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

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.