Optimization of Bauhinia monandra seed oil extraction via artificial neural network and response surface methodology: A potential biofuel candidate
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Date
2015-01-30
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier B.V.
Abstract
The influence of sample weight, time, and solvent type and their reciprocal interactions on Bauhinia
monandra seed oil (BMSO) yield using artificial neural network (ANN) and response surface methodology (RSM) was investigated. Also, the BMSO obtained was characterized to determine its aptness for
oleochemical industry. Numerically predicted optimum values for the extraction process using RSM
model were found to be the same for the developed ANN model. The optimum values were sample
weight of 60 g, time of 100 min and petroleum ether with a corresponding BMSO yield of 14.8 wt%. Performance evaluation of the models by multiple coefficient of correlation (R), coefficient of determination
(R2) and absolute average deviation (AAD) showed that the ANN model was marginally better (R = 0.9995,
R2 = 0.9991, AAD = 0.27%) than the RSM model (R = 0.9993, R2 = 0.9986, AAD = 0.49%) in predicting BMSO
yield. Physicochemical properties of the BMSO such as acid value (7.56 mg KOH/g), indicated that it is
non-edible and the fatty acids profile showed that the oil was highly unsaturated (87.9%), which makes
it a potential candidate for biodiesel production.
Description
Industrial Crops and Products 67 (2015) 387–394
Keywords
Oilseed, Bauhinia monandra, Modeling, Optimization, Artificial neural network, Response surface methodology