TY  - JOUR
IS  - 9
N2  - 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.
ID  - scholars7047
KW  - Algorithms; Electric lines; Exponential functions; Forecasting; Functions; Gallium; Genetic algorithms; Hydration; Hydrocarbons; Nitrogen; Optimization; Particle swarm optimization (PSO); Pipelines
KW  -  Deepwater hydrocarbon; Deepwater pipelines; Hydrate formation; Intelligent optimization; Mathematical prediction models; Optimization algorithms; Pressure conditions; Transportation system
KW  -  Gas hydrates
Y1  - 2016///
A1  - Abbasi, A.
A1  - Hashim, F.M.
JF  - Petroleum Science and Technology
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84975303577&doi=10.1080%2f10916466.2016.1170842&partnerID=40&md5=457734ad7fc16f2db7b64d97dfa18a01
VL  - 34
AV  - none
N1  - cited By 6
TI  - A prediction model for the natural gas hydrate formation pressure into transmission line
SP  - 824
PB  - Taylor and Francis Inc.
SN  - 10916466
EP  - 831
ER  -