Journal
JOURNAL OF THE IRANIAN CHEMICAL SOCIETY
Volume 16, Issue 1, Pages 11-20Publisher
SPRINGER
DOI: 10.1007/s13738-018-1476-y
Keywords
Artificial neural network; ANFIS; Metal-organic framework; Adsorption
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available