Using domestic drain water as affordable and clean energy: predictive modeling and sensitivity analysis of a horizontal heat exchanger

Beata Piotrowska, Sabina Kordana-Obuch and Mariusz Starzec

Energy, 2025, vol. 341, issue C

Abstract: In this study, the efficiency of a horizontal heat exchanger (HHE) for drain water heat recovery was analyzed. The eXtreme Gradient Boosting (XGB) algorithm was employed to predict the system parameters, and the models were evaluated using the R2, RMSE, and MAE metrics. Sensitivity analysis was performed using the SHapley Additive exPlanations (SHAP) method to determine the influence of input variables, including mixed water flow rate (q), hot water temperature (Thw), cold water temperature (Tcw), drain water temperature (Tdw), and linear bottom slope of the heat exchanger (s) on key output variables: heat transfer rate (P), percentage of recovered heat (R), heat exchanger effectiveness (ε) and the number of heat transfer units (NTU). The results indicate that the most significant factors influencing heat recovery are the parameters q and s, especially in Configuration A (balanced flow). In Configurations B and C (unbalanced flow), the significance of temperature-related variables increases. These findings highlight the importance of selecting the optimal HHE configuration and demonstrate how advanced machine learning models can support the effective design and operation of horizontal heat exchangers.

Keywords: Horizontal heat exchanger; Waste heat recovery; Machine learning; Sensitivity analysis; Clean energy (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:341:y:2025:i:c:s0360544225049680

DOI: 10.1016/j.energy.2025.139326

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