Determining Empirical Relationship of Rubber Drying Process using Machine Learning
Issued Date
2023-01-01
Resource Type
ISSN
21593442
eISSN
21593450
Scopus ID
2-s2.0-85179513999
Journal Title
IEEE Region 10 Annual International Conference, Proceedings/TENCON
Start Page
647
End Page
650
Rights Holder(s)
SCOPUS
Bibliographic Citation
IEEE Region 10 Annual International Conference, Proceedings/TENCON (2023) , 647-650
Suggested Citation
Yimwadsana B. Determining Empirical Relationship of Rubber Drying Process using Machine Learning. IEEE Region 10 Annual International Conference, Proceedings/TENCON (2023) , 647-650. 650. doi:10.1109/TENCON58879.2023.10322323 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/91564
Title
Determining Empirical Relationship of Rubber Drying Process using Machine Learning
Author(s)
Author's Affiliation
Other Contributor(s)
Abstract
Rubber is considered an important material for humankind and it is one of the most important products in Southeast Asian countries. However, the production of rubber could harm the environment due to the conventional use of acid and salt. We propose a rubber drying process using heat and constructed a rubber heating tunnel. We also propose a strategy to determine the time it takes to dry rubber so that the rubber is sufficiently dried without overheating at different temperature levels. We found that this strategy could not make use of conventional curve fitting methods based on least squares since it cannot handle discrete or categorical input data very well. We propose a non-linear Machine Learning regression technique based on neural network and found that neural network has the ability to predict the output variable quite well despite the input variables contain discrete or categorical values.