H2-selective mixed matrix membranes modeling using ANFIS,PSO-ANFIS,GA-ANFIS |
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Authors: | Mashallah Rezakazemi Amir Dashti Morteza Asghari Saeed Shirazian |
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Affiliation: | 1. Department of Chemical Engineering, Shahrood University of Technology, Shahrood, Iran;2. Separation Processes Research Group (SPRG), Department of Engineering, University of Kashan, Kashan, Iran;3. Energy Research Institute, University of Kashan, Ghotb-e-Ravandi Ave., Kashan, Iran;4. Department of Chemical Sciences, Bernal Institute, University of Limerick, Limerick, Ireland |
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Abstract: | The novel contribution of the current study is to employ adaptive neuro-fuzzy inference system (ANFIS) for evaluation of H2-selective mixed matrix membranes (MMMs) performance in various operational conditions. Initially, MMMs were prepared by incorporating zeolite 4A nanoparticles into polydimethylsiloxane (PDMS) and applied in gas permeation measurement. The gas permeability of CH4, CO2, C3H8 and H2 was used for ANFIS modeling. In this manner, the H2/gas selectivity as the output of the model was modeled to the variations of feed pressure, nanofiller contents and the kind of gas, which were defined as input (design) variables. The proposed method is based on the improvement of ANFIS with genetic algorithm (GA) and particle swarm optimization (PSO). The PSO and GA were applied to improve the ANFIS performance. To determine the efficiency of PSO-ANFIS, GA-ANFIS and ANFIS models, a statistical analysis was performed. The results revealed that the PSO-ANFIS model yields better prediction in comparison to two other methods so that root mean square error (RMSE) and coefficient of determination (R2) were obtained as 0.0135 and 0.9938, respectively. The RMSE and R2 values for GA-ANFIS were 0.0320 and 0.9653, respectively, and for ANFIS model were 0.0256 and 0.9787, respectively. |
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Keywords: | Hydrogen separation Membrane ANFIS PSO GA Zeolite 4A nanoparticles AARD average absolute relative deviation ANFIS adaptive neuro-fuzzy inference system ANN artificial neural networks cognitive acceleration social acceleration FCM fuzzy C-means clustering FL fuzzy logic GA genetic algorithm MFs membership functions MMMs mixed matrix membranes MSRE mean squared relative error PDMS polydimethylsiloxane PSO particle swarm optimization coefficient of determination RMSE root mean square error inertia weight damping ratio initial inertia weight |
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