Machine Learning Modeling and Experimental Optimization of Heat Exchanger for Efficient Building Systems

Articles in Press

Document Type : Research Paper

Authors
Nayeemuddin Mohammed 1
Faizan Ahmed 2
Hiren Mewada 3
1 Department of Civil Engineering, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia
2 Department of Mechanical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia
3 Department of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia
10.22055/jacm.2026.49121.5747
Abstract
The compact design, high thermal efficiency, and adaptability to various fluids make plate heat exchangers (PHE) valuable applications in smart buildings. A computer-controlled experimentation unit employs PLC-SCADA software to monitor and record fluid temperature, flow rate, enabling efficient control, visualization, and data recording. The experimental results indicate the hot and cold fluid flow rate 0.5-1.4 l/min, 1-3 l/min, feed temperature (50–60 °C), optimal heat exchanger efficiency 58.54 %. Response Surface Methodology (RSM) and Machine Learning (ML) such as Decision Tree (DT), Adaptive Boosting (AdaBoost), and Bootstrap Aggregating (Bagging) were used along with hyperparameters using grid search to achieve the best predictive performance. RSM demonstrates the highest accuracy, achieving an R2 of 0.99 and low error metrics (RMSE = 0.71, MAE = 0.62), making it a robust method for performance prediction. Bagging demonstrated the best performance, with an R2 score of 0.942 and 0.935 on the training and test datasets, the lowest error (RMSE = 2.037, MAE = 1.566) on the test dataset. To link the algorithm's predictions to thermal physics, a feature-importance analysis using Shapley Additive Explanations (SHAP) is also conducted. It is observed that the cold fluid flow rate is the primary driving parameter for prediction, showing an inverse relationship with effectiveness. The findings of this research indicate that both RSM and ML techniques can predict the PHE effectiveness with good accuracy. This paper proposes a key experimental and data-driven approach for predicting and optimizing the performance of a PHE that can be integrated into smart building technologies.
Keywords
Machine learning
Modeling
Optimization
Heat exchanger
Effectiveness
Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

