Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Method for predicting baked piece smoke hydrogen cyanide based on robust regression modeling

A robust regression and hydrogen cyanide technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve laborious, time-consuming, high-cost problems, reduce detection costs, ensure robustness, and improve detection efficiency Effect

Active Publication Date: 2014-11-12
CHINA TOBACCO YUNNAN IND
View PDF4 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problems of time-consuming, laborious and extremely high cost in the process of detecting the HCN data of the flue gas of the grilled slices in the prior art, the present invention proposes a method based on robust regression modeling to predict hydrogen cyanide in the flue gas of the grilled slices

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Method for predicting baked piece smoke hydrogen cyanide based on robust regression modeling
  • Method for predicting baked piece smoke hydrogen cyanide based on robust regression modeling
  • Method for predicting baked piece smoke hydrogen cyanide based on robust regression modeling

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] (1) List the physical and chemical data of the known baked slices and the flue gas HCN data 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 HCN data, the linear correlation coefficient r between each physical and chemical data and flue gas HCN 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 HCN data; the linear correlation coefficient r between the physical an...

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 roasted slices to be tested are nicotine=2.26, total volatile alkalis=0.28, total nitrogen=1.9, nicotine nitrogen=0.39, protein=9.43, wood Gram value = 2.75 is applied as an input variable to the prediction model of step (3), and the model prediction value Y=106.63990-304.78669*nicotine-62.56795*total volatile alkalis+2899.43742 can be obtained *Total Nitrogen-1101.50935*Nicotine Nitrogen-459.45547*Protein-12.02897*Shimug value=113.9007. In order to verify the reliability of the prediction results of the model, the traditional detection method was used to measure the HCN value of the flue gas of the baked sheet: 112.25.

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 roasted slices to be tested are nicotine=2.4, total volatile alkalis=0.29, total nitrogen=1.8, nicotine nitrogen=0.42, protein=8.66, wood The gram value=3.24 is applied to the prediction model in step (3) as an input variable, and the model prediction value Y=106.63990-304.78669*nicotine-62.56795*total volatile alkalis+2899.43742 can be obtained *Total Nitrogen-1101.50935*Nicotine Nitrogen-459.45547*Protein-12.02897*Shimug value=95.5023. In order to verify the reliability of the prediction results of the model, the HCN value of the flue gas of the baked sheet was determined to be 96.07 by using the traditional detection method.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention provides a method for predicting baked piece smoke hydrogen cyanide based on robust regression modeling. A model from the physical and chemical indicator items to smoke HCN is established through existing baked piece physical and chemical data and smoke HCN data, and for unknown baked piece smoke HCN samples, the baked piece smoke HCN value can be directly predicted through physical and chemical component data of the samples. By means of the method, the steps of coiling, combustion, smoke capture, detection and the like in a traditional chemical method are omitted; meanwhile, due to the adoption of a robust regression model, the defects caused by singular value samples in the physical and chemical data or the smoke data can be effectively overcome; compared with ordinary linear regression modeling, robust regression modeling has the superior advantage that robustness of the model is ensured to a great extent. The practice proves that the smoke HCN value in the baked pieces can be effectively predicted through the model, detection efficiency is greatly improved, and detection cost is reduced.

Description

technical field [0001] The invention relates to a method for predicting hydrogen cyanide in flue gas of baked 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. The traditional way to obtain the HCN data of the flue gas of the grill is to detect the chemical composition indicators in the flue g...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 白晓莉段如敏彭国岗朱勇谢志强周桂圆王保兴刘挺
Owner CHINA TOBACCO YUNNAN IND
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com