Machine learning approach for predicting refrigerant two-phase pressure drop inside Brazed Plate Heat Exchangers (BPHE)

This paper presents a Gradient Boosting Machines (GBM) model for predicting refrigerant two-phase frictional pressure gradient inside Brazed Plate Heat Exchangers (BPHE) based on an extensive database that includes 1624 boiling data-points, 925 condensation data-points, 16 different plate geometries, and 16 different refrigerants (including 4 natural refrigerants and 6 other low-GWP refrigerants). The model accounts for the effect of plate geometry, operating conditions and refrigerant properties. The model is able to reproduce the whole database with a Mean Absolute Percentage Error (MAPE) of 6.6%. The GBM model exhibits a better predictive performance than the state-of-the-art analytical-computational procedures for two-phase pressure drop inside BPHE available in the open literature. The characteristic parameters of the GBM model are thoroughly reported in the paper.