Prediction of effective thermal conductivity of moist porous materials using artificial neural network approach

Singh, Ramvir; Bhoopal, Rajpal S.; Kumar, Sajjan

doi:10.1016/j.buildenv.2011.06.019

Cited by 63 publications

(28 citation statements)

References 22 publications

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“…Back propagation (BP) learning algorithm is usually used for learning procedure. The mathematical background of BP algorithm can be found in [25, 26]. …”

Section: Methodsmentioning

confidence: 99%

Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique

Turan

Gümüşel

Özgönenel

2013

The Scientific World Journal

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An intensive study has been made to see the performance of the different liner materials with bentonite on the removal efficiency of Cu(II) and Zn(II) from industrial leachate. An artificial neural network (ANN) was used to display the significant levels of the analyzed liner materials on the removal efficiency. The statistical analysis proves that the effect of natural zeolite was significant by a cubic spline model with a 99.93% removal efficiency. Optimization of liner materials was achieved by minimizing bentonite mixtures, which were costly, and maximizing Cu(II) and Zn(II) removal efficiency. The removal efficiencies were calculated as 45.07% and 48.19% for Cu(II) and Zn(II), respectively, when only bentonite was used as liner material. However, 60% of natural zeolite with 40% of bentonite combination was found to be the best for Cu(II) removal (95%), and 80% of vermiculite and pumice with 20% of bentonite combination was found to be the best for Zn(II) removal (61.24% and 65.09%). Similarly, 60% of natural zeolite with 40% of bentonite combination was found to be the best for Zn(II) removal (89.19%), and 80% of vermiculite and pumice with 20% of bentonite combination was found to be the best for Zn(II) removal (82.76% and 74.89%).

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“…Back propagation (BP) learning algorithm is usually used for learning procedure. The mathematical background of BP algorithm can be found in [25, 26]. …”

Section: Methodsmentioning

confidence: 99%

Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique

Turan

Gümüşel

Özgönenel

2013

The Scientific World Journal

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“…The fundamental theory and formulas of BP ANN and transfer functions can be found elsewhere. 33,34 The experimental data of the osmotic coefficients were the output variable. A three-layer BP ANN model usually used to deal with data was set up.…”

Section: Prediction Modelsmentioning

confidence: 99%

Prediction of osmotic coefficients for ionic liquids in various solvents with artificial neural network

Cao

Yao

Wang

et al. 2017

JSCS

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The relationship between the structural descriptions and osmotic coefficients of binary mixtures containing sixteen different ionic liquids and seven kinds of solvents has been investigated by back propagation artificial neural network (BP ANN). The influence of temperature on the osmotic coefficients was considered and the concentrations of ionic liquids were close to 1 mol kg-1 , except in acetonitrile. Multi linear regression (MLR) was used to choose the variables for the artificial neural network (ANN) model. A three layer BP ANN with seven variables containing structural descriptions of the ionic liquids and the character of the solvent as input variables was developed. Compared with experimental data, the osmotic coefficients calculated using the ANN model had a high squared correlation coefficient (R 2) and a low root mean squared error (RMSE).

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“…We can more and more frequently witness the application of neural networks, fractals theory or perturbation methods in different branches of engineering. Artificial neural networks have been applied among others to solve the issue of inverse heat conduction, with the assumption of functional dependence of heat conduction on temperature, and to predict the effective heat conduction in porous materials [29,30]. Perturbation methods are applied almost in all branches of science, including technology.…”

Section: Introductionmentioning

confidence: 99%

Estimation of thermal transmittance based on temperature measurements with the application of perturbation numbers

et al. 2017

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Fast estimation of thermal transmittance based on temperature measurements is uncertain, and the obtained results can be burdened with a large error. Nevertheless, such attempts should be undertaken merely due to the fact that a precise measurement by means of heat flux measurements is not always possible in field conditions (resentment of the residents during the measurements carried out inside their living quarters), and the calculation methods do not allow for the nonlinearity of thermal insulation, heat bridges or other fragments of building envelope of diversified thermal conductivity. The present paper offers the estimation of thermal transmittance and internal surface resistance with the use of temperature measurements (in particular with the use of thermovision). The proposed method has been verified through tests carried out on a laboratory test stand built in the open space, subjected to the influence of real meteorological conditions. The present elaboration involves the estimation of thermal transmittance by means of temperature measurements. Basing on the mentioned estimation, the authors present correction coefficients which have impact on the estimation accuracy. Furthermore, in the final part of the paper, various types of disturbance were allowed for using perturbation numbers, and the introduced by the authors Bcredibility area of thermal transmittance estimation^was determined.

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Prediction of effective thermal conductivity of moist porous materials using artificial neural network approach

Cited by 63 publications

References 22 publications

Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique

Prediction of Heavy Metal Removal by Different Liner Materials from Landfill Leachate: Modeling of Experimental Results Using Artificial Intelligence Technique

Prediction of osmotic coefficients for ionic liquids in various solvents with artificial neural network

Estimation of thermal transmittance based on temperature measurements with the application of perturbation numbers

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