Bio-metal-organic framework-based cobalt glutamate for CO2/N2 separation: Experimental and multi-objective optimization with a neural network

Abstract

The outstanding properties of metal-organic frameworks (MOFs) have proven that this type of crystalline adsorbent has great potential in CO2 capture applications. Most of the MOF research studies on new functional MOFs are conducted to improve the performance of CO2 gas adsorption. Combined studies of material evaluation and process design on engineering issues in CO2 capture applications in industry are rarely carried out. In this study, the authors attempted to address engineering issues by developing a biometal-organic framework with the bioligand L-glutamic acid that has more practical fabrication cost than petrochemical MOFs. Herein, the demonstration of the prediction and optimization of CO2 adsorption capacity, selectivity, and heat of adsorption using a multiobjective genetic algorithm (MOGA) combined with an artificial neural network (ANN). The success of the Bio-MOF fabrication was evaluated by scanning electron microscopy, N2 adsorption-desorption isotherm analysis, thermal gravimetric analysis, X-ray diffraction, and Fourier transform infrared spectroscopy techniques. Furthermore, volumetric measurements were performed at several temperatures (27, 35 and, 50 °C). The isosteric heat of adsorption was then evaluated by an indirect method with the Clausius-Clapeyron (C-C) and Chakraborty, Saha, and Koyama (CSK) equations. Then, CO2/N2 selectivity was analysed by IAST techniques by regressing the experimental data with the Langmuir-Freundlich isothermal equation. The computational study by ANN and MOGA also gives satisfying results in balancing three requirements criteria. Thus, this study paved the way for the development of low-cost scalable MOF fabrication in industry by applying the optimization and balancing principles of the three objective functions.