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ABSTRACT This work is aimed at modeling the carbon dioxide (CO2) loading capacities by exploiting an artificial neural network model in two applicable amino acid salt solutions blended with amine… Click to show full abstract
ABSTRACT This work is aimed at modeling the carbon dioxide (CO2) loading capacities by exploiting an artificial neural network model in two applicable amino acid salt solutions blended with amine solutions as an additive over a wide range of temperature and pressure. In this regard, a group of 740 experimental data points for CO2 loading capacity has been collected from recent literature works. The results show that the developed network has good capability to predict CO2 loading capacity in solutions with average relative deviation equal to 3.8608, mean square error value of 0.0045, and correlation coefficient equal to 0.9976.
Journal Title: Petroleum Science and Technology
Year Published: 2019
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