A nonlinear autoregressive exogenous neural network (NARX-NN) model for the prediction of solvent-based oil extraction from <i>Hura crepitans</i> seeds

Abstract

Abstract Vegetable oils are a crucial source of raw materials for many industries. In order to meet the rising demand for oil on global scale, it has become essential to investigate underutilized plant oilseeds. Hura crepitans seeds are one of the underused plant oilseeds from which oil can be produced via solvent-based extraction. For the purpose of predicting the oil yield from Hura crepitans seeds, the extraction process was modelled using a nonlinear autoregressive exogenous neural network (NARX-NN). The input variables to the model are seed/solvent ratio, extraction temperature and extraction time, while oil yield is the response output variable. NARX-NN model is based on 200 data samples, and model architecture comprises of 3 inputs, 1 hidden layer (with 15 neurons) and 1 output with 2 delay elements. The performance evaluation was carried out to examine the accuracy of the developed model in predicting oil yield from Hura crepitans using different statistical indices. The overall correlation coefficient, R (0.80829), mean square error, MSE (0.0120), root mean square error, RMSE (0.1080) and standard prediction error, SEP (0.1666) show that NARX-NN model can accurately be used for the prediction oil yield from Hura crepitans seeds.