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Intelligent prediction of methane hydrate phase boundary conditions in ionic liquids using deep learning algorithms
The objective of this work is to predict the methane hydrate phase boundary equilibrium temperature in the presence of ionic liquids (ILs) using machine learning techniques to overcome the limitations of the existing empirically proposed models. To achieve the objectives of this work, five deep neural networks (DNN) algorithms; Adadelta, Ftrl, Adagrad, Adam, and RMSProp coupled with six activation functions (elu, leaky relu, sigmoid, relu, tanh, and selu) were used on 610 experimental datasets from literature. The independent variables used to predict the ILs methane hydrate boundary temperature were pressure (2.39â��100.43 MPa), concentration (0.10â��50 wt.), and ILs molecular weight (91.11â��339.50 gmolâ��1). The study revealed that Adadelta DNN optimization algorithm and elu activation functions gave the best predictions with an average RMSE of 0.6727 and 0.6989, respectively. The findings suggest that the use of Adadelta coupled with elu accurately predicts the methane hydrate phase boundary condition in the presence of ionic liquids. The excellent performance of Adadelta and elu resides in their ability to predict exponential data trends which is the fundamental behavior of hydrate phase behavior condition. This work pioneered the use of machine learning techniques to predict hydrate behavior conditions in IL systems. Thus, the findings in this work will enhance the development of simple hydrate phase behavior properties predictive software for IL systems. © 2023 Taylor & Francis Group, LLC.
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