Estimation of production efficiency in the extraction of essential oil from orange peel using neural networks - Fajardo Muñoz, Sandra Elvira; Freire Castro, Anthony Josue; Mejía Garzón, Michael Isaac (Ingeniería Química y Desarrollo, 2024)
In this work, a feedforward Artificial Neural Network (ANN) with 9 hidden layers and backpropagation (BP) training algorithms and Levenberg-Marquardt (LM) weight adjustment algorithms were used for the prediction of oil extraction yield from the orange peel (Citrus sinensis), For training and validation, data were used in the amount of load in grams as an input variable and the oil yield in percentage as an output variable, which were obtained in the distillation technique by steam entrainment using the Clevenger trap. Different architectures were studied by varying the number of neurons in the hidden layer, finding that the ANN with 9 neurons provided the best fit of the experimental data, which indicates greater efficiency and accuracy compared to the other architectures analyzed. Regarding the experimental data, the percentage mean square error (MSE%) and the determination coefficient , were evaluated, finding for the ANN values ​​of MSE%=0.0040 and =0.9929, proving that the hypothesis research is true. These results show the efficacy and potential of using neural networks for modeling and prediction of orange oil extraction performance within the domain of training data.