[1] Ahmed, F., Sharizal Abdul Aziz, M., Palaniandy, P., Shaik, F., A review on application of renewable energy for desalination technologies with emphasis on concentrated solar power, Sustainable Energy Technologies and Assessments 53, 2022, 102772.
[2] Indumathy, M., Sobana, S., Panda, B., Panda, R.C., Modelling and control of plate heat exchanger with continuous high-temperature short time milk pasteurization process – A review, Chemical Engineering Journal Advances, 11, 2022, 100305.
[3] Zheng, D., Wang, J., Chen, Z., Baleta, J., Sundén, B., Performance analysis of a plate heat exchanger using various nanofluids, International Journal of Heat and Mass Transfer, 158, 2020, 119993.
[4] Alazwari, M.A., Abu-Hamdeh, N.H., Salilih, E.M., Exergetic performance analysis on helically coiled tube heat exchanger-forecasting thermal conductivity of SiO2/EG nanofluid using ANN and RSM to examine effectiveness of using nanofluids, Journal of Thermal Analysis and Calorimetry, 144, 2021, 2721–2733.
[5] Yoladi, M., Akyurek, E.F., Kotcioglu, İ., Experimental investigation of cross-flow heat exchangers with helical fins: Performance analysis via RSM and ANN, International Journal of Thermal Sciences, 218, 2025, 110111.
[6] Gherasim, I., Taws, M., Galanis, N., Nguyen, C.T., Heat transfer and fluid flow in a plate heat exchanger, Part I. Experimental investigation, International Journal of Thermal Sciences, 50, 2011, 1492–1498.
[7] Zeinali Heris, S., Pasvei, S., Pourpasha, H., Mohammadfam, Y., Sharifpur, M., Meyer, J., Experimental analysis of graphene-COOH/water and TiO2/Water nanofluids in plate heat exchangers: Heat transfer performance and stability, Renewable Energy, 245, 2025, 122822.
[8] Tekir, M., Experimental study on the thermal performance of hybrid nanofluid in a compact plate heat exchanger under the influence of a magnetic field, Case Studies in Thermal Engineering, 69, 2025, 106031.
[9] Yahaya, R.I., Mustafa, M.S., Md Arifin, N., Md Ali, F., Mohamed Isa, S.S.P., Heat transfer optimization for the unsteady mixed convection flow of hybrid nanofluid over a permeable EMHD riga plate with thermal radiation and convective boundary condition, Multiscale and Multidisciplinary Modeling, Experiments and Design, 8, 2025, 192.
[10] Lowrey, S., Hughes, C., Sun, Z., Thermal-hydraulic performance investigation of an aluminum plate heat exchanger and a 3D-printed polymer plate heat exchanger, Applied Thermal Engineering, 194, 2021, 117060.
[11] Ham, J., Lee, G., Kwon, O., Bae, K., Cho, H., Numerical study on the flow maldistribution characteristics of a plate heat exchanger, Applied Thermal Engineering, 224, 2023, 120136.
[12] Mayer, J.M., Walters, N., Albrecht, K.J., Madden, D.A., Bala Chandran, R., Experimental characterization of heat transfer coefficients in a moving-bed shell-and-plate heat exchanger with non-contact temperature measurements, International Journal of Heat and Mass Transfer, 242, 2025, 126819.
[13] Ben Hamida, M.B., Ali, A.B.M., Sawaran Singh, N.S., Mostafa, L., Optimization of MXene-based aqueous ionic liquids for solar systems using conventional and AI-based techniques, Scientific Reports, 15, 2025, 20565.
[14] Ham, J., Lee, G., Kwon, O., Bae, K., Cho, H., Numerical study on the flow maldistribution characteristics of a plate heat exchanger, Applied Thermal Engineering, 224, 2023, 120136.
[15] Gut, J.A.W., Pinto, J.M., Optimal configuration design for plate heat exchangers, International Journal of Heat and Mass Transfer, 47, 2004, 4833–4848.
[16] He, S., Wang, M., Forgione, N., Pucciarelli, A., Tian, W.X., Qiu, S.Z., Su, G.H., A multi-task Transformer-Mamba-Seq framework for real-time estimation of spatiotemporal thermal stratification in passive residual heat exchanger, International Communications in Heat and Mass Transfer, 169, 2025, 109868.
[17] Kanaris, A.G., Mouza, A.A., Paras, S.V., Optimal design of a plate heat exchanger with undulated surfaces, International Journal of Thermal Sciences, 48, 2009, 1184–1195.
[18] Elbarghthi, A.F.A., Dvorak, V., Al-Dailami, Z., Bediako, E.G., Wen, C., Multi-domain analysis and optimisation for plate heat exchangers: Integrating theory, Experiment, and CFD-based approaches, Energy Conversion and Management, 344, 2025, 120187.
[19] Mikhaeil, M., Gaderer, M., Dawoud, B., On the development of an innovative adsorber plate heat exchanger for adsorption heat transformation processes; an experimental and numerical study, Energy, 207, 2020, 118272.
[20] Zou, J., Hirokawa, T., An, J., Huang, L., Camm, J., Recent advances in the applications of machine learning methods for heat exchanger modeling—a review, Frontiers in Energy Research, 11, 2023.
[21] Yoladi, M., Akyurek, E.F., Kotcioglu, İ., Experimental investigation of cross-flow heat exchangers with helical fins: Performance analysis via RSM and ANN, International Journal of Thermal Sciences, 218, 2025, 110111.
[22] He, S., Ye, Y., Wang, M., Zhang, J., Tian, W., Qiu, S., Su, G.H., A machine learning and CFD based approach for fouling rapid prediction in shell-and-tube heat exchanger, Nuclear Engineering and Design, 432, 2025, 113759.
[23] Yang, Z., He, S., Yu, J., Wang, Q., Qiu, H., Wang, M., Tian, W., Su, G.H., A machine learning-based generative design approach for rapid topology optimization of microchannel heat sinks, International Communications in Heat and Mass Transfer, 169, 2025, 109655.
[24] Ahmed, F., Aziz, M.S.A., Shaik, F., Khor, C.Y., Optimization of a novel spray flash desalination system integrated with concentrated solar power utilizing response surface methodology, Desalination, 558, 2023, 116640.
[25] Sun, Y., Cai, L., Chen, Y., Wang, S., Optimization of a high through-flow design turbine using response surface method, Physics of Fluids, 36, 2024, 046106.
[26] He, Y., Rao, L., E, D., Lai, N.-C., Fang, J., Jiang, Z., Multi-response optimization for a waste heat recovery rotary drum with flights: Focusing on enhancement and homogenization of heat exchange, Chemical Engineering Research and Design, 218, 2025, 218–229.
[27] Mohammed, N., Palaniandy, P., Shaik, F., Deepanraj, B., Mewada, H., Statistical analysis by using soft computing methods for seawater biodegradability using ZnO photocatalyst, Environmental Research, 227, 2023, 115696.
[28] Xu, L., Wang, B., Zhang, Y., Wu, R., Liu, J., Li, X., Impact damage prediction of steel tube confined reinforced concrete columns based on a finite Element–machine learning model and transfer learning ideas, Journal of Building Engineering, 113, 2025, 114008.
[29] Liu, K., Lu, C., Chen, W., Chen, B., Dai, Y., A machine learning framework for predicting the fire resistance time of cold-formed steel walls, Journal of Building Engineering, 2025, 114206.
[30] Mihalakakou, G., Giannadakis, A., Malefaki, S., Souliotis, M., Georgiou, P., Romaios, A., Antzoulatou, A., Nikolakopoulos, P., Paravantis, J.A., Coupling simulation-based and machine learning methodologies for energy optimization and environmental impact mitigation in buildings, Journal of Building Engineering, 112, 2025, 113809.
[31] Zhou, S., Sun, Z., Li, W., Guo, J., Sun, D., Xu, L., Wu, K., A Hybrid Machine Learning Framework with GAN-Based Data Augmentation for Predicting Strain Properties of Fiber-Reinforced Repair Mortar, Journal of Building Engineering, 2025, 114140.
[32] Shehzad, A., Xiu-Xin, W., Xing-Huai, H., Ullah, K., Mohammad, A., Althobaiti, A., Flah, A., AI-driven seismic optimization of outrigger systems in high-rise buildings: A machine learning framework for enhanced performance in earthquake-prone regions, Journal of Building Engineering, 112, 2025, 113864.
[33] Baruah, A.C., Sychterz, A.C., Machine learning-based damage classification and comparative life cycle assessment of Origami Pill Bug for emergency shelters, Journal of Building Engineering, 113, 2025, 114051.
Journal of Applied and Computational Mechanics

Articles in Press, Corrected Proof
Available Online from 01 May 2026