Optimization of subcritical butane extraction for tiger nut oil based on artificial neural network coupled with PSO
In order to optimize the subcritical butane extraction process for dehulled tiger nut oil,single factor experiment was taken to determine the levels of the factor,central composite face-centered design(CCF)was used to optimize the subcritical extraction conditions,based on which response surface methodology(RSM)and back propagation artificial neural network(BP-ANN)models were constructed,respectively.The BP-ANN was optimized by particle swarm optimization(PSO),and the optimization results of the RSM model and PSO-BP-ANN model were compared.The optimal extraction conditions optimized by RSM and PSO-BP-ANN models were as follows:solid-liquid ratio(dehulled tiger nut:butane)was 1:10.36 g/mL,incubation time for 45 min,extraction temperature was 30℃,the rolling thickness was 0.5 mm;the solid-liquid ratio was 1:10.67 g/mL,the extraction time was 40.10 min,the extraction temperature was 34℃,and the thickness of the rolled billet was 0.5 mm.Under the optimal con-ditions,the predicted extraction rate of the RSM model was 91.63%,the experimental result was 94.27%,and the relative error was 2.56%.The prediction value of the PSO-BP-ANN model was 95.58%,the validation value was 95.14%,and the relative error was 0.46%.The artificial neural network coupled particle swam optimization(PSO-BP-ANN)was used to optimize the subcritical extraction process of tiger nut oil,which had advantages of high ex-traction rate and small error.This study can provide a reference for the application of subcritical extraction technolo-gy in the efficient production of tiger nut oil.
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维普
