to existing physicochemical data and flue gas CO data of the flue-cured tobacco slices, and for an unknown flue gas CO sample of flue-cured tobacco slices, the flue gas CO content of the flue-cured tobacco slices can be directly predicted by physicochemical component data of the flue-cured tobacco slices. According to the method, the steps of winding, burning, gas capture, detection and the like in a conventional chemical mode are removed; meanwhile, a robust regression model is adopted, so that the disadvantages caused by singular value samples in the physicochemical data or the flue gas data can be effectively avoided, the robustness of the model is guaranteed to a very great extent, and the robust regression modeling is superior to general linear regression modeling. The practice proves that the model can effectively predict the flue gas CO content of the flue-cured tobacco slices, the detection efficiency is greatly improved, and the detection cost is reduced.

Description

technical field [0001] The invention relates to a method for predicting carbon monoxide in flue gas of grilled slices based on robust regression modeling, and belongs to the technical field of specific calculation models. Background technique [0002] Tobacco smoke is an extremely complex mixture produced by the burning, cracking and distillation of tobacco during cigarette smoking. The harmfulness of cigarette products to the human body is produced through the process of burning and smoking. The harmful components in the smoke are mainly formed during the combustion process, and the chemical characteristics of the smoke change with the internal chemical components of the tobacco leaf raw materials. Therefore, the chemical properties of raw materials of cigarette tobacco leaves determine the chemical properties and safety of cigarette smoke. Carbon monoxide (hereinafter referred to as: CO) is a product of incomplete combustion of tobacco, which directly affects human healt...

Claims

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Application Information

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IPC IPC(8): G06Q10/04
CPCY02P90/84G06Q10/04
Inventor 白晓莉魏帅吴丽君段如敏余贺龙王保兴朱勇卢伟刘挺
Owner CHINA TOBACCO YUNNAN IND
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