Development of a Prediction Model for Gas Hydrate Formation in Multiphase Pipelines by Artificial Intelligence
Jai Krishna Sahith Sayani
University College Dublin, Belfield, School of Chemical and Bioprocess Engineering, D04V1W8 Dublin, Ireland
Universiti Teknologi PETRONAS, Mechanical Engineering Department, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Universiti Teknologi PETRONAS, CO2 Research Centre (CO2RES), 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Search for more papers by this authorVinayagam Sivabalan
Universiti Teknologi PETRONAS, CO2 Research Centre (CO2RES), 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Universiti Teknologi PETRONAS, Chemical Engineering Department, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Search for more papers by this authorKhor Siak Foo
Universiti Teknologi PETRONAS, CO2 Research Centre (CO2RES), 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Universiti Teknologi PETRONAS, Chemical Engineering Department, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
PTTEP, Level 26-30, Tower 2, Petronas Twin Towers, Kuala Lumpur City Centre, 50088 Kuala Lumpur, Malaysia
Search for more papers by this authorSrinivasa Rao Pedapati
Universiti Teknologi PETRONAS, Mechanical Engineering Department, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Search for more papers by this authorCorresponding Author
Bhajan Lal
Universiti Teknologi PETRONAS, CO2 Research Centre (CO2RES), 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Universiti Teknologi PETRONAS, Chemical Engineering Department, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Correspondence: Bhajan Lal ([email protected]), CO2 Research Centre (CO2RES), Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia.Search for more papers by this authorJai Krishna Sahith Sayani
University College Dublin, Belfield, School of Chemical and Bioprocess Engineering, D04V1W8 Dublin, Ireland
Universiti Teknologi PETRONAS, Mechanical Engineering Department, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Universiti Teknologi PETRONAS, CO2 Research Centre (CO2RES), 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Search for more papers by this authorVinayagam Sivabalan
Universiti Teknologi PETRONAS, CO2 Research Centre (CO2RES), 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Universiti Teknologi PETRONAS, Chemical Engineering Department, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Search for more papers by this authorKhor Siak Foo
Universiti Teknologi PETRONAS, CO2 Research Centre (CO2RES), 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Universiti Teknologi PETRONAS, Chemical Engineering Department, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
PTTEP, Level 26-30, Tower 2, Petronas Twin Towers, Kuala Lumpur City Centre, 50088 Kuala Lumpur, Malaysia
Search for more papers by this authorSrinivasa Rao Pedapati
Universiti Teknologi PETRONAS, Mechanical Engineering Department, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Search for more papers by this authorCorresponding Author
Bhajan Lal
Universiti Teknologi PETRONAS, CO2 Research Centre (CO2RES), 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Universiti Teknologi PETRONAS, Chemical Engineering Department, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia
Correspondence: Bhajan Lal ([email protected]), CO2 Research Centre (CO2RES), Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak Darul Ridzuan, Malaysia.Search for more papers by this authorAbstract
A prediction model is developed by means of artificial neural networks (ANNs) to determine the gas hydrate formation kinetics in multiphase gas dominant pipelines with crude oil. Experiments are conducted to determine the rate of formation and reaction kinetics of hydrates formation in multiphase systems. Based on the results, an artificial intelligence model is proposed to predict the gas hydrate formation rate in multiphase transmission pipelines. Two ANN models are suggested with single-layer perceptron (SLP) and multilayer perceptron (MLP). The MLP shows more accurate prediction when compared to SLP. The models were predicted accurately with high prediction accuracy both for the pure and multiphase systems.
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