Comparison of intelligent systems, artificial neural networks and neural fuzzy model for prediction of gas hydrate formation rate

Mohammad Javad Jalalnezhad, Mohammad Ranjbar, Amir Sarafi, Hossein Nezamabadi-Pour


DOI: https://doi.org/10.12777/ijse.7.1.35-40

Abstract


The main objective of this study was to present a novel approach for predication of gas hydrate formation rate based on the Intelligent Systems. Using a data set including about 470 data obtained from flow tests in a mini-loop apparatus, different predictive models were developed. From the results predicted by these models, it can be pointed out that the developed models can be used as powerful tools for prediction of gas hydrate formation rate with total errors of less than 4%.


Keywords


Artificial neural network; fuzzy Inference System; gas hydrate formation; rate model.

Full Text:

FULL TEXT PDF

References


Sloan, E. D. ,(1997). Clathrate hydrates of natural gases. New York: Marcel Dekker.

Hammerschmidt, E. G. ,(1934). Formation of gas hydrates in natural gas transmission lines. Industrial & Engineering Chemistry ,26:851–855.

Vysniauskas, J. W., and Bishnoi, P. R. ,(1983).A kinetic study of methane hydrate formation. chemical Engineering Science, 38:1061–1072.

Englezos, P., Kalogerakis, N., Dholabhai, P. D., and Bishnoi, P. R. ,(1987). Kinetics offormation of methane and ethane gas hydrates. Chemical Engineering Science,42:2647–2658.

Skovborg, P., and Rasmussen, P.,(1994). A mass transport limited model for the growth of methane and ethane gas hydrates. Chemical Engineering Science ,49:1131–1143.

Kashchiev, D., and Firoozabadi, A., 2003. Induction time in crystallization of gas hydrates. Journal of Crystal Growth 250:499–515.

Talaghat, M. R., Esmaeilzadeh, F., and Fathikalajahi, J. ,(2009). Experimental and theoretical investigation of simple gas hydrate formation with or without presence of kinetic inhibitors in a flow mini-loop apparatus. Fluid Phase Equilibria ,279:28–40.

Graupe, D. ,(2007). Principles of artificial neural networks. Singapore: World Scientific Publishing Co.

Blusari, A. B. ,1995. Neural networks for chemical engineers. Amsterdam: Elsevier Science Press.

Shadravanan, Rahim.,Schaffie, Mahin., and Ranjbar, Mohammad.,(2010). Prediction of Hydrate Formation Rate in the Presence of inhibitors. Journal of Energy Sources, Part A.

Mamdani, E., and Assilian, S. ,(1975). An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7:1–13.

Sugeno, M., and Kang, G. ,(1988). Structure identification of fuzzy model. Fuzzy Sets Syst. 28:15–33.

Jang, J. S. R. ,(1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cyber. 23:665–685.

Fuzzy logic toolbox for use with MATLAB user guide,(2007).

Zadeh, L. A,(1984). , Making computers think like people .IEEE Spectrum ,8:26–32.




Published by Department of Chemical Engineering University of Diponegoro Semarang
Google Scholar
IJSE  by http://ejournal.undip.ac.id/index.php/ijse is licensed under Creative Commons Attribution 3.0 License.