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.