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Quantitative study of thermal barrier models for paper-based barrier materials using adaptive neuro-fuzzy inference system

  • Zi`ang Xia , Long Wang , Chaojie Li , Xue Li , Jingxue Yang , Baoming Xu , Na Wang , Yao Li and Heng Zhang ORCID logo EMAIL logo
Published/Copyright: July 19, 2024
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Abstract

A composite silicone emulsion-biomass polymer paper-based barrier coating material with high barrier performance was prepared by double-layer coating, and the material was tested for oil repellency. The composition-structure-property data set of the paper-based barrier materials was constructed based on the experimental data. An adaptive neuro-fuzzy inference system (ANFIS) was used to construct a prediction model of the coating structure in high-temperature environments to achieve quantitative analysis of the barrier performance in high-temperature environments. The ANFIS prediction model was constructed based on two algorithms, the grid partitioning algorithm and the subtractive clustering algorithm, and the accuracy of the model determined by the two algorithms was compared for training, validation and testing of this experimental data. The results showed that the prediction model of the grid partitioning method had a better fit with the experimental data, with a root mean square error (RMSE) value of 7.00383 and a R-squared (R 2) of 0.9644 between the model prediction data and the actual data.


Corresponding author: Heng Zhang, College of Marine Science and Biological Engineering, Qingdao University of Science and Technology, Qingdao 260412, Shandong, China; and Zhejiang Key Laboratory of Alternative Technologies for Fine Chemicals Process, Shaoxing University, Shaoxing 312000, Zhejiang, China, E-mail:

Award Identifier / Grant number: ZR2022MB135

Acknowledgements

The author would also like to graciously thank Mengyuan Li and Shulan Hua for their investigation, data curation, and support. This study was funded by Shandong Provincial Natural Science Foundation of China (Grant No. ZR2022MB135), Open Fund of Zhejiang Key Laboratory of Alternative Technologies for Fine Chemicals Process, and the Graduate Student Independent Research and Innovation Program of Qingdao University of Science and Technology (Grant No. S2023KY039).

  1. Ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors.

  2. Consent for publication: All authors approved the manuscript and submission to this journal.

  3. Availability of data and materials: Data and materials available on request from the authors.

  4. Competing interests: The authors declare no competing interests.

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Received: 2023-11-04
Accepted: 2024-06-13
Published Online: 2024-07-19
Published in Print: 2024-09-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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