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Research Article

Comparative analysis of fouling resistance prediction in shell and tube heat exchangers using advanced machine learning techniques

Kouidri Ikram1, Kaidameur Djilali1, Dahmani Abdennasser1, 2, Raheem Al-Sabur3, Benyekhlef Ahmed2, Abdel-Nasser Sharkawy4, 5

1Dept. of Mechanical Eng., GIDD Industrial Eng. and Sustainable Development Laboratory, Faculty of Science and Technology, University of Relizane, Algeria
2Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Algeria
3Dept. of Mechanical Eng., College of Eng., University of Basrah, Basrah 61001, Iraq
4Dept. of Mechanical Eng., Faculty of Eng., South Valley University, Qena 83523, Egypt
5Dept. of Mechanical Eng., College of Eng., Fahad Bin Sultan University, Tabuk 47721, Saudi Arabia



Fouling resistance;  

Heat exchanger; 

Machine learning; 




Heat exchangers are utilized in a vast region of the process industry for heating and cooling. Long-term operation of heat exchangers results in decreased efficiency due to many problems, such as fouling. Therefore, the object of this research paper is to use three artificial intelligence techniques (feedforward neural networks-multilayer perceptron (FNN-MLP), nonlinear autoregressive networks with exogenous inputs (NARX), and support vector machines (SVM-RBF)) for predicting the fouling resistance in the tube and the shell heat exchanger in the preheating circuit of atmospheric distillation. The results summarize the high training as well as the predictive capacity of the "FFNN-MLP" model for predicting the fouling resistance in the heat exchanger with the highest coefficient of correlation (R = 0.99961) and the lowest root-mean-squared error (nRMSE = 1.0031%) for the testing phase, where the FNN-MLP network is superior to that provided using the SVM model (R = 0.9955 and nRMSE = 3.8652%). All the models of artificial networks and machine learning techniques used in the current work can be used to predict the fouling resistance in heat exchanger data with high accuracy. Despite this, the FNN-MLP model is the preferred model compared with the other proposed models, followed by the NARX model.

© 2023 MIM Research Group. All rights reserved.


20/04/2024 Collaboration for HSTD-2024Editorial Board of our journal and Organizing Committee of the III. International Conference on High-Speed Transport Development (HSTD) have agreed to collaborate. Extended versions of the selected papers from the conference will be published in our journal. For more see Events.

20/04/2024 Collaboration for DSL2024-SS1Editorial Board of our journal and Organizing Committee of the DSL2024 Fluid Flow, Energy Transfer & Design (SS1) have agreed to collaborate. Extended versions of the selected papers from the session will be published in our journal. For more see Events. .

8/12/2023 Special Issue: Embark on a journey of innovation with the journal of Research on Engineering Structures and Materials as we unveil a compelling opportunity for contributors in our upcoming special issue, "Design, Analysis, and Manufacturing of Composite Vehicle Structures." Led by distinguished Guest Editors Liubov Gavva and Oleg Mitrofanov from Moscow Aviation Institute. For more info see the link.

(More details of the news may be given in the News section)

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2023 Reviewer Awards:

Please, visit Reviewer Awards section for the winners of the 2022 RESM reviewer awards.

2023 Best Paper Award:

The paper authored by Ferzan Fidan, Naim Aslan, Mümin Mehmet Koç entitled as “Morpho-structural and compressive mechanical properties of graphene oxide reinforced hydroxyapatite scaffolds for bone tissue applications” is awarded.

2023 Most Cited Paper Award:

The paper authored by Ercan Işık, Ehsan Harirchian, Hüseyin Bilgin, Kirti Jadhav entitled as “The effect of material strength and discontinuity in RC structures according to different site-specific design spectra" is awarded.


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