Comparative Investigation of n-Hexane and Ethanol Solvents Used in Eleais guinesis Kernel Oil Extraction and Optimization via Two Computational Modelling

The global usages of oil seed products are on high demand; which gave rise to the need to optimize the extraction of Elaeis guinness kernel oil. This work investigated the performance of n-hexane and ethanol as solvents for extraction and optimization of Elaeis guinesis kernel oil via Response System Methodology (RSM) and Artificial Neural Networks (ANNs) computational modeling. The 5 days sun-dried Elaeis guinesis Seeds collected were crushed, the oil was extracted from the powdered seed using a Soxhlet extractor, with n-hexane and ethanol as solvents. The result analyzed by average computation of 40min extraction time, 175 ml solvents, and 50g sample weight for both solvents shown that the average oil yield for n-hexane is 38.15% (w w-1) and 28.83% (w w-1) for ethanol. At the box-Behnken experimental design having the same averaged independent variables, the average predicted values of: RSM is 35.21; ANNs is 37.21 for n-hexane solvent, while for ethanol solvent, the average predicted values of: ANNs is 31.118; RSM is 30.80. The coefficients of determination (R2) for RSM were 99.94% for n-hexane and 99.89% (w w-1) for ethanol, and ANN has 99.99% (w w-1) for n-hexane and 99.899% (w w-1). As a result; n-hexane is better than ethanol in term of oil extraction, ANNs has higher predicted values for optimization in both solvents, therefore it is a better model for oil’s optimization, it further proved that both models can be used adequately to represent the actual relationship of the chosen factors which can be applied for optimization simultaneously.

Keywords:

Elaeis guinesis kernel oil, Box-Behnken, Soxhlet Extractor, Solvents ANNs,

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