predicting the cetane number of biodiesel using two ai-models: the gradient-based ann and ann optimized by genetic algorithm

Time-consuming and costly experiments to measure the 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. the three input variablesof the model for predicting the target variable of the biodiesel cn are the average number of carbon atoms, average number of double bonds, and 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), mean square error (mse), average absolute relative deviation (aard), standard deviation (std) and mean absolute percentage error (mape). the resulting outcomes revealed that the highest r² and the lowest mse were related to the gb-ann model with two hidden layers, trainbfg learning method and 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.