Modeling the Physical Properties of Gamma Alumina Catalyst Carrier Based on an Artificial Neural Network

Majdi, Hasan Shakir; Saud, A. N.; Saud, S. N.

doi:10.3390/ma12111752

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…An artificial neural network model has been designed by Sheikh et al to predict the properties (porosity, density, shrinkage, and surface area) of porous alumina prepared by the gel casting method [12]. The model input parameters are sintering temperature, yeast cell concentration, and soaking time, while the outputs are porosity, density, shrinkage, and surface area.…”

Section: Introductionmentioning

confidence: 99%

Critical thermal shock temperature prediction of alumina using improved hybrid models based on artificial neural networks and Shannon entropy

Fissah

Belghalem

Djeddou

et al. 2022

JMES

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This study investigates the potential of a simple and Hybrid artificial neural network (ANN) to predict dense alumina's critical thermal shock temperature (ΔTc). The predictive models have been constructed using two ANNS models (M1, M2). In the first model (M1), elaboration, physical and mechanical parameters have been exploited to build three ANNs, namely generalized linear regression (M1-GLRNN), extreme learning machine (M1-ELM), and radial basis function (M1-RBFNN). The second model (M2) has been built by the three models mentioned above incorporated by the Shannon Entropy (SE) method. To compare the performance of all the developed models, coefficient of correlation (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency coefficient (NSE) have been considered. It is found that M2-RBFNN model with (RMSE = 4.3526, MAPE= 0.3406, NSE = 0.9921, and R= 0.9960) had superiority to the M1-RBFNN model (RMSE = 4.7030, MAPE= 0.3003, NSE = 0.9908, and R = 0.9954). More importantly, the contribution of the present work is that prediction of ΔTc has been performed through the developed hybrid model (M2-RBFNN), which reduces the number of inputs from six to only four inputs and offers high accuracy for all the studied variables.

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Section: Introductionmentioning

confidence: 99%

Critical thermal shock temperature prediction of alumina using improved hybrid models based on artificial neural networks and Shannon entropy

Fissah

Belghalem

Djeddou

et al. 2022

JMES

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“…The porous ceramics have an aroused growing interest due to the diversity of areas in which it can be applied, such as catalytic supports, solid particle collectors, fuel and liquid metal filters, reactor membranes, and thermal insulation in furnace coatings [1][2][3][4][5]. The main properties make them suitable for a variety of applications, where the presence of high temperatures and chemical aggression does not allow the use of metallic or polymeric materials.…”

Section: Introductionmentioning

confidence: 99%

Thermal Conductivity Optimization of Porous Alumina Ceramics via Taguchi Model

Aswad

Saud

Ahmed

2020

MSF

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A comparative analysis of the thermal conductivity for porous alumina using Taguchi method has been reported in the current research. Porous alumina is one of the most critical ceramics amongst those that are widely used in the thermal insulator industry; this is because of their physical properties. Thus, the investigation of these properties is highly desirable. Test variables were performed for the thermal conductivity studies-weight per cent of a pore-forming agent (yeast), sintering temperature, and soaking time. Through implementing the experimental design using the Taguchi method for thermal conductivity of porous alumina was statistically analyzed. The Signal-to-noise ratio and variance analysis investigated the influence of different parameters on the porous media's thermal conductivity. The result of research determines that the addition of the pore-forming agent obtained a higher thermal insulator. Based on the optimum conditions obtained from the Taguchi method factor was 20wt.% weight of yeast cell , sintering temperature at 1200 C , and the holding time 1.5 h. that give higher value of the S/N ratio.

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Triggering the aqueous interparticle association of γ‒Al2O3 hierarchical assemblies using divalent cations and cellulose nanofibers

Hudelja

Wicklein

Kuščer

et al. 2021

Journal of the European Ceramic Society

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Modeling the Physical Properties of Gamma Alumina Catalyst Carrier Based on an Artificial Neural Network

Cited by 8 publications

References 18 publications

Critical thermal shock temperature prediction of alumina using improved hybrid models based on artificial neural networks and Shannon entropy

Critical thermal shock temperature prediction of alumina using improved hybrid models based on artificial neural networks and Shannon entropy

Thermal Conductivity Optimization of Porous Alumina Ceramics via Taguchi Model

Triggering the aqueous interparticle association of γ‒Al2O3 hierarchical assemblies using divalent cations and cellulose nanofibers

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