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Principal component analysis and deep neural networks in modeling the melt flow index of degradable plastics

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Abstract

Melt flow index (MFI) is one of the physical properties of degradable plastics that must be considered in the production of well-degradable plastics. This is because MFI affects some of the mechanical properties of degradable plastics. This study aims to model the MFI of degradable plastics using two nonparametric methods, namely principal component analysis (PCA) and deep neural networks (DNN). PCA was used to reduce the dimension of the input data set in the first stage of modeling. Then, the DNN was used as an alternative model to estimate the accuracy of predicting the MFI of degradable plastics. The result shows that the model accuracy is very good, with a least mean squared error of 0.00004 and a mean absolute percentage error of 2.46% using 4 principal components. The study also highlights the advantages of combining PCA and DNN in modeling MFI for degradable plastics and provides some ideas for further research.

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Acknowledgements

The authors thank Wan Hasamudin Wan Hasan of the Malaysian Palm Oil Board for providing data on the physical properties of degradable plastics. This research was also supported by the Ministry of Higher Education (MoHE) through the Fundamental Research Grant Scheme (FRGS/1/2019/STG06/UNIKL/02/3).

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Authors and Affiliations

  1. Department of Mathematics, Universiti Kuala Lumpur Malaysia France Institute, 43650, Bandar Baru Bangi, Selangor, Malaysia

    Syamsiah Abu Bakar

  2. Faculty of Science and Technology, School of Mathematical Sciences, Universiti Kebangsaan Malaysia, UKM, 43600, Bangi, Selangor Darul Ehsan, Malaysia

    Syamsiah Abu Bakar & Saiful Izzuan Hussain

  3. France Collaboration Programme, Universiti Kuala Lumpur Malaysia France Institute, 43650, Bandar Baru Bangi, Selangor, Malaysia

    Mourad Zirour

  4. Department of Software Engineering Information System, Universiti Kuala Lumpur Malaysian Institute of Information Technology, 50250, Wilayah Persekutuan Kuala Lumpur, Malaysia

    Mohammad Faizuddin Md Noor

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  1. Syamsiah Abu Bakar
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Correspondence to Syamsiah Abu Bakar.

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Bakar, S.A., Hussain, S.I., Zirour, M. et al. Principal component analysis and deep neural networks in modeling the melt flow index of degradable plastics. Int J Adv Eng Sci Appl Math (2023). https://doi.org/10.1007/s12572-023-00352-5

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