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Optimization of electrochemically synthesized Cu3(BTC)2 by Taguchi method for CO2/N2 separation and data validation through artificial neural network modeling

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Frontiers of Chemical Science and Engineering 2020年第2期14卷 233-247页

作者： kasra pirzadeh Ali Asghar Ghoreyshi Mostafa Rahimnejad Maedeh Mohammadi Chemical Engineering Department Babol Noshirvani University of TechnologyBabolIran

Cu(BTC),a common type of metal organic framework(MOF),was synthesized through electrochemical route for CO capture and its separation from *** method was employed for optimization of key parameters affecting the synthesis of Cu(BTC).The results indicated that the optimum synthesis conditions with the highest CO selectivity can be obtained using 1 g of ligand,applied voltage of 25 V,synthesis time of 2 h,and electrode length of 3 *** single gas sorption capacity of the synthetized microstructure Cu(BTC)for CO(at 298 K and 1 bar)was a considerable value of 4.40 mmol·*** isosteric heat of adsorption of both gases was calculated by inserting temperature-dependent form of Langmuir isotherm model in the Clausius-Clapeyron *** adsorption of CO/N binary mixture with a concentration ratio of 15/85 vol-%was also studied experimentally and the result was in a good agreement with the predicted value of IAST ***,Cu(BTC)showed no considerable loss in CO adsorption after six sequential *** addition,artificial neural networks(ANNs)were also applied to predict the separation behavior of CO/N mixture by MOFs and the results revealed that ANNs could serve as an appropriate tool to predict the adsorptive selectivity of the binary gas mixture in the absence of experimental data.

关键词： Cu3(BTC)2 electrochemical synthesis CO2 adsorption Taguchi optimization ANN modeling

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