Prediction and Experimental Verification of CO2 Adsorption on Ni/DOBDC Using a Genetic Algorithm鈥揃ack-Propagation Neural Network Model

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• 作者：Zhi Guo Qu ; Hui Wang ; Wen Zhang ; Liang Zhou ; Ying Xin Chang
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2014
• 出版时间：July 30, 2014
• 年：2014
• 卷：53
• 期：30
• 页码：12044-12053
• 全文大小：582K
• ISSN：1520-5045

文摘

A model combined a back-propagation neural network (BPNN) with a genetic algorithm (GA) based on experimental data as training samples was established to predict the CO₂ adsorption capacity for metal organic frameworks (MOFs) of Ni/DOBDC. The random function of the conventional BPNN model was modified by the GA鈥揃PNN model for optimizing the initial weights and bias nodes. The amounts of adsorbed CO₂ and corresponding isosteric heat of adsorption on Ni/DOBDC were synchronously studied within a wide temperature range (25鈥?45 掳C) and pressure range (0鈥?.5 MPa). The predicted results of the proposed GA鈥揃PNN model and those of theoretical models and a BPNN model were compared with the experimental data. The proposed model provided a more accurate prediction than those of the theoretical models and BPNN model. In particular, the theoretical models were invalid in the low-pressure range (0鈥?.1 MPa).