Simulation of Gas Hydrate Formation Condition in the Presence and Absence of Thermodynamics Inhibitors Using Empirical Correlations and Data-Driven Models

Document Type : Research Paper

Authors

1 Department of chemical engineerin, Tarbiat Modares University, Tehran

2 Department of chemical engineerin, Khalij Fars University, Booshehr

Abstract

Hydrates are known to occur in a variety of natural-gas handling facilities and processing equipment in oil fields, refineries, and chemical plants where natural gas and water coexist at elevated pressures and reduced temperatures. Prevention of hydrate formation requires prediction of hydrate formation temperature or pressure. In this paper, two data-driven models, i.e. artificial neural network (ANN) and neuro-fuzzy system (ANFIS model) have been applied to predict hydrate formation pressure by available experimental data in this field as alternative tools. For this purpose, optimum structure of data-driven models was determined by statistical parameters. Optimum neural network and ANFIS models were compared with well-known empirical correlation and thermodynamic model of Heriot-Watt University. The results obtained in this work indicate that ANFIS model is more accurate in prediction of hydrate formation pressure than ANN. Furthermore, comparison shows that ANFIS matches better with experimental data compared with empirical correlation and HWHYD model in terms of statistical values.

Keywords

References

منابع
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Journal of Petroleum Research
Volume 21, Issue 65 - Serial Number 65
May and June 2011
Pages 82-97

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