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AdaBoost Metalearning Methodology for Modeling the Incipient Dissociation Conditions of Clathrate Hydrates

This paper proposes the AdaBoost metalearning methodology to combine the outcomes of tree-based models of classification and the regression tree (CART) algorithm for estimating the equilibrium dissociation temperature of clathrate… Click to show full abstract

This paper proposes the AdaBoost metalearning methodology to combine the outcomes of tree-based models of classification and the regression tree (CART) algorithm for estimating the equilibrium dissociation temperature of clathrate hydrates. In addition to the AdaBoost-CART models, models based on the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) approaches were also developed. Training and testing of the models were done utilizing a gathered database of more than 3500 experimental data on incipient dissociation conditions of CO2 and other hydrate systems. With the average absolute relative deviation percent (AARD%) between 0.03 and 0.07, 0.04 and 1.09, and 0.09 and 1.01, which were obtained by the presented AdaBoost-CART, ANFIS, and ANN models, respectively, the targets were reproduced with satisfactory accuracy. However, for all of the studied clathrate hydrate systems, the proposed AdaBoost-CART models provide more reliable results. Indeed, the obtained AARD% values for tree-based models are lower than those of other models.

Keywords: methodology; dissociation; incipient dissociation; clathrate hydrates; adaboost metalearning; metalearning methodology

Journal Title: ACS Omega
Year Published: 2021

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