Keshvari Sepehr, Farizhendi Saeid Abedi, Ghiasi Mohammad M, Mohammadi Amir H
Department of Chemical Engineering, Islamic Azad University, Bushehr Branch, Bushehr 19585/936, Iran.
Faculty of Chemical Engineering, Tarbiat Modares University, Tehran 14115-111, Iran.
ACS Omega. 2021 Oct 8;6(41):26919-26931. doi: 10.1021/acsomega.1c03214. eCollection 2021 Oct 19.
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 CO 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.
本文提出了AdaBoost元学习方法，以结合基于树的分类模型和回归树（CART）算法的结果，来估计笼形水合物的平衡解离温度。除了AdaBoost-CART模型外，还开发了基于自适应神经模糊推理系统（ANFIS）和人工神经网络（ANN）方法的模型。利用收集到的关于CO和其他水合物系统初始解离条件的3500多个实验数据的数据库对模型进行训练和测试。所提出的AdaBoost-CART、ANFIS和ANN模型分别获得的平均绝对相对偏差百分比（AARD%）在0.03至0.07、0.04至1.09和0.09至1.01之间，目标值的再现精度令人满意。然而，对于所有研究的笼形水合物系统，所提出的AdaBoost-CART模型提供了更可靠的结果。实际上，基于树的模型获得的AARD%值低于其他模型。
