Artificial intelligence in industrial heat exchanger fouling prediction: A 20-year systematic review of AI, ML, and DL approaches

Authors

• Abdul Wahid Soomro, Universiti Malaya
• Miss Laiha Mat Kiah, Universiti Malaya
• Rafidah Md Noor, Universiti Malaya
• Salim Newaz Kazi, Universiti Malaya
• Kaleemullah Shaikh, Universiti Malaya
• Wajahat Ahmed Khan, Universiti Malaya
• Ihsan Ali, Southeast Missouri State University

Document Type

Review

Publication Date

2-1-2026

Abstract

Fouling in heat exchangers (HXs) affects various industries by lowering efficiency and increasing costs. Traditional fouling-prediction models often do not reflect important mechanistic information and thus become very complex and less reliable. The applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) open new frontiers, as these techniques can model complex correlations and work with large volumes of data. This review synthesizes 51 articles published between 2005 and June 2025, outlining key trends, persistent research limitations, and emerging directions. Models such as artificial neural networks (ANNs)/deep neural networks (DNNs) and Gaussian process regression (GPR) deliver the optimal results in terms of accurate prediction.

Publication Title

ICT Express

DOI

10.1016/j.icte.2025.12.003

Recommended Citation

Soomro, Abdul Wahid; Mat Kiah, Miss Laiha; Md Noor, Rafidah; Kazi, Salim Newaz; Shaikh, Kaleemullah; Khan, Wajahat Ahmed; and Ali, Ihsan, "Artificial intelligence in industrial heat exchanger fouling prediction: A 20-year systematic review of AI, ML, and DL approaches" (2026). Research Publications (2026 to 2030). 219.
https://knova.um.edu.my/research_publications_2026_2030/219

Volume

12

Issue

1

First Page

92