A new empirical model and neural network-based approach for evaluation of isobaric heat capacity of natural gas

Abstract

Isobaric heat capacity of natural gas is of prominent thermophysical property as it is directly related to thermodynamic energy functions, thereby its trustworthy prediction can open a new window for better establishment of the basis for its theoretical and engineering studies. In the present study, a new accurate and simple empirical correlation as a function of specific gravity (γg), temperature (T), and pressure (P) was developed to rapidly estimate the isobaric specific heat capacity (Cp) of natural gas without using gas composition. Due to the limitations of proposed experimental models and relations derived from gas compressibility factor (Z-factor) equations in the literature to a certain temperature, pressure and specific gravity range, an artificial neural network (ANN) based on back-propagation method was also applied for reliable prediction of Cp of natural gas. 847 sets of data from a diverse range of T, P, and γg were used to develop the neural network architecture and topology. Moreover, Genetic algorithm (GA) and Particle swarm (PS) optimization as population-based stochastic search algorithms were also utilized to optimize the weights and biases of networks to establish the best combination of input variables leading to the minimum Cp. The ANN demonstrated an accurate and promising prediction with a correlation coefficient of 0.99691 and 0.99518 for total dataset and test data, respectively.