期刊
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
卷 53, 期 30, 页码 12044-12053出版社
AMER CHEMICAL SOC
DOI: 10.1021/ie404396p
关键词
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资金
- National Natural Science Foundation of China [51176149]
- National Key Projects of Fundamental R/D of China (973 Project) [2011CB610306]
- Fundamental Research Funds for the Central Universities [08143053]
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-BPNN 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 (25-145 degrees C) and pressure range (0-3.5 MPa). The predicted results of the proposed GA-BPNN 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 (0-0.1 MPa).
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