Optimization of Zeolite-X Catalysed Palm Oil Transesterification Using Response Surface Methodology
- DOI
- 10.2991/978-94-6463-148-7_24How to use a DOI?
- Keywords
- palm oil; transesterification; zeolite-X; Response Surface Methodology (RSM)
- Abstract
In this study, response surface methodology was applied to optimize the transesterification of palm oil using zeolite-X prepared from rice husk silica and aluminum foil as a catalyst. For this purpose, response surface methodology (RSM) with a 3-level-3 factor central composite design was applied to investigate the effect of three experimental factors on the percentage of conversion of the oil into methyl esters. A quadratic model was derived from the RSM with the aid of analysis of variance (ANOVA) and Design Expert 6.0.8 software to predict oil conversion and reveals that the mathematical model is Y = 53.38 + 14.4X1 + 15.2053 X2 + 2.3 X3, suggesting that the most influencing factor for transesterification of palm oil is the ratio of oil to methanol.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Diska Indah Alista AU - Kamisah Delilawati Pandiangan AU - Khoirin Nisa AU - Wasinton Simanjuntak AU - Erika Noviana AU - Selvia Anggraini Hasan PY - 2023 DA - 2023/05/29 TI - Optimization of Zeolite-X Catalysed Palm Oil Transesterification Using Response Surface Methodology BT - Proceedings of the 12th International Conference on Green Technology (ICGT 2022) PB - Atlantis Press SP - 232 EP - 238 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-148-7_24 DO - 10.2991/978-94-6463-148-7_24 ID - Alista2023 ER -