developing a model for calculating pure gas thermal conductivity at p=1bar using particle swarm optimization algorithm

One of the most crucial variables in the study of heat transport is thermal conductivity and methods for measuring this variable have long been sought after. in this paper, to achieve the equation for approximation of the thermal conductivity coefficient, 61 experimental data were collected for pure gases in p=1 bar and variable temperature (91.88-1500 k). the proposed model was then obtained using the particle swarm optimization (pso) algorithm in matlab v2015. it includes a variety of hydrocarbon and non-hydrocarbon compounds. the physical properties of pure gases including temperature, critical temperature, critical pressure, molecular weight, viscosity, and heat capacity at constant volume were obtained for pure components and used for prediction of the conductivity of these gases. also, during the validation phase, the suggested model attained the most accurate prediction withr^2=0.9995. this model is capable of predicting the thermal conductivity coefficient of gases with a mean relative error percentage of 4.67% and mean square error percentage of 2.4210×10-4% compared to actual data. these results are significantly better than those obtained from other models.