Experimental study for predicting the specific heat of water based
            <scp>
              Cu‐Al
              <sub>2</sub>
              O
              <sub>3</sub>
            </scp>
            hybrid nanofluid using artificial neural network and proposing new correlation

The weak thermal conductance of a phase change material (PCM) can be intensified by dispersing nanostructured materials called nano-PCM. Accurate thermal conductivity (TC) prediction of nano-PCM is essential to evaluate heat transport during phase change processes, namely, melting and solidification. The present study develops an artificial neural network (ANN) to forecast the TC of n-octadecane as a PCM with dispersed oxide nanoparticles. A total of 122 experimental datasets from existing literature with a wide range of temperatures (5-60 C), nanoparticles (CuO, Al 2 O 3 , TiO 2 , and mesoporous SiO 2 ), nanoparticle mass fractions (0.5-12 wt%) are compiled to train a multi-layered feed-forward ANN with Levenberg-Marquardt back-propagation algorithm.An optimal architecture of the neural network is acquired by varying the number of network hidden layers, the number of neurons in each layer, and the transfer function of layers. The minimum mean square error (MSE) of 1.3512 Â 10 À5 is obtained for the best developed ANN. Results show that average absolute deviation (AAD) of 0.002458, mean absolute percentage error (MAPE) of 0.8260%, and correlation coefficient (R) of 0.999964948 are achieved for training data. Moreover, MAPE, AAD, and R values are, respectively, 0.9478, 0.002167, and 0.9999715861 for testing data. The maximum percentage errors of ANN computed values are 2.31%, and 0.812% for liquid and solid phases, respectively. This indicates that the ANN model accurately predicts the enhanced TC of nano-PCM across various oxide nanoparticles, temperatures, and nanoparticle loadings.

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“…The procedure of finding optimal ANN is illustrated in Figure 2. Many researchers have used a similar method to find the best network 34‐55 …”

Section: Resultsmentioning

confidence: 99%

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Motahar

Sadri

2021

Intl J of Energy Research

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“…In this study, an ANN model has been developed in order to predict electrical energy production values of a diesel engine cogeneration power plant. However, many applications of ANNs in different areas are also included in the literature [55]. Although there are various studies in the literature on cogeneration power plants, there is no study on estimating the electrical energy production values of diesel engine cogeneration power plants using ANNs.…”

Section: Introductionmentioning

confidence: 99%

Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant

Çolak¹

2021

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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In this study artificial neural network (ANN) has been developed in order to estimate the electricity production of cogeneration power plant, which produces a total of 11.52 MW electric power, consisting of two V type and 12 cylinders each of which is 5.760 kW diesel engines running with heavy fuel oil no 6. In the ANN which was developed for the estimation of electric power generation of cogeneration, power plant(W), Time period (t), working hours (h), fuel consumption (m) and internal power consumption (Wp) values were used as input variables. After evaluating the performance of different ANNs, an ANN, consisting of one hidden layer and 10 neurons, was considered to be the most ideal one. As a result of the comparison with experimental data, it is concluded that this model estimates the electricity generation values of the cogeneration power plant with an R-value of 0,99073 and mean square error 4.734e-8 Bu çalışmada iki adet V tipi 12 silindirli dizel motordan oluşan ve her biri 5.760 kW olmak üzere toplam 11.52 MW elektrik enerjisi üreten kojenerasyon enerji santralinin elektrik üretiminin tahmin edilmesi için yapay sinir ağı (YSA) geliştirilmiştir. 6 no'lu fuel oil ile çalışan kojenerasyon enerji santralinin elektrik enerjisi üretiminin (W) tahmini için geliştirilen YSA'da, zaman (t), çalışma saatleri (h), yakıt tüketimi (m) ve iç tüketim (Wp) değerleri giriş değişkenleri olarak kullanılmıştır. Farklı YSA'ların performansı değerlendirildikten sonra, bir gizli katman ve 10 nörondan oluşan YSA en ideal model olarak değerlendirilmiştir. Deneysel verilerle yapılan karşılaştırma sonucunda, bu modelin kojenerasyon enerji santralinin elektrik üretim değerlerini 0,99073 R değeri ve 4.734e-8 MSE ile tahmin edebileceği sonucuna varılmıştır.

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“…The results revealed that ANN gives better results compared to theoretical models. Colak et al 37 accompanied an tentative study on the determination of Cu‐Al 2 O 3 /water hybrid nanofluid's specific heat arranged in five dissimilar rate in among 0.0125% and 0.2% in the temperature series of 20°C to 65°C. With the experimental data obtained, they developed an ANN and suggested a novel mathematical equation for guesstimate the tentative values of the Cu‐Al 2 O 3 /Water.…”

Section: Introductionmentioning

confidence: 99%

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

Çolak

2020

Int J Energy Res

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107

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In this study, the effect of the amount of data used in the design of artificial neural networks (ANNs) on the predictive accuracy of ANNs was investigated. Five different ANNs were designed using the experimentally measured specific heat data of the Al 2 O 3 /water nanofluid prepared at volumetric concentrations of 0.0125, 0.025, 0.05, 0.1 and 0.2 using the Al 2 O 3 nanoparticle. The developed ANN is a multi-layer perceptron, feedforward and backpropagation model. In each ANN with 15 neurons in the hidden layer, the volumetric concentration (φ) and temperature (T) values were nominated as input layer factors and the specific heat value was estimated as the output value. With the aim of survey the effect of the amount of data on the predicted results of the ANN, a different amount of datasets were used in each developed ANN. In this context, in total 260 data were used in the Model 1 ANN. Subsequently, the total amount of data was reduced by 20% in each developed neural network and 55 data were used in the ANN named Model#5. The results obtained show that ANNs are highly talented of predicting the specific heat values of Al 2 O 3 /water nanofluid. However, in the comparisons, it was evaluated that the amount of data used had a share on the prediction performance of the ANN and that the decrease in the amount of data with the prediction performance of the ANN decreased.

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Experimental study for predicting the specific heat of water based Cu‐Al ₂ O ₃ hybrid nanofluid using artificial neural network and proposing new correlation

Cited by 104 publications

References 63 publications

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

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Experimental study for predicting the specific heat of water based Cu‐Al 2 O 3 hybrid nanofluid using artificial neural network and proposing new correlation

Cited by 104 publications

References 63 publications

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Applying artificial neural networks to predict the enhanced thermal conductivity of a phase change material with dispersed oxide nanoparticles

Experimental study for artificial neural network (ANN) based prediction of electric energy production of diesel engine based cogeneration power plant

An experimental study on the comparative analysis of the effect of the number of data on the error rates of artificial neural networks

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Product

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Experimental study for predicting the specific heat of water based Cu‐Al ₂ O ₃ hybrid nanofluid using artificial neural network and proposing new correlation