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Unwana-obong, N. U.
Department of Mechanical Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike, Nigeria
Ntunde, D. I.
Department of Mechanical Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike, Nigeria
Allen, M. A.
Department of Mechanical Engineering, College of Engineering and Engineering Technology, Michael Okpara University of Agriculture, Umudike, Nigeria
ABSTRACT
This study performed a comparative analysis of
the Response Surface Methodology (RSM) and the Artificial Neural Network (ANN)
models employed to predicts the thermo-hydraulic performance of a Gas Turbine
Heat Exchanger Unit 16 (HEU-16) at Transcorp Power Limited where the operating
parameters was obtained from Mark V Speed control system. The three independent
operating parameters were: oil inlet temperature, water inlet temperature, and
mass flow rate and the responses evaluated were heat transfer coefficient,
pressure drop, and thermal effectiveness of the heat exchanger system.
Experimental data obtained were analyzed using Design-Expert software for RSM
modeling, while ANN modeling was carried out using multilayer feed-forward,
back-propagation (B.P.) algorithms, Levenberg-Marquardt training algorithm was
the best at predicting the performance of the heat exchanger. The results
revealed that the operating parameters significantly affected the thermal and
hydraulic behavior of the exchanger. Increase in inlet temperatures and mass
flow rate enhanced the heat transfer coefficient and thermal effectiveness,
although pressure drop also increased correspondingly. The developed quadratic
RSM models were statistically significant with high coefficients of
determination, indicating good agreement between predicted and experimental
values. However, ANN demonstrated superior predictive accuracy with higher R²
values and lower mean square error compared to RSM, due to its enhanced
capability in handling nonlinear process relationships. The predicted results
established optimum operating conditions at an oil inlet temperature of 69.47
°C, water inlet temperature of 54.02 °C, and mass flow rate of 269.42 m3/s.
Under these conditions, the predicted heat transfer coefficient, pressure drop,
and thermal effectiveness were 691.08 W/m²·K, 1.88 kPa, and 0.718 respectively,
with a desirability value of 1.0. The study demonstrated that ANN and RSM are
reliable tools for thermal system prediction and performance enhancement in
industrial heat exchanger applications.
Keywords: Heat Exchanger, Pressure drops, Thermal coefficients, Prediction, Thermo-hydraulic
https://doi.org/10.33922/j.ujet_v12i2_4
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Published
Sunday, May 03, 2026
Issue
Vol. 12, No. 2, June 2026
Article Section
GENERAL
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