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Robust regression modeling based dried slice flue gas ammonia prediction method

A robust regression and flue gas technology, applied in measurement devices, instruments, scientific instruments, etc., can solve the problems of high cost, labor and time, and achieve the effect of ensuring robustness, reducing detection costs, and improving detection efficiency.

Active Publication Date: 2014-10-15
CHINA TOBACCO YUNNAN IND
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the existing technology to detect NH in flue gas of baking sheet 3 The process of data is time-consuming, labor-intensive, and extremely expensive. The present invention proposes a method based on robust regression modeling to predict flue gas ammonia from roasted slices.

Method used

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  • Robust regression modeling based dried slice flue gas ammonia prediction method
  • Robust regression modeling based dried slice flue gas ammonia prediction method
  • Robust regression modeling based dried slice flue gas ammonia prediction method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] (1) Compare the physical and chemical data of the known baked sheet with the flue gas NH 3 The data is listed correspondingly, and a data sample set is established, in which the physical and chemical data include total sugar, reducing sugar, nicotine, total volatile alkali, total nitrogen, nicotine nitrogen, protein, Schmuck value, nitrogen-alkali ratio, chlorine, potassium, sugar Alkali ratio and ammonia base, as shown in the table below:

[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 flue gas NH 3 The column vector y of the data, the physical and chemical data and the flue gas NH are calculated respectively by the following formula 3 The linear correlation coefficient r:

[0053] (1)

[0054] In the formula: x is a column vector of some physical and chemical data, y is flue gas NH 3 The column vector of the data; get the physical and chem...

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 baked slices to be tested, that is, total sugar = 25.94, reducing sugar = 22.43, total volatile alkali = 0.28, total nitrogen = 1.9, protein = 9.43, Shimu Ke Value = 2.75, nitrogen-alkaline ratio = 0.84, and ammonia-alkali = 0.04 are applied as input variables to the prediction model in step (3), and the flue gas NH of the roasted sheet to be tested can be calculated and calculated. 3 The model predicted value Y=-30.08507-0.22834*total sugar+0.66854*reducing sugar+185.12201*total volatile base-77.63949*total nitrogen+13.16111*protein-0.26502*Shimuke value+11.63123*nitrogen-alkaline ratio-183.04849*ammonia State base=9.135. In order to verify the reliability of the model prediction results, the traditional detection method was used to m...

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 baked slices to be tested, that is, total sugar = 28.01, reducing sugar = 24.86, total volatile alkali = 0.29, total nitrogen = 1.8, protein = 8.66, Shimu Ke Value = 3.24, nitrogen-alkaline ratio = 0.75, and ammonia-alkali = 0.04 are applied as input variables to the prediction model in step (3), and the flue gas NH of the roasted sheet to be tested can be calculated and calculated. 3 The model predicted value Y=-30.08507-0.22834*total sugar+0.66854*reducing sugar+185.12201*total volatile base-77.63949*total nitrogen+13.16111*protein-0.26502*Shimuke value+11.63123*nitrogen-alkaline ratio-183.04849*ammonia State base=8,591. In order to verify the reliability of the model prediction results, the traditional detection method was used to me...

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Abstract

The invention provides a robust regression modeling based dried slice flue gas ammonia prediction method. A model from physical and chemical index items to the flue gas ammonia is established through the existing dried slice physical and chemical data and flue gas ammonia data and a flue gas ammonia value of a dried slice can be directly predicted through the physical and chemical composition data of an unknown dried slice flue gas ammonia sample. According to the robust regression modeling based dried slice flue gas ammonia prediction method, the steps of rolling, combustion, flue gas capture, detection and the like which are performed by a traditional chemical method are omitted, meanwhile the robust regression model is adopted, the defects caused by singular value samples in the physical and chemical data or the flue gas data can be effectively overcome, the robustness of the model is ensured to a great extent, and accordingly the robust regression modeling is excellent in comparison with the ordinary linear regression modeling. Practice shows that the flue gas ammonia value of the dried slice can be effectively predicted, the detection efficiency is greatly improved, and the detection cost is reduced.

Description

technical field [0001] The invention relates to a method for predicting flue gas ammonia from 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. Flue gas ammonia (hereinafter referred to as: NH 3 ) mainly comes from nitrogen-containing compounds in tobacco. Ammonia not only affects the...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01N33/00
Inventor 白晓莉彭国岗段如敏余贺龙周桂圆谢志强刘挺王保兴
Owner CHINA TOBACCO YUNNAN IND
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