Advanced modeling and performance analysis of a photovoltaic-thermal double compression heat pump for industrial thermal applications
Bibliographic record
Abstract
This study focuses on the modeling and analysis of the performance of a Photovoltaic-Thermal (PVT) Double Compression Heat Pump system for industrial thermal applications. The main goal of the study is to assess the viability of the integrated system for high-temperature process heat. The performance of the PVT system under varying climatic conditions is assessed using MATLAB, while the double compression heat pump is simulated in Engineering Equation Solver (EES). Validation of the PVT model is achieved using an experimental setup and data obtained from London, Ontario. The heat pump model is compared to a multi-stage compression-throttle water-based high-temperature heat pump from previous studies, yielding a root mean square error (RMSE) of 0.4 for its Coefficient of Performance (COP). Under validated conditions, the heat pump achieves a COP of 3.0 at a 30 °C source and a 120 °C sink, demonstrating effective heat delivery. Sensitivity analysis using contour plots and regression techniques reveals that COP values of up to 7.0 are achievable at source temperatures of 45 °C–50 °C and sink temperatures of 90 °C–120 °C. Conversely, lower source temperatures and higher sink temperatures lead to decreased efficiency, with COP dropping below 3.0. Regression analysis further highlights the stronger impact of sink temperature on COP compared to source temperature, emphasizing the need to optimize sink conditