Genetic algorithm optimized artificial neural network models of single- and multi-component gas adsorption isotherms for hydrogen purification

Abstract

Accurate isotherm models contribute to predicting the amount of adsorbate adsorbed in the adsorbent within a certain range of pressure and temperature. Experimental adsorption isotherms of five components (CO2, CO, N2, CH4 and H2) on pelletized zeolite 13X from publication data were correlated with common-used fundamental isotherm models, including Langmuir, Sips, dual-site Langmuir (DSL), and temperature-dependent (TD) isotherm models, including TD Langmuir, TD Sips and TD DSL. Overall, the average relative errors (AREs) of TD models are higher than those of common-used fundamental isotherm models, but TD models have better applicability. In addition, three surrogate artificial neural networks (ANNs) optimized by genetic algorithm (GA) of single- and multi-component gas adsorption isotherm models are proposed. Three GA-ANN models set the adsorption amount as the output and take one, two or three potential inputs, such as adsorption pressure, temperature and adsorbate components. Three GA-ANN models generally have lower AREs and better applicability than fundamental isotherm models and TD isotherm models. The proposed GA-ANN3 model, which has three inputs, makes a new attempt at adsorption isotherms and can fit the multi-component gas adsorption at different temperatures with the change of adsorption pressure. The adsorber dynamics for multi-component adsorption (CO2: CO: N2: H2 = 19.9: 0.1: 44.6: 35.4 mol%) in zeolite 13X bed are compared by the extended TD Langmuir model and extended TD DSL model. Since the AREs of the TD DSL model are smaller than that of the TD Langmuir, the breakthrough curves of multi-component gas simulated by the TD DSL model are better than that of TD Langmuir. These results can contribute to relevant researchers choosing suitable adsorption isotherm models for numerical simulations of adsorption processes.