Accurate estimation of liquid thermal conductivity is highly necessary to appropriately design equipments in different industries. Respect to this necessity

in the current investigation a feed-forward artificial neural network (ANN) model is examined to correlate the liquid thermal conductivity of normal and aromatic hydrocarbons at the temperatures range of 257-338 K and atmospheric pressure. For this purpose

956 experimental thermal conductivities for normal and aromatic hydrocarbons are collected from different previously published literature. During the modeling stage

to discriminate different substances

critical temperature (

T

c

)

critical pressure (

P

c

) and acentric factor (

ω

) are utilized as the network inputs besides the temperature. During the examination

effects of different transfer functions and number of neurons in hidden layer are investigated to find the optimum network architecture. Besides

statistical error analysis considering the results obtained from available correlations and group contribution methods and proposed neural network is performed to reliably check the feasibility and accuracy of

the proposed method. Respect to the obtained results

it can be concluded that the proposed neural network consisted of three layers namely

input

hidden and output layers with 22 neurons in hidden layer was the optimum ANN model. Generally

the proposed model enables to correlate the thermal conductivity of normal and aromatic hydrocarbons with absolute average relative deviation percent (AARD)

mean square error (MSE)

and correlation coefficient (

R

2

) of lower than 0.2%

1.05×10

-7

and 0.9994

respectively.