Efficient prediction of water vapor adsorption capacity in porous metal–organic framework materials: ANN and ANFIS modeling

Abstract

Optimum design of water vapor separation process (dehumidification) using adsorption process mostly depends on the selection of appropriate porous materials or adsorbents with the highest equilibrium storage capacity of the vapor. Equilibrium capacity is generally evaluated through cost-demanding experiments via direct measurement of the vapor isotherm. Reliable prediction of the vapor adsorption capacity in porous materials provides a robust tool to a quick screening of porous materials appropriating for dehumidification process. In this article, adsorption capacity of water vapor in metal–organic framework (MOF) materials is predicted using two robust “artificial neural network (ANN)” and “Adaptive network-based fuzzy inference system (ANFIS)” methods. The three parameters of the surface area, pore volume and pore diameters are selected as input and the water vapor adsorption capacities of MOFs were computed as the output of the models. Comparison of the obtained results and real experimental data implied the superiority of the ANFIS and ANN models to predict the water vapor adsorption capacity into MOFs with a mean squared error (MSE) of 0.005 and 0.002, respectively. This clearly indicates a great potential for the application of both ANN and ANFIS methods to rapid screen MOFs suitable for water vapor adsorption.