DOI: https://doi.org/10.14710/medstat.17.1.36-44

A-OPTIMAL DESIGN IN NON-LINEAR MODELS TO INCREASE SILICON DIOXIDE PURITY LEVELS

Ghea Weisha  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia
Erfiani Erfiani  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia
Irzaman Irzaman  -  Department of Physics, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia
*Utami Dyah Syafitri  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University, Indonesia
Open Access Copyright (c) 2024 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract

Silica is the most mineral found on earth and is widely used in industry. Silica used in industry is usually silicon dioxide with a purity ≥ 95% and its often sold at a higher cost. To obtain the silica at a lower cost, silica extraction from biomass such as rice husk can be conducted. The purity of silica extracted from biomass tends to be lower than that of mineral silica. Silica with low purity can be increased by adjusting the temperature and the rate of temperature rise. This research aims to obtain the best design to determine the purity of silicon dioxide. The design of this study was generated based on the A-optimality criterion using the DETMAX algorithm. The A-optimality criterion is minimizing the trace of the variance-covariance of the parameter estimation. The best design points obtained using A-optimal design consist of three temperature groups: the minimum temperature of 800°C, the middle temperature of 850°C, and the maximum temperature of 900°C, with varying rates of temperature rise. Points were repeated at the temperature of 850°C, with rates of temperature rise of 1.67°C/min and 3.34°C/min. 

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Keywords: Optimal Design; A-Optimal; Silicon Dioxide

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Language : EN
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  1. Atkinson, A. C., Donev, A. N., & Tobias, R. D. (2007). Optimum Experimental Design, with SAS. Oxford: Oxford University Press
  2. Bartle, R. B., & Sherbert, D. R. (2011). Introduction to Real Analysis (Fourth Edi). New York: John Wiley and Sons, Inc
  3. Casnan, Noor, E., Hardjomidjojo, H., Irzaman, & Rohaeti, E. (2019). Scaling up of the pyrolysis process to produce silica from rice husk. Journal of Engineering and Technological Sciences, 51(6), 747–761. https://doi.org/10.5614/j.eng.technol.sci.2019.51.6.1
  4. Coniwanti, P., Srikndhy, R., & Apriliyanni. (2008). Pengaruh proses pengeringan,normalitas HCl, dan temperatur pembakaran pada pembuatan silika Dari Sekam Padi. Jurnal Teknik Kimia, 15(1), 5–11
  5. de Aguiar, P. F., Bourguignon, B., Khots, M. S., Massart, D. L., & Phan-Than-Luu, R. (1995). D-optimal designs. Chemometrics and Intelligent Laboratory Systems, 30(2), 199–210. https://doi.org/10.1016/0169-7439(94)00076-X
  6. Har, N. P., Irzaman, & Irmansyah. (2019). Crystallinity and electrical properties of silicon dioxide (SiO2) from rice straw. AIP Conference Proceedings, 020028-1-020028–6. https://doi.org/10.1063/1.5141641
  7. Irzaman, Oktaviani, N., & Irmansyah. (2018). Ampel Bamboo Leaves Silicon Dioxide (SiO2) Extraction. IOP Conference Series: Earth and Environmental Science, 141(1), 4–12. https://doi.org/10.1088/1755-1315/141/1/012014
  8. Jones, B., Allen-Moyer, K., & Goos, P. (2020). A-optimal versus D-optimal design of screening experiments. Journal of Quality Technology, 53(4), 369–382. https://doi.org/10.1080/00224065.2020.1757391
  9. Jorena. (2009). Menentukan Energi Gap Semikonduktor Silikon Melalui Pengukuran Resistansi Bahan pada Suhu Beragam. Jurnal Penelitian Sains, 12(1), 1–3
  10. Luh, B. S. (1991). Rice Production (second edit). USA: Van Nostard Reinhold. https://doi.org/10.2307/920283
  11. Masrur, Irmansyah, & Irzaman. (2013). Optimasi Kelajuan Pemanasan Pada Ekstraksi. Jurnal Biofisika, 9(2), 13–20
  12. Mitchell, T. J. (2000). An algorithm for the construction of "D-Optimal" experimental designs. Technometrics, 42(1), 48–54. https://doi.org/10.1080/00401706.2000.10485978
  13. Montgomery, D. C. (2013). Design and Analysis of Experiments (Eighth Edi). New York: John Wiley and Sons, Inc. https://doi.org/10.2307/3009858
  14. Rivai, M., Sartono, B., Erfiani, & Irzaman. (2018). G Optimal Design in Non linear Models to Increase Silicon Oxide Purity Levels and Electrical Conductivity. International Journal of Scientific Research in Science, Engineering and Technology, 4(11), 150–155. https://doi.org/10.32628/ijsrset21841110
  15. Sa’diyah, H., Nurhimawan, S., Fatoni, S. A., Irmansyah, I., & Irzaman, I. (2016). Ektraksi Silikon Dioksida Dari Daun Bambu. V, SNF2016-BMP-13-SNF2016-BMP-16. https://doi.org/10.21009/0305020303
  16. Yang, M., Biedermann, S., & Tang, E. (2013). On optimal designs for non-linear models: A general and efficient algorithm. Journal of the American Statistical Association,

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