Tobacco dampening machine discharge moisture content prediction method based on gradient boosting tree

Abstract

The invention discloses a tobacco dampening machine discharge moisture content prediction method based on a gradient boosting tree and belongs to the tobacco field. According to the tobacco dampeningmachine discharge moisture content prediction method based on a gradient boosting tree. complete process parameters and environmental parameters of the tobacco dampening machine as independent variables, a dampening prediction model is established by using a gradient boosting tree algorithm, the influence of different influence factors on the discharge moisture content of the tobacco dampening machine is fully considered, and meanwhile, the prediction and interpretation capabilities of the prediction model on nonlinear characteristics of a dampening process are enhanced. The model can predictthe moisture content of the discharged material without human experience intervention in the working environment of the actual moisture regaining process, the precision of the prediction result is high, and the calculation speed is high. According to the prediction method, the discharging moisture content and the required water adding amount of the dampening machine can be automatically calculated, and then the temperature and humidity of tobacco leaves are more effectively controlled, so that the quality of tobacco shreds is improved.

Description

technical field [0001] The invention belongs to the field of tobacco, and more specifically relates to a method for predicting the moisture content of a tobacco conditioner discharge based on a gradient boosting tree. Background technique [0002] Loose moisture regain is an important part of the tobacco production process and plays a key role in the quality and taste of cigarettes. With the improvement of the level of automation and intelligence in the tobacco industry, the requirements for the temperature and humidity control of the discharge of the loose moisture conditioner are further increased. However, in actual operation, due to the large number of variables and complex interrelationships, it is difficult to establish an effective method of controlling the moisture content of the discharge material. The moisture content of the discharge material for resurgence depends on the operator's manual adjustment using experience or a simple prediction model, such as based on ...

AI Technical Summary

Problems solved by technology

[0003] However, the method based on linear regression fails to fully consider and integrate the complexity of the actual production process of tobacco resurgence. The use of relatively simple prediction methods such as traditional process combination improvement or linear regression has insufficient ability to deal with the complexity of the system, and at the same time, it cannot Fully consider the joint influence of environmental parameters and production process on the moisture content of the output

The accuracy of the calculated results is limited and cannot be reliably used in the prediction of the moisture content of the output of the moisture regainer

[0004] In the early research of moisture resurfacing machines, limited by hardware conditions and data acquisition capabilities, the types of parameters, data quantity and granularity used for model calculation and fitting were insufficient, which was not conducive to establishing a complex and reliable moisture resurgence model

At the same time, the maturity of mathematical theory, especially machine learning theory, and the level of research on industrial production processes are relatively low, and there is a lack of solutions that combine complex machine learning models with resurgence processes

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