Volume 3 · Issue 4 (2026)
10.66521/2938-9933-2026042701
Predicting Working Fluid Pump Frequency for Outlet Temperature Regulation in Parabolic Trough Solar Thermal Fields Based on Multiple Nonlinear Regression
Hongxia Yan*, Jialan Sun
Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, College of Mechanical & Energy Engineering, Beijing University of Technology, Beijing 100124, China
Corresponding Author: Hongxia Yan (y.hx123@163.com)
Abstract: This paper presents a data-driven model to predict working fluid pump frequency for stable outlet temperature control in parabolic trough solar thermal fields, addressing challenges like multi-factor coupling and strong nonlinearity. Using operational data from an experimental platform, five key features (molten salt flow rate, inlet/outlet temperatures, absorbed thermal power, and direct solar radiation) are extracted after cleaning and imputation. Spearman correlation eliminates collinear variables. A multiple nonlinear regression model incorporating polynomial and logarithmic terms significantly outperforms a linear baseline (R²=0.779 vs. 0.245). Although the model is primarily trained on clear-sky midday data with limited DNI variation, it achieves a MAPE of 12.1% on low-irradiance samples (<900 W/m²), indicating reasonable robustness. The model supports pump frequency pre-determination for given outlet temperature setpoints and sensor redundancy verification, enhancing control system reliability.
Keywords: Parabolic trough solar thermal field; Working fluid pump frequency prediction; Multiple nonlinear regression; Data-driven modeling; Feature selection
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