Exergy analysis of direct expansion solar-assisted heat pumps using artificial neural networks

Mohanraj, M.; Jayaraj, S.; Muraleedharan, C.

doi:10.1002/er.1534

Cited by 50 publications

(19 citation statements)

References 22 publications

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“…The ANN-predicted results obtained from LM11 (MLFFN using LM variant with eleven neurons in hidden layer) network configuration are used for thermodynamic performance simulation of a FCSAH using flat and pin-fin absorber plate. The optimal network parameters obtained in this work are similar to the earlier studies reported on modeling of solar air heaters [27], geothermal heat pumps [42] and solar-assisted heat pumps [43][44][45][46]. According to the above-mentioned studies, the MLFFN with LM variant and one hidden layer was identified as the good network configuration.…”

Section: Thermodynamic Performance Predictions Using Artificial Neuramentioning

confidence: 82%

“…The process of combining the signals and generating the output of each connection is represented as mass. The three-layer feed-forward network is the widely used network configuration for energy and exergy performance predictions of renewable energy systems [42][43][44][45][46][47][48][49][50]. The network is trained with a suitable learning approach to perform a particular function by adjusting the mass coefficient values between processing neurons.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

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Thermodynamic performance of forced convection solar air heaters using pin–fin absorber plate packed with latent heat storage materials

Kumar

Babu²,

Mohanraj

2016

J Therm Anal Calorim

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In this work, the thermodynamic performance of a forced convection solar air heater (FCSAH) was evaluated using two different absorber plate configurations, namely a flat absorber plate and a pin-fin absorber plate packed with latent heat storage material (paraffin wax). The experiments were carried out under the meteorological conditions of Coimbatore city in India. The parameters such as outlet air temperature, thermo-hydraulic efficiency and exergy efficiency were evaluated with reference to solar intensity, ambient temperature and ambient wind velocity. An artificial neural network (ANN) model was developed to simulate the thermodynamic performance of a FCSAH using flat absorber plate and pin-fin absorber plate packed with paraffin wax to have a realistic performance comparison. The ANN-predicted results are found to be closer to the experimental values with a maximum fraction of absolute variance, minimum root-mean-square errors and minimum coefficient of variance. The results showed that the pin-fin absorber plate packed with paraffin wax has additional heat storage for the period of 3 h with 2-5°C enhanced outlet air temperature when compared to the flat absorber plate. The FCSAH using pin-fin absorber plate packed with paraffin wax has 3-35 % higher thermo-hydraulic efficiency with 2-15 % higher exergy efficiency when compared to the flat absorber plate. The results confirmed that pin-fin absorber plate packed with paraffin is a good option to enhance the thermodynamic performance of a FCSAH.

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Section: Thermodynamic Performance Predictions Using Artificial Neuramentioning

confidence: 82%

Section: Artificial Neural Networkmentioning

confidence: 99%

Thermodynamic performance of forced convection solar air heaters using pin–fin absorber plate packed with latent heat storage materials

Kumar

Babu²,

Mohanraj

2016

J Therm Anal Calorim

Self Cite

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“…A real technical revolution is needed to help the power industry to become cleaner than before. Solar energy aided power [6] or equipment [7] have been noticed and done some groping research works in the whole world.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of solar aided thermal power generation with various power plants

Qin

Yang

et al. 2010

Int. J. Energy Res.

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SUMMARYSolar aided power generation (SAPG) is an efficient way to make use of low or medium temperature solar heat for power generation purposes. The so-called SAPG is actually 'piggy back' solar energy on the conventional fuel fired power plant. Therefore, its solar-to-electricity efficiency depends on the power plant it is associated with. In the paper, the developed SAPG model has been used to study the energy and economic benefits of the SAPG with 200 and 300 MW typical, 600 MW subcritical, 600 MW supercritical, and 600 and 1000 MW ultra-supercritical fuel power units separately. The solar heat in the temperature range from 260 to 901C is integrated with abovementioned power units to replace the extraction steam (to preheat the feedwater) in power boosting and fuel-saving operating modes. The results indicate that the benefits of SAPG are different for different steam extracted positions and different power plants. Generally, the larger the power plant, the higher the solar benefit if the same level solar is integrated.

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“…The application of artificial neural network for the exergy performance prediction of solar adsorption refrigeration system working at different conditions is necessary nowadays for making the analysis simple. Many earlier studies have reported the application of artificial neural network for the performance predictions of vapour compression refrigeration systems such as for direct expansion heat pump [7], for modeling solar cooling systems [8], and for modeling cascade refrigeration systems [9] with the acceptable accuracy. Recently some works about the use of ANN in energy systems have been published [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Performance Prediction of Solar Adsorption Refrigeration System by Ann

Baiju

Muraleedharan

2012

ISRN Thermodynamics

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This paper proposes a new approach for the performance analysis of a single-stage solar adsorption refrigeration system with activated carbon-R134a as working pair. Use of artificial neural network has been proposed to determine the performance parameters of the system, namely, coefficient of performance, specific cooling power, adsorbent bed (thermal compressor) discharge temperature, and solar cooling coefficient of performance. The ANN used in the performance prediction was made in MATLAB (version 7.8) environment using neural network tool box.In this study the temperature, pressure, and solar insolation are used in input layer. The back propagation algorithm with three different variants namely Scaled conjugate gradient, Pola-Ribiere conjugate gradient, and Levenberg-Marquardt (LM) and logistic sigmoid transfer function were used, so that the best approach could be found. After training, it was found that LM algorithm with 9 neurons is most suitable for modeling solar adsorption refrigeration system. The ANN predictions of performance parameters agree well with experimental values with R2 values close to 1 and maximum percentage of error less than 5%. The RMS and covariance values are also found to be within the acceptable limits.

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Exergy analysis of direct expansion solar-assisted heat pumps using artificial neural networks

Cited by 50 publications

References 22 publications

Thermodynamic performance of forced convection solar air heaters using pin–fin absorber plate packed with latent heat storage materials

Thermodynamic performance of forced convection solar air heaters using pin–fin absorber plate packed with latent heat storage materials

Evaluation of solar aided thermal power generation with various power plants

Performance Prediction of Solar Adsorption Refrigeration System by Ann

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