Prediction of gas hydrate formation using empirical equations and data-driven models
| Autor: | Masoud Mehrizadeh |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
Adaptive neuro fuzzy inference system Petroleum engineering Artificial neural network Neuro-fuzzy business.industry Fossil fuel Clathrate hydrate Statistical parameter 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Petrochemical Natural gas 0103 physical sciences Environmental science 0210 nano-technology business |
| Zdroj: | Materials Today: Proceedings. 42:1592-1598 |
| ISSN: | 2214-7853 |
| Popis: | Gas hydrates are formed in various natural gas transmission facilities and gas processing equipment in oil and gas fields, refineries, petrochemical plants and chemical industries when water and natural gas combine at sufficiently low temperatures and high pressures condition. Estimation of the temperature and pressure at which hydrates are formed is the first measure that is usually taken to prevent hydrate formation. In this paper, two data-driven models namely artificial neural network (ANN) and Adaptive Neuro Fuzzy Interference System (ANFIS model), were developed as an alternative instruments that draw on empirical data to estimate hydrate formation pressure for various gas systems. To this end, statistical parameters were used to determine the optimum structure of each data-driven model applied on these systems. The results obtained from neural network ANN and ANFIS models were compared to the experimental equations According to the performance criteria, the ANFIS model outperforms the ANN model in all cases. The results also showed that the ANFIS model was in more agreement with experimental data than empirical equations. |
| Databáze: | OpenAIRE |
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