In pursuit of the best artificial neural network configuration for the prediction of output parameters of corrugated plate heat exchanger

Dheenamma, Mutuku; Soman, Divya P.; Muthamizhi, K.; Kalaichelvi, P.

doi:10.1016/j.fuel.2018.11.034

Cited by 18 publications

(3 citation statements)

References 11 publications

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“…Eight input parameters have been considered and three output parameters have been estimated (Nusselt and Reynolds numbers and the overall heat transfer coefficient). Recently, Dheenamma et al [22] developed ANN models for predicting the output plate heat exchanger parameters (overall heat transfer coefficient, effectiveness factor, friction factor of cold and hot fluids), utilizing four (cold and hot fluid Reynolds and Prandtl numbers) or five (concentration of the cold fluid cold and hot fluid Reynolds and Prandtl numbers) input parameters. Mohanraj [23] reviewed some applications of artificial neural networks for different heat transfer equipment and found that most architectures of the neural networks for thermal analysis of heat exchangers are multilayer feed forward networks, while only very few are neuro fuzzy interface systems (ANFIS) or radial biased function networks.…”

Section: Introductionmentioning

confidence: 99%

Modeling the outlet temperature in heat exchangers: Case study

Bărbulescu

Barbeș

2021

Therm sci

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This article presents the results of the study of the heat transfer in a heat exchanger where the working fluid is the crude oil prepared for desalination, and the thermic agent is the recirculating heavy gasoline fraction. Firstly, the Reynolds numbers have been computed using the temperatures and flow rates of the fluids as input variables. Then, General Regression Neural Network and Multilayer Perceptron (MLP) were used for the outlet temperatures estimation using the inlet temperatures and the Reynolds numbers as input variables. The best models on the training dataset were obtained utilizing a MLP with one hidden layer, while the best performance on the validation dataset was obtained using a MLP network with two hidden layers

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Section: Introductionmentioning

confidence: 99%

Modeling the outlet temperature in heat exchangers: Case study

Bărbulescu

Barbeș

2021

Therm sci

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“…The artificial intelligence techniques are the most efficient tools to accurately predict the performance of the thermoelectric generator system for waste heat recovery and diminish the limitations of the corresponding experimental and numerical approaches. Dheenamma et al have shown the artificial neural network models to predict the overall heat transfer coefficient, friction factors of hot and cold fluids, and effectiveness of the plate type heat exchanger by considering the Reynolds number of hot and cold fluids, Prandtl numbers of hot and cold fluids and the concentration of the cold fluid as the input conditions [17]. Angeline et al have formulated the artificial neural network to predict the performance parameters of open circuit voltage, maximum power and matched load resistance of the thermoelectric generator for various hot side temperatures.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

et al. 2020

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The present study elaborates the suitability of the artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) to predict the thermal performances of the thermoelectric generator system for waste heat recovery. Six ANN models and seven ANFIS models are formulated by considering hot gas temperatures and voltage load conditions as the inputs to predict current, power, and thermal efficiency of the thermoelectric generator system for waste heat recovery. The ANN model with the back-propagation algorithm, the Levenberg–Marquardt variant, Tan-Sigmoidal transfer function and 25 number of hidden neurons is found to be an optimum model to accurately predict current, power and thermal efficiency. For current, power and thermal efficiency, the ANFIS model with pi-5 or gauss-5-membership function is recommended as the optimum model when the prediction accuracy is important while the ANFIS model with gbell-3-membership function is suggested as the optimum model when the prediction cost plays a crucial role along with the prediction accuracy. The proposed optimal ANN and ANFIS models present higher prediction accuracy than the coupled numerical approach.

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“…Similarly, Dheenamma et al [21] presented ANN model for predicting overall heat transfer coefficient, effectiveness factor and friction factor, while Mohanraj et al [22] used ANN for analyzing energy and exergy of refrigeration, air conditioning and heat pump. In their further investigation, they performed thermal analysis on heat exchanger using ANN.…”

Section: Introductionmentioning

confidence: 99%

CFD analysis for thermo-hydraulic properties in a tubular heat exchanger using curved circular rings

Singh

Kharkwal

Gautam

et al. 2020

J Therm Anal Calorim

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The never-ending demands of humans have led researchers to find more efficient energy conversion technologies. Enhancing heat transfer rate in a tubular heat exchanger through curved circular rings is one type of passive method that is studied in this work. The computational analysis has been done using "FLUENT" module of ANSYS 16.0. The tubular heat exchanger has internal diameter of 68.1 mm, length of 1.5 m and constant heat flux of 1000 W m −2. Air is the working fluid, which flows in Re varying from 3000 to 21,000. After reaching steady state conditions, the Nusselt number and friction factor are calculated for CCR rings. Furthermore, perforation has been done on these rings with perforation index 8, 12 and 16% and thermo-hydraulic properties are investigated. Computational results show that perforating the rings have a significant effect on heat transfer and friction factor. Greater the perforation index, greater is the Nu and TPF. The maximum enhancements obtained in Nu and TPF are 7.4 and 1.56 times, respectively (as compared to that of smooth tube), for PCCR-16%.

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In pursuit of the best artificial neural network configuration for the prediction of output parameters of corrugated plate heat exchanger

Cited by 18 publications

References 11 publications

Modeling the outlet temperature in heat exchangers: Case study

Modeling the outlet temperature in heat exchangers: Case study

Artificial Neural Network and Adaptive Neuro-Fuzzy Interface System Modelling to Predict Thermal Performances of Thermoelectric Generator for Waste Heat Recovery

CFD analysis for thermo-hydraulic properties in a tubular heat exchanger using curved circular rings

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