Transfer Learning Applied to Heat Exchanger Fouling
Chinedu Silva
King Fahd University of Petroleum and Minerals
Niklas Mensah
Delft University of Technology
Abstract
The rapid expansion of hydrogen fuel cells across South Asia has intensified the need to characterize heat exchanger fouling under realistic operating conditions. The experimental programme was designed to isolate the dominant mechanisms observed in hydrogen fuel cells. Removal efficiency exceeded 55% under the optimized operating conditions. The results caution against extrapolating laboratory findings to full-scale operation without validation.
Keywords:
heat exchanger foulingtransfer learningmaximum power point trackingfederated learning
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