Development of hybrid models for prediction of gas permeation through FS/POSS/PDMS nanocomposite membranes

Rezakazemi, Mashallah; Azarafza, Abouzar; Dashti, Amir; Shirazian, Saeed

doi:10.1016/j.ijhydene.2018.07.124

Cited by 49 publications

(8 citation statements)

References 31 publications

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“…The accuracy of the results in this work will significantly improve due to better estimation of parameters for nonlinear problems performed by the hybrid model. Previous studies suggest that the Gaussian type has been applied as membership functions (MFs) 37, 39–43, 62–67.…”

Section: Resultsmentioning

confidence: 99%

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

et al. 2021

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An analyzing tool as a sustainable method to combine CO2 capture and production of CH4 by utilizing CO2 as a feedstock is proposed. The impact of incorporating metallic promoters such as Fe, La, Ce, and Co to an Al2O3‐supported catalyst containing Ni as the first metal in the CO2 methanation was modeled. Smart models were employed to analyze the CO2 conversion and CH4 selectivity in CH4 production from CO2. The genetic programming (GP) model provides a mathematical framework for the estimation of CO2 conversion and CH4 selectivity. The model inputs are catalyst surface area, temperature, H2/CO2 ratio, gas‐hourly space velocity, and catalyst pore volume and diameter. The results confirm that the GP model estimates the CO2 conversion and CH4 selectivity very well.

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Section: Resultsmentioning

confidence: 99%

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

et al. 2021

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“…When the mentioned synergistic method is applied, the performance of hybrid intelligent systems would be enhanced. Accordingly, a method known as the neuro‐fuzzy computing method is an example of a technique which is the outcome of intelligent systems , .…”

Section: Methodsmentioning

confidence: 99%

Development of a 3D Hybrid Intelligent‐Mechanistic Model for Simulation of Multiphase Chemical Reactors

Rezakazemi

Shirazian

2018

Chem Eng & Technol

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A novel intelligent‐mechanistic model was developed to understand the behavior of multiphase chemical reactors. Computational fluid dynamics (CFD) and an intelligent algorithm were combined to predict different levels of 3D cylindrical bubble‐column reactors. An adaptive neuro‐fuzzy inference system (ANFIS) was used as the intelligence algorithm, and different ANFIS parameters were evaluated. With about one third of the training data the method can predict the overall behavior of the gas fraction in the reactor. A number of rules significantly influence the accuracy of the ANFIS method. After finding appropriate parameters, the method is applied for prediction of points which are not simulated with CFD, representing ANFIS mesh refinement. Also, bubble‐column reactors without training of exact values of measured data or numerical results can be predicted. Main advantages are time savings and reduction of computational expenses.

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“…In the fields of biomedicine, chemistry, materials, and the environment, the machine learning technique has been successfully applied to the analysis of the relationship between the characteristics and the performances of materials [15][16][17][18][19]. Specific to membrane-based gas separation, machine learning has been applied to the performance prediction and structural optimization of polymer membranes, zeolite membranes, metal-organic framework membranes, and composite membranes [20][21][22][23][24][25][26]. For CMS membrane, Behnia et al [27] predicted the gas permeability and selectivity through statistical analysis and modeling based on five influential factors, including the type of precursor, blend composition of precursors, final pyrolysis temperature, vacuum pressure during pyrolysis, and operating pressure.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Influencing Factors on the Gas Separation Performance of Carbon Molecular Sieve Membrane Using Machine Learning Technique

et al. 2022

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Gas separation performance of the carbon molecular sieve (CMS) membrane is influenced by multiple factors including the microstructural characteristics of carbon and gas properties. In this work, the support vector regression (SVR) method as a machine learning technique was applied to the correlation between the gas separation performance, the multiple membrane structure, and gas characteristic factors of the self-manufactured CMS membrane. A simple quantitative index based on the Robeson’s upper bound line, which indicated the gas permeability and selectivity simultaneously, was proposed to measure the gas separation performance of CMS membrane. Based on the calculation results, the inferred key factors affecting the gas permeability of CMS membrane were the fractional free volume (FFV) of the precursor, the average interlayer spacing of graphite-like carbon sheet, and the final carbonization temperature. Moreover, the most influential factors for the gas separation performance were supposed to be the two structural factors of precursor influencing the porosity of CMS membrane, the carbon residue and the FFV, and the ratio of the gas kinetic diameters. The results would be helpful to the structural optimization and the separation performance improvement of CMS membrane.

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Development of hybrid models for prediction of gas permeation through FS/POSS/PDMS nanocomposite membranes

Cited by 49 publications

References 31 publications

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

Development of a 3D Hybrid Intelligent‐Mechanistic Model for Simulation of Multiphase Chemical Reactors

Analysis of Influencing Factors on the Gas Separation Performance of Carbon Molecular Sieve Membrane Using Machine Learning Technique

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Development of hybrid models for prediction of gas permeation through FS/POSS/PDMS nanocomposite membranes

Cited by 49 publications

References 31 publications

Modeling of Catalytic CO2 Methanation Using Smart Computational Schemes

Modeling of Catalytic CO2 Methanation Using Smart Computational Schemes

Development of a 3D Hybrid Intelligent‐Mechanistic Model for Simulation of Multiphase Chemical Reactors

Analysis of Influencing Factors on the Gas Separation Performance of Carbon Molecular Sieve Membrane Using Machine Learning Technique

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Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes

Modeling of Catalytic CO₂ Methanation Using Smart Computational Schemes