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


Cu3(BTC)2, a common type of metal organic framework (MOF), was synthesized through electrochemical route for CO2 capture and its separation from N2. Taguchi method was employed for optimization of key parameters affecting the synthesis of Cu3(BTC)2. The results indicated that the optimum synthesis conditions with the highest CO2 selectivity can be obtained using 1 g of ligand, applied voltage of 25 V, synthesis time of 2 h, and electrode length of 3 cm. The single gas sorption capacity of the synthetized microstructure Cu3(BTC)2 for CO2 (at 298 K and 1 bar) was a considerable value of 4.40 mmol · g−1. The isosteric heat of adsorption of both gases was calculated by inserting temperature-dependent form of Langmuir isotherm model in the Clausius-Clapeyron equation. The adsorption of CO2/N2 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 method. Moreover, Cu3(BTC)2 showed no considerable loss in CO2 adsorption after six sequential cycles. In addition, artificial neural networks (ANNs) were also applied to predict the separation behavior of CO2/N2 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.

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Correspondence to Ali Asghar Ghoreyshi.

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Pirzadeh, K., Ghoreyshi, A.A., Rahimnejad, M. et al. Optimization of electrochemically synthesized Cu3(BTC)2 by Taguchi method for CO2/N2 separation and data validation through artificial neural network modeling. Front. Chem. Sci. Eng. (2020) doi:10.1007/s11705-019-1893-1

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  • Cu3(BTC)2 electrochemical synthesis
  • CO2 adsorption
  • Taguchi optimization
  • ANN modeling