Paper
30 December 2024 Modeling analysis and application of moisture loss in storage bulker and tobacco cutter processes of the tobacco shreds production line
Yaoping Tang, Zhenxun Jin, Ben Guo, Liming Zhu
Author Affiliations +
Proceedings Volume 13394, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024); 133941G (2024) https://doi.org/10.1117/12.3052569
Event: International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024), 2024, Hohhot, China
Abstract
The stability of the moisture content at the front end of cut tobacco dryer (MC-FECD) reflects the ability to control the moisture content of tobacco leaves in the preprocessing section of the tobacco shreds production, and it affects the control effect of the moisture content of cut tobacco in the drying process. Therefore, this paper adopts the correlation analysis method to determine the factors that affect the MC-FECD, and proposes to establish a support vector machine learning model using the "Modeling method of moisture content difference". After research and production verification, it is found that the R2 of the "Modeling method of moisture content difference" established in this paper is improved by 58.33% compared with the direct Modeling method. Compared with the expert experience model, the standard deviation of the MC-FECD between batches of the intelligent production control model established in this paper is reduced from 0.06% to 0.03%. The qualified rate increased from 20% to 50%. This study provides a research foundation and ideas for stable regulation of MC-FECD.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yaoping Tang, Zhenxun Jin, Ben Guo, and Liming Zhu "Modeling analysis and application of moisture loss in storage bulker and tobacco cutter processes of the tobacco shreds production line", Proc. SPIE 13394, International Workshop on Automation, Control, and Communication Engineering (IWACCE 2024), 133941G (30 December 2024); https://doi.org/10.1117/12.3052569
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KEYWORDS
Moisture

Humidity

Modeling

Data modeling

Atmospheric modeling

Air temperature

Machine learning

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