New hybrid thermodynamic model to predict hydrate stability temperature of systems containing diverse water-insoluble promoters and diverse gases, a novel fuzzy approach
Gas hydrate technology -with its attractive advantages- needs reliable tools for producing accurate data. Predictive models are important for providing these data sets. However, thermodynamic modeling of systems containing diverse gases and diverse additives is so complicated and rare. Existing mode...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025016433 |
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| Summary: | Gas hydrate technology -with its attractive advantages- needs reliable tools for producing accurate data. Predictive models are important for providing these data sets. However, thermodynamic modeling of systems containing diverse gases and diverse additives is so complicated and rare. Existing models are exclusive for specific materials and sensitive to tuning parameters and auxiliary equations. In this study a new thermodynamic model is introduced via Fuzzy Inference System FIS for phase equilibrium modeling of hydrate stability conditions of diverse water-insoluble agents in presence of diverse gases. In fact a novel hybrid model is introduced by integrating artificial intelligence –here fuzzy sets theory- and classic thermodynamics based on first principles of equation of state, phase equilibrium theory, K-Value, Langmuir theory along with researchers’ inferences and statistical tools. The performance of proposed models are assessed through systems containing 1- CO2+Cyclopentane, 2- Methane+Pyrrolidine, 3- N2+Tetrahydrofuran, 4- Methane+Cyclohexane, 5- Methane+Cyclooctane, 6-Methane+2-Pentanone, 7- Propane+Cyclohexane, 8-Propane+Methylcyclohexane, 9- Ethane+Methylcyclohexane and 10- Methane+Ethane+Propane+Cyclohexane. Equilibrium curves exhibit good agreement with real data. The average absolute deviation AAD % and maximum absolute deviation MAD % of present model are equal to 0.902 % and 3.021 % respectively. It is noteworthy proposed model has high capability to efficiently predict many other water-insoluble agents with many other gases. Comparative statistics of Leverage, Cook’s distance, Lagged Residuals, Normal Probability, DFfits and DFBetas are all successfully passed by results guaranteeing the safe and reliable applicability worth of novel hybrid model. |
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| ISSN: | 2590-1230 |