Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries

Rezaei, Mohammad Hossein; Sadeghzadeh, Milad; Nazari, Mohammad Alhuyi; Ahmadi, Mohammad Hossein; Astaraei, Fatemeh Razi

doi:10.1093/ijlct/cty026

Cited by 71 publications

(36 citation statements)

References 34 publications

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“…[1][2][3] Utilization of renewable energy in the energy conversion systems, such as the sun or wind energy can be a solution to these challenging issues. 4,5 It is conceived renewable energies specifically solar energy, be a major origin of providing energy demands of human beings activity. 6 Although solar energy is an everlasting and the most available source of energy, it has some negative features such as lower density and its intermittent inherent.…”

Section: Introductionmentioning

confidence: 99%

Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Sadeghzadeh

Ahmadi

Kahani

et al. 2019

Energy Science & Engineering

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115

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In the current study, Multilayer Perceptron Artificial Neural Network (MLP‐ANN) mode, Radial Basis Function Artificial Neural Network (RBF‐ANN), and Elman Back Propagation Neural Network (Elamn BP‐ANN) are developed to predict the thermal efficiency of a flat‐plate solar collector. TiO2 (20 nm)/water nanofluids are prepared using two‐step method and used in the designed solar system. All experiments are done in Mashhad city, Iran (Longitude/Latitude: 36.2605°N, 59.6168°E), according to EUROPEAN STANDARD EN 12975‐2 as a quasi‐dynamic test (QDT) method, and the solar collector is exposed to the south with the tilt angle of 55°. Three levels of inlet temperature (ambient air temperature, 52 and 74°C), 3 levels of volumetric flow rate (36, 72, and 108 L/(m2.h)), and 4 levels of nanofluid concentrations (0, 0.1, 0.2, and 0.3 wt.%) are considered as the input data, and the thermal efficiency of the solar system is calculated. According to the output results of developed models, the best prediction of thermal performance is obtained by MLP‐ANN model, although other generated models are also able to predict the efficiency of the solar collector with appropriated accuracy.

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Section: Introductionmentioning

confidence: 99%

Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Sadeghzadeh

Ahmadi

Kahani

et al. 2019

Energy Science & Engineering

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115

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“…Based on International Energy Agency report, energy related carbon dioxide emission had 1.4% growth in 2017. Clean energy sources including solar, geothermal, wind and hydropower gained importance in recent decades because of fossil fuels' environmental problems and the possibility of their exhaustion in near future [6][7]. Renewable energies are employed for various applications such as heating [8][9][10], cooling [11], electricity generation [12][13], and desalination [14].…”

Section: Introductionmentioning

confidence: 99%

Applications of nanofluids in geothermal: A review

Ahmadi¹,

Ramezanizadeh²,

Nazari³

et al. 2018

MMEP

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Geothermal energy is one of the most suitable sources of energy since it is possible to use it continuously for generating power and providing heat. Firstly, recent trends in geothermal energy are discussed and compared with other types of renewable energies. According to the results of this section, geothermal energy is an attractive choice for future power generation due to its low carbon dioxide emission and levelzied cost of electricity in comparison with other renewable energy sources. Afterwards, applications of nanofluid in geothermal-based energy systems are reviewed and their important results are represented. On the basis of literature review, using nanofluids can result in augment in geothermal systems. The enhancement is dependent on several factors including the type of nanofluid, concentration and system specification. According to the results of a study, the effect of using nanofluid on heat transfer rate became more significant at higher flow rates. In addition, using nanofluids can reduce the size of heat exchangers used in geothermal-based system. The main effects of employing nanofluids is increase in convective heat transfer and pressure loss.

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“…In this way, some investigations have been implemented on the use of nanotechnology in thermal applications [9][10][11][12][13][14][15][16][17][18][19]. Additionally, some studies 2 of 14 have focused on the prediction of the thermal conductivity ratio associated with various nanofluids with the help of using experiments and artificial neural networks [20][21][22][23][24][25][26][27][28][29][30][31]. Vafaei et al [32] predicted the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids by using ANN (artificial neural network) at the temperature range of 25-50 • C. According to the results, the best performance belonged to the neural network with 12 neurons in the hidden layer.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

et al. 2020

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In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.

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Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries

Cited by 71 publications

References 34 publications

Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Smart modeling by using artificial intelligent techniques on thermal performance of flat‐plate solar collector using nanofluid

Applications of nanofluids in geothermal: A review

Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

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