Comparative Study between Regression and Soft Computing Models to Maximize the Methane Storage Capacity of Anthracite-Based Adsorbents

Abstract

Adsorbed natural gas (ANG) technology is a safe and low-cost approach for natural gas storage. Improving the volumetric adsorption capacity of adsorbents in the ANG tank can enhance the fuel density and make this technology cost-effective compared to other available CH4 storage approaches. For this purpose, the present research focuses on maximizing CH4 uptake on low-cost and available anthracite-based carbon materials via experimental and analytical investigations. The effect of preparation variables of the chemical agent (KOH) impregnation ratio to the anthracite (2.6-4.3 g/g), activation temperature (666-834 °C), and retention time (39-140 min) on the specifications of the coal-based activated carbons (ACs) and their CH4 adsorption capacity were examined experimentally. The results were analyzed through empirical models, including response surface methodology (RSM), our in-house developed models, namely, regularization networks (RN) and adaptive neuro-fuzzy interface systems. The statistical assessment revealed that all three established models could effectively predict the methane adsorption capacity of the carbon samples based on their preparation conditions. The superior performance of our in-house RN is dedicated to its robust theoretical backbone in the regularization theory. Finally, the carbon sample prepared under the optimized preparation conditions, based on the RSM and genetic algorithm, showed the highest CH4 uptake of 175 cm3(STP)/cm3. Based on the authors' knowledge, the volumetric CH4 capacity of the optimized AC is one of the highest values reported in the literature among different classes of the adsorbent.