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Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

Author

Listed:
  • Amir Zolghadri

    (Department of Chemistry, North Tehran Branch, Islamic Azad University, Tehran 1651153311, Iran)

  • Heydar Maddah

    (Department of Chemistry, Payame Noor University (PNU), Tehran 19395-3697, Iran)

  • Mohammad Hossein Ahmadi

    (Faculty of Mechanical Engineering, Shahrood University of Technology, Shahrood 3619995161, Iran)

  • Mohsen Sharifpur

    (Department of Mechanical and Aeronautical Engineering, University of Pretoria, Pretoria 0002, South Africa
    Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404, Taiwan)

Abstract

This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of 150–350, temperature in the range of 70–90 K, and nanoparticle concentration in the range of 2–4% were selected as network input variables, while the corresponding Nusselt number and energy consumption were considered as the network target. The network has 3 inputs, 1 hidden layer with 22 neurons and an output layer. The SOM neural network was also used to determine the number of winner neurons. The advanced optimal artificial neural network model shows a reasonable agreement in predicting experimental data with mean square errors of 0.0023357 and 0.00011465 and correlation coefficients of 0.9994 and 0.9993 for the Nusselt number and energy consumption data set. The obtained values of e MAX for the Nusselt number and energy consumption are 0.1114, and 0.02, respectively. Desirable results obtained for the two factors of correlation coefficient and mean square error indicate the successful prediction by artificial neural network with a topology of 3-22-2.

Suggested Citation

  • Amir Zolghadri & Heydar Maddah & Mohammad Hossein Ahmadi & Mohsen Sharifpur, 2021. "Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:16:p:8824-:d:609956
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    References listed on IDEAS

    as
    1. Hojjat, Mohammad, 2020. "Nanofluids as coolant in a shell and tube heat exchanger: ANN modeling and multi-objective optimization," Applied Mathematics and Computation, Elsevier, vol. 365(C).
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    Cited by:

    1. Thejaraju Rajashekaraiah & Girisha Kanuvanahalli Bettaiah & Parvathy Rajendran & Mohamed Abbas & Sher Afghan Khan & C. Ahamed Saleel, 2022. "Numerical Modelling and Experimental Validation of Novel Para Winglet Tape for Heat Transfer Enhancement," Mathematics, MDPI, vol. 10(16), pages 1-19, August.
    2. Ammar A. Melaibari & Yacine Khetib & Abdullah K. Alanazi & S. Mohammad Sajadi & Mohsen Sharifpur & Goshtasp Cheraghian, 2021. "Applying Artificial Neural Network and Response Surface Method to Forecast the Rheological Behavior of Hybrid Nano-Antifreeze Containing Graphene Oxide and Copper Oxide Nanomaterials," Sustainability, MDPI, vol. 13(20), pages 1-17, October.
    3. Basma Souayeh & Suvanjan Bhattacharyya & Najib Hdhiri & Fayçal Hammami & Essam Yasin & S. Suresh Kumar Raju & Mir Waqas Alam & Tarfa Alsheddi & Muneerah Al Nuwairan, 2022. "Effect of Magnetic Baffles and Magnetic Nanofluid on Thermo-Hydraulic Characteristics of Dimple Mini Channel for Thermal Energy Applications," Sustainability, MDPI, vol. 14(16), pages 1-27, August.
    4. Basma Souayeh & Suvanjan Bhattacharyya & Najib Hdhiri & Mir Waqas Alam, 2022. "Selection of Best Suitable Eco-Friendly Refrigerants for HVAC Sector and Renewable Energy Devices," Sustainability, MDPI, vol. 14(18), pages 1-16, September.

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