Modeling of the soybean oil bleaching and optimization of its conditions in the refining process for environmental interest

Authors

  • Zahir Henache
  • Abdelhamid Boukerroui
  • Imad Kashi

DOI:

https://doi.org/10.2478/nbec-2018-0005

Keywords:

Bleaching, Bleaching clay rejects, Modeling, Pigments, Pollution, Soybean oil

Abstract

In this work, we propose mathematical models describing the soybean oil bleaching process as a function of its parameters (temperature: 80 – 120 °C, clay dosage: 0.25 – 2 %, contact time: 10 – 30 min). The crude soybean oil visible spectrum shows three values of maximum wavelength (λmax). A value at 426 nm corresponding to the chlorophyll-a, the values at 451 and at 479 nm were assigned to β-carotene pigment. The models were developed using multiple linear regression analysis (MLRA) and were performed with Matlab programming language. The input variables are the temperature (X1), the clay dosage (X2) and the contact time (X3). The output parameter is the bleaching capacity (Y in % uptake). Statistical analysis methods were used to analyze and to confirm the reliability of the selected models. The optimal bleaching conditions for the soybean oil were: temperature 100 °C; clay dosage 2 % w/w and contact time 30 min. The highest bleaching capacity was found to be 81.04 % at 426 nm, 90.60 % at 451 nm and 93.66 % at 479 nm. The developed models allowed predicting the bleaching capacity representing the removal of the β-carotene and chlorophyll-a pigments present in the crude soybean oil at each λmax. Also they allowed a better control of the most influencing parameters on the bleaching step and contribute to the optimization of the spent bleaching clay rejects by optimizing the amount of bleaching clay used in the refining process; consequently, to reduce risks of pollution.

Downloads

Published

2018-09-19

How to Cite

Henache, Z. ., Boukerroui, A. ., & Kashi, I. (2018). Modeling of the soybean oil bleaching and optimization of its conditions in the refining process for environmental interest. Nova Biotechnologica Et Chimica, 17(1), 48–57. https://doi.org/10.2478/nbec-2018-0005

Issue

Section

Research Articles