MODELLNG AND OPTIMISATION OF OIL EXTRACTION FROM LOOFAH (Luffa cylindrica) SEEDS USING BINARY SOLVENT MIXTURE
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Date
2019
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Publisher
Turkish Chemical Society
Abstract
Toxicity and safety concern coupled with the recent increase in its price has necessitated the need for finding alternative solvents to n-hexane. In this study, the effect of binary solvent (ethanol/n-hexane) composition at various extraction temperatures and times on the oil yield was investigated using response surface methodology (RSM). Artificial neural network (ANN) was used as a modelling tool for predicting the oil yield and the performance of both ANN and RSM models was compared. The optimum oil yield (27.67%) was obtained at extraction temperature (40 °C), extraction time (151.9 min) and binary solvent composition (98% ethanol /2% n-hexane). The predicted oil yield values from ANN model was more accurate than that of RSM when compared with experimental values. The fatty acid profile revealed that the refining process promoted saturation of the extracted oil with 67.75% of palmitic acid present in refined loofah seed oil (RLSO). This study demonstrated the feasibility of using a binary mixture of ethanol and n-hexane as a suitable replacement to the commonly used toxic n-hexane solvent for the extraction of oil from loofah seeds.
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Keywords
Waste management, Luffa cylindrical, Artificial neural network, optimization, oil extraction