Abstract
In this study, a supervised learning algorithm known as multi-layer perceptron neural network (MLP-NN) is employed to predict the dissociation pressure of semiclathrate hydrates of CO2+N2, CH4+N2, CO2+CH4, CO2, CH4, and N2 systems in the presence of different concentrations of TBAB. The hydrate dissociation pressure was assumed as a function of the molar concentrations of the gases, the weight percent of TBAB tetra n-butyl ammonium bromide (TBAB) and the system temperature. A data set of 349 data points gathered from the open literature and divided into 70%, 15%, and 15% ratio for three subsets of training, validation and testing, respectively. The optimum network structure was selected through a trial and error procedure with the overall R2 of 0.97, MSE of 0.16, and AARD% of 6.19%. While both graphical and statistical measures approved the robustness and performance of the network, the applicability of the model and quality of experimental data was investigated through Leverage approach.