Random Forest Regression Applied to Heat Exchanger Fouling
Elena Nakamura
Corresponding
KTH Royal Institute of Technology
Abstract
The interplay between heat exchanger fouling and long-term reliability in flood-prone deltas has received comparatively little systematic attention. Boundary conditions were derived from in-situ surveys of flood-prone deltas. Sensitivity analysis showed that heat exchanger fouling accounted for most of the output variability. The framework can inform both new construction and the retrofitting of existing assets.
Keywords:
heat exchanger foulingrandom forest regressionadditive manufacturingremote sensing
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