Developing Genetic Algorithm-Based Neural Networks and Sensitivity Analysis For Thermal Conductivity of Natural Gases

The artificial neural network (ann) approach was applied to develop simple correlations for predicting the thermal conductivity of nitrogenmethane and carbon dioxide-methane mixtures. the genetic algorithm method was used to obtain global optimum parameters (weights and biases) of the anns. the methane mole fraction, temperature, pressure, and density as effective parameters on the thermal conductivity were network input variables. 171 and 180 data points related to the nitrogen-methane and carbon dioxide-methane gas mixtures respectively, divided to test and train datasets. simple correlations were obtained due to the small number of optimal neurons in the ann structures. the mean relative errors of 0.206 % and 0.199 % for the testing dataset indicate the high accuracy and validation of the correlations. the work indicates that artificial intelligence approaches are very useful for the thermal conductivity modeling in natural gases. a sensitivity analysis was performed on all input variables that indicates that the gas mixture density has the greatest impact on the thermal conductivity.