Prediction of heat transfer coefficient, pressure drop, and overall cost of double-pipe heat exchangers using the artificial neural network

Çolak, Andaç Batur; Açıkgöz, Özgen; Mercan, Hatice; Dalkılıç, Ahmet Selim

doi:10.1016/j.csite.2022.102391

Cited by 22 publications

(5 citation statements)

References 45 publications

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“…Andaç Batur Çolak et al [1] used an ANN model to determine the thermophysical performance of the DPHE. Pipe and annulus side pressure decreases, and overall cost were evaluated using Model-one with deviations of 0.16%, 0.23%, 0.02%, and 0.003%, and Model-two with variances of 0.02%, 0.18%, 0.16%, and 0.15%, respectively.…”

Section: Literature Review and Objectivementioning

confidence: 99%

“…Heat transfer enhancement techniques are frequently employed in thermal power plants, refrigerators, air conditioners, vehicles, and other technical applications. Over the past few years, various heat transfer augmentation practices are employed in engineering applications [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Heat transfer augmentation (enhancement) techniques are classified as: (i) Active techniques, (ii) Passive techniques, and (iii) Compound techniques.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Investigation of the Effect of Integrated Fins on Heat Transfer Rate of Double Pipe Heat Exchanger

B M,

Sadashive Gowda,

H V

2023

jmmf

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In this paper, the effects of integrated fins on the thermal performance of the concentric tube heat exchanger with a variable flow rate of hot and cold water are discussed. The inner pipe is made of copper pipe of length = 800 mm, OD=30 mm, ID=26 mm, t=2 mm. the integrated fines of the geometry 1.25 mm and 1.5 mm pitch length, 1 mm of the depth of cut are considered in the study. The thermal performance of the heat exchanger with a flow rate of hot and cold fluids ranges from 0.014 kg s- 1 to 0.07 kg s-1 and the effects of parallel and counter flow direction are also discussed in detail. The experimental result confirms that the thermal performance of the concentric tube heat exchanger is more in the case of the tube with integrated fins and counter flow direction with the highest flow rate of water in the pipe and annulus of the heat exchanger.

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Section: Literature Review and Objectivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of the Effect of Integrated Fins on Heat Transfer Rate of Double Pipe Heat Exchanger

B M,

Sadashive Gowda,

H V

2023

jmmf

View full text Add to dashboard Cite

show abstract

“…Of particular interest is the potential to enhance the thermal conductivity of these fluids by incorporating nanoparticles made of highly thermally conductive materials. Common base fluids include water, ethylene glycol, oil, and other fluids, while the nanoparticles can be composed of metals, metal oxides, carbon nanotubes, or graphene [45][46][47][48][49][50][51][52][53][54].…”

Section: Dalkilic Et Al's Experimental Investigation Included a Measu...mentioning

confidence: 99%

Untitled

2023

Enenstrg

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Solid waste management is among the most popular urban environmental problems in many developing countries due to increasing urban, economic, and industrial activities. Indonesia is a developing country with many coastal settlements, such as Makassar City. This study aims to determine Waste Management on the coast of Makassar City, namely the "Kawasan Pelabuhan", a slum settlement. An investigation was carried out regarding waste generation, storage, and collection. The population of this study was 119 houses/households consisting of permanent and semi-permanent houses partially built over the sea on the coast. The method used in this research is quantitative and qualitative. The analysis results show that the volume of waste generated is around 58% organic and 42% inorganic, with an average of 1.8 kg per household for organic waste and 1.31 kg for inorganic waste. Most houses use individual bins; only houses on the main road get communal collection and storage services.

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“…In recent years, neural networks have been widely used in a variety of fields due to their ability to fit arbitrary continuous functions and their strong nonlinear mapping capabilities [7][8][9]. Moreover, Golgiyaz et al also proposed an artificial neural network prediction model for predicting flue gas temperature using flame images spectra, which is able to better analyze the correlation between the flame image and the reference temperature and emissions of the flue gas content through the features of the flame image and analyze the reference temperature and emissions of the flue gas content [10].…”

Section: Introduction 1literature Reviewmentioning

confidence: 99%

A boiler oxygen content and furnace temperature prediction model based on honey badger algorithm optimized neural network

Ji,

Tao,

Zhang

2024

Eng. Res. Express

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Aiming at the problem that it is difficult to accurately predict the oxygen content and furnace temperature during the combustion process inside the circulating fluidized bed boiler, this paper proposes a prediction model with a mixture of the convolutional neural network(CNN), bi-directional long-short term memory network(biLSTM), and honey badger algorithm(HBA). First, process variables related to furnace temperature and oxygen content were selected as input variables using actual test data from circulating fluidized bed boilers. Secondly, the parsed input variables are analyzed by CNN through an analytical mechanism to extract the data into smaller details and features, which are used as inputs to the biLSTM layer. The BiLSTM layer selectively memorizes the information of the input temporal data and learns the information dependence of the temporal data sequences, which then solves the temporal problem of the training of the irregular trend of the oxygen content and the furnace temperature. Finally, the HBA is utilized to continuously traverse and search the CNN-biLSTM model to find the optimal parameters of the model. The experimental results show: the CNN-biLSTM neural network model mixed with the HBA is able to accurately predict the oxygen content and furnace temperature. The experimental results show that the CNN-biLSTM neural network model with hybrid HBA is able to accurately predict the oxygen content and furnace temperature, and the average MAPE errors for the oxygen content are HBA-CNN-biLSTM (2.92E-03), CNN (7.96E-02), LSTM (5.13E-02), GRU (4.83E-02), RF (4.96E-02), RBF (8.41E-02), SVM (5.71E-02), RNN ( 5.53E-02), CNN-LSTM (4.79E-02).

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Prediction of heat transfer coefficient, pressure drop, and overall cost of double-pipe heat exchangers using the artificial neural network

Cited by 22 publications

References 45 publications

Experimental Investigation of the Effect of Integrated Fins on Heat Transfer Rate of Double Pipe Heat Exchanger

Experimental Investigation of the Effect of Integrated Fins on Heat Transfer Rate of Double Pipe Heat Exchanger

Untitled

A boiler oxygen content and furnace temperature prediction model based on honey badger algorithm optimized neural network

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