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)

Amir Zolghadri, Heydar Maddah, Mohammad Hossein Ahmadi and Mohsen Sharifpur
Additional contact information
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

Sustainability, 2021, vol. 13, issue 16, 1-17

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.

Keywords: artificial neural network; Nusselt number; mean square error; SOM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/16/8824/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/16/8824/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:16:p:8824-:d:609956

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().