Artificial neural network approach for prediction of thermal behavior of nanofluids flowing through circular tubes

Vaferi, Behzad; Samimi, Fereshteh; Pakgohar, E.; Mowla, Dariush

doi:10.1016/j.powtec.2014.06.062

Cited by 109 publications

(56 citation statements)

References 53 publications

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“…The weights and biases can be adjusted by training the network using the standard back propagation algorithm. Our previous findings [29,30,[33][34][35]40] justified that designing the MLP model from normalized data is easier than working on the original data. Therefore, in the present study all the variables have been mapped between [0 1] intervals.…”

Section: Design Of An Ann Modelmentioning

confidence: 94%

“…Feed-forward and recurrent networks are two different types of the ANN models [39,40]. The feedforward topologies including multi-layer perceptron (MLP), radial basis function (RBF), generalized regression (GR), and cascade-forward backpropagation (CFB) neural networks are among the most widely used types of ANN models for handling the regression problem [30,33,41,42].…”

Section: Different Types Of Artificial Neural Network 221 Feed-fomentioning

confidence: 99%

“…Independent variables enter the hidden layer. This layer performs some mathematical manipulations on the received information and then transfers it to the output layer [40]. The neuron(s) of the output layer present values of the dependent variables.…”

Section: Different Types Of Artificial Neural Network 221 Feed-fomentioning

confidence: 99%

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Prediction of degree of crystallinity for the LTA zeolite using artificial neural networks

Ghanbari

Vaferi

2017

Materials Science-Poland

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Zeolites are microporous aluminosilicate/silicate crystalline materials with three-dimensional tetrahedral configuration. In this study, the degree of crystallinity of the synthesized Linde Type A (LTA) zeolite, which is the main indicator of its quality/purity is tried to be modeled. Effect of crystallization time, temperature, molar ratio of the synthesis gel on the relative crystallinity of the LTA zeolites is investigated using artificial neural networks. Our experimental observations and some data collected from literature have been used for adjusting the parameters of the proposed model and evaluating its performance. It has been observed that two-layer perceptron network with eight hidden neurons is the most accurate approach for the considered task. The designed model predicts the experimental datasets with a mean square error of 3.99 × 10 −6 , absolute average relative deviation of 8.69 %, and regression coefficient of 0.9596. The proposed model can decrease the required time and number of experiments to evaluate the extent of crystallinity of the LTA zeolites.

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Section: Design Of An Ann Modelmentioning

confidence: 94%

Section: Different Types Of Artificial Neural Network 221 Feed-fomentioning

confidence: 99%

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Prediction of degree of crystallinity for the LTA zeolite using artificial neural networks

Ghanbari

Vaferi

2017

Materials Science-Poland

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“…These models are designed in the middle of the twentieth century by mathematical simulation of the working procedures of the human nervous system [22]. The ANN approach with a strictly feedforward structure namely multi-layer perceptron (MLP) is the most well-known and widely used ANN type for function approximation in various fields of science and engineering up to now [31][32][33][34].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…The author's previous experiences [32,34] have justified that a Levenberg-Marquardt usually provides better results [36]. Therefore, the Levenberg-Marquardt training algorithm has been used in this study.…”

Section: Trainingmentioning

confidence: 99%

Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks

2015

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Artificial Neural Network Modeling of Hydrodynamics of Liquid‐Solid Circulating Fluidized Beds

Palkar

Shilapuram

2016

Chem Eng & Technol

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Solids holdup and solids circulation rate are the two important hydrodynamic variables affected by process conditions. These two variables have a significant influence on the performance of a liquid-solid circulating fluidized bed (LSCFB). An artificial neural network (ANN) methodology was developed and simulated to predict the performance of the LSCFB for the experimental dataset collected under various process conditions. Different statistical parameters were applied to evaluate the prominent and unique characteristic features of the ANN-predicted parameters. The ANN model successfully predicted the experimental observations and captured the actual nonlinear behavior noticed during the experiments. Model validation confirmed that this data-driven technique can be used to model such nonlinear systems.

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Artificial neural network approach for prediction of thermal behavior of nanofluids flowing through circular tubes

Cited by 109 publications

References 53 publications

Prediction of degree of crystallinity for the LTA zeolite using artificial neural networks

Prediction of degree of crystallinity for the LTA zeolite using artificial neural networks

Prediction of thermal conductivity of alumina water-based nanofluids by artificial neural networks

Artificial Neural Network Modeling of Hydrodynamics of Liquid‐Solid Circulating Fluidized Beds

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