Artificial Intelligence-Based Techniques for Fouling Resistance Estimation of Shell and Tube Heat Exchanger: Cascaded Forward and Recurrent Models

Kouidri, Ikram; Dahmani, Abdennasser; Furizal, Furizal; Ma’arif, Alfian; Mostfa, Ahmed A.; Amrane, Abdeltif; Mouni, Lotfi; Sharkawy, Abdel-Nasser

doi:10.3390/eng6050085

Open AccessArticle

Artificial Intelligence-Based Techniques for Fouling Resistance Estimation of Shell and Tube Heat Exchanger: Cascaded Forward and Recurrent Models

by

Ikram Kouidri

¹

,

Abdennasser Dahmani

^2,3,

Furizal Furizal

^4,5

,

Alfian Ma’arif

⁶

,

Ahmed A. Mostfa

⁷

,

Abdeltif Amrane

^8,*

,

Lotfi Mouni

⁹

and

Abdel-Nasser Sharkawy

^10,11

¹

GIDD Industrial Engineering and Sustainable Development Laboratory, Department of Mechanical Engineering, Faculty of Science and Technology, University of Relizane, Bourmadia 48000, Algeria

²

Department of Mechanical Engineering, Faculty of Applied Sciences, University of Bouira, Bouira 10000, Algeria

³

Laboratory of Biomaterials and Transport Phenomena (LBMPT), University of Medea, Medea 26000, Algeria

⁴

Department of Research and Development, Peneliti Teknologi Teknik Indonesia, Sleman 55281, Indonesia

⁵

Department of Informatics Engineering, Universitas Islam Riau, Pekanbaru 28284, Indonesia

⁶

Department of Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta 55191, Indonesia

⁷

Department of Computer Science, University of Al-Hamdaniya, Nineveh 41006, Iraq

⁸

Univ Rennes, Ecole Nationale Supérieure de Chimie de Rennes, CNRS, ISCR—UMR6226, F-35000 Rennes, France

⁹

Laboratoire de Gestion et Valorisation des Ressources Naturelles et Assurance Qualité, Faculté SNVST, Université de Bouira, Bouira 10000, Algeria

¹⁰

Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt

¹¹

Mechanical Engineering Department, College of Engineering, Fahad Bin Sultan University, Tabuk 47721, Saudi Arabia

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^*

Author to whom correspondence should be addressed.

Eng 2025, 6(5), 85; https://doi.org/10.3390/eng6050085

Submission received: 11 February 2025 / Revised: 7 April 2025 / Accepted: 21 April 2025 / Published: 24 April 2025

(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications)

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Abstract

Heat exchangers play a crucial role in transferring heat between two mediums, directly impacting energy efficiency, product quality, and operational safety in industrial systems. This study presents a novel approach for fouling resistance estimation using two artificial intelligence models, the cascaded forward network (CFN) and the recurrent neural network (RN), with a minimal set of six input parameters. The proposed models utilize temperature and flow sensor data from heat exchangers to predict fouling resistance. The training process is optimized using the Levenberg–Marquardt (LM) algorithm, ensuring rapid convergence and high accuracy. Model performance is assessed based on mean squared error (MSE), regression values (R), and statistical error analysis. The results demonstrate that both models achieve high accuracy in predicting fouling resistance, with the CFN model outperforming the RN model. The CFN model achieves an MSE of 1.54 × 10⁻⁸, significantly lower than the RN model (MSE = 3.05 × 10⁻⁸), resulting in a 49.5% improvement in accuracy. Additionally, statistical analysis, including error histograms and correlation analysis, further confirms the robustness of the proposed models. Compared to traditional methods, the proposed AI-based models reduce computational complexity while maintaining superior accuracy. This study highlights the potential of AI in predictive maintenance and industrial optimization, paving the way for future enhancements in intelligent fouling estimation systems.

Keywords: heat exchanger; fouling resistance; cascaded forward network; recurrent network; statistical analysis; predictive maintenance

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MDPI and ACS Style

Kouidri, I.; Dahmani, A.; Furizal, F.; Ma’arif, A.; Mostfa, A.A.; Amrane, A.; Mouni, L.; Sharkawy, A.-N. Artificial Intelligence-Based Techniques for Fouling Resistance Estimation of Shell and Tube Heat Exchanger: Cascaded Forward and Recurrent Models. Eng 2025, 6, 85. https://doi.org/10.3390/eng6050085

AMA Style

Kouidri I, Dahmani A, Furizal F, Ma’arif A, Mostfa AA, Amrane A, Mouni L, Sharkawy A-N. Artificial Intelligence-Based Techniques for Fouling Resistance Estimation of Shell and Tube Heat Exchanger: Cascaded Forward and Recurrent Models. Eng. 2025; 6(5):85. https://doi.org/10.3390/eng6050085

Chicago/Turabian Style

Kouidri, Ikram, Abdennasser Dahmani, Furizal Furizal, Alfian Ma’arif, Ahmed A. Mostfa, Abdeltif Amrane, Lotfi Mouni, and Abdel-Nasser Sharkawy. 2025. "Artificial Intelligence-Based Techniques for Fouling Resistance Estimation of Shell and Tube Heat Exchanger: Cascaded Forward and Recurrent Models" Eng 6, no. 5: 85. https://doi.org/10.3390/eng6050085

APA Style

Kouidri, I., Dahmani, A., Furizal, F., Ma’arif, A., Mostfa, A. A., Amrane, A., Mouni, L., & Sharkawy, A.-N. (2025). Artificial Intelligence-Based Techniques for Fouling Resistance Estimation of Shell and Tube Heat Exchanger: Cascaded Forward and Recurrent Models. Eng, 6(5), 85. https://doi.org/10.3390/eng6050085

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Artificial Intelligence-Based Techniques for Fouling Resistance Estimation of Shell and Tube Heat Exchanger: Cascaded Forward and Recurrent Models

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