%N 9
%A A. Abbasi
%A F.M. Hashim
%D 2016
%K Algorithms; Electric lines; Exponential functions; Forecasting; Functions; Gallium; Genetic algorithms; Hydration; Hydrocarbons; Nitrogen; Optimization; Particle swarm optimization (PSO); Pipelines, Deepwater hydrocarbon; Deepwater pipelines; Hydrate formation; Intelligent optimization; Mathematical prediction models; Optimization algorithms; Pressure conditions; Transportation system, Gas hydrates
%L scholars7047
%O cited By 6
%X Gas hydrate is a major challenge in deepwater hydrocarbon transmission lines. It can lead to safety hazards in production and flow assurance in the transportation system of hydrocarbons. The authors propose a mathematical prediction model for hydrate formation pressure conditions based on exponential function and intelligent optimization. The intelligent optimization approach namely genetic algorithm (GA), particle swarm optimization (PSO) and grey wolf optimizer (GWO) were used to search the best value of coefficients that give a minimum error in prediction of pressure conditions during hydrate formation in the deepwater pipeline. The proposed approach was tested on the four experimental data of with and without inhibitor and nitrogen in the mixture of gases. The proposed approach of hydrate formation pressure conditions prediction model will be helpful in finding hydrate formation pressure in deepwater methane gas pipeline. © 2016, Taylor & Francis Group, LLC.
%J Petroleum Science and Technology
%T A prediction model for the natural gas hydrate formation pressure into transmission line
%R 10.1080/10916466.2016.1170842
%V 34
%I Taylor and Francis Inc.
%P 824-831