Development of novel correlation for prediction of hydrate formation temperature based on intelligent optimization algorithms

Since hydrate formation could cause problems such as high pressure drop and clogging in pipelines, estimation of hydrate formation conditions is of vital importance in prevention of such problems. To do this, firstly hydrate formation conditions for methane and pure water system should be studied and then corrections be imposed on the methane–pure water system correlation to predict the formation conditions for natural gas and in the presence of impurities and salts. Many correlations for methane–pure water system have been suggested but they all seems to lack accuracy in hydrate formation conditions prediction. In this paper, on the basis of experimental data from Sloan work, a new correlation was developed using MATLAB curve fitting software and then optimized using optimization methods such as Genetic Algorithm, Particle Swarm Algorithm, and Imperialist Competitive Algorithm to enhance the accuracy of the correlation. The results of the new correlation have been compared to previous works and it shows that the new correlation has the lowest amount of error and the highest accuracy.