Modeling and Simulation
Hadis Tanha; Fatemeh Bashipour
Abstract
Time-consuming and costly experiments to measure cetane number (CN) of biodiesel make computations even more valuable. In the current study, two artificial intelligence (AI) models have been used to predict the biodiesel CN by using comprehensive datasets (440 datasets). They were the gradient-based ...
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Time-consuming and costly experiments to measure cetane number (CN) of biodiesel make computations even more valuable. In the current study, two artificial intelligence (AI) models have been used to predict the biodiesel CN by using comprehensive datasets (440 datasets). They were the gradient-based artificial neural network (GB-ANN) and the multi-layer-perceptron ANN optimized by the genetic algorithm (GA-ANN) for the first time. Three model's input variables for predicting the target variable of the biodiesel CN are the average number of carbon atoms, the average number of double bonds, and the average molecular weight of the fatty acid methyl esters. The learning function, transfer function, number of hidden layers, and number of neurons in the hidden layers are some of the optimized parameters in the current AI-models. The developed models were compared using statistical criteria such as the coefficient of determination (R2), the mean square error (MSE), the average absolute relative deviation (AARD), the standard deviation (STD), and the mean absolute percentage error (MAPE). The resulting outcomes revealed that the highest R2 and the lowest MSE are related to the GB-ANN model with two hidden layers, trainbfg learning method, and the logsig-tansig-purelin transfer function. The R2 and MSE for the optimized model are equal to 0.9296 and 0.0005, respectively. Although the GA-ANN achieved acceptable outcomes, its statistical analyses produced weaker outcomes than the AI-model based on GB-ANN.