TY - JOUR ID - 119586 TI - Developing genetic algorithm-based neural networks and sensitivity analysis for thermal conductivity of natural gases JO - Iranian Journal of Chemical Engineering(IJChE) JA - IJCHE LA - en SN - 1735-5397 AU - Beigzadeh, R. AU - Ozairy, R. AD - Department of Chemical Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran Y1 - 2020 PY - 2020 VL - 17 IS - 2 SP - 44 EP - 55 KW - Thermal conductivity KW - natural gases KW - Artificial Neural Network KW - Genetic Algorithm KW - Sensitivity analysis DO - 10.22034/ijche.2020.249879.1349 N2 - The artificial neural network (ANN) approach was applied to develop simple correlations for predicting the thermal conductivity of nitrogen-methane 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 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 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. UR - https://www.ijche.com/article_119586.html L1 - https://www.ijche.com/article_119586_c98e47a167372cfb54e00f8e8d5b783e.pdf ER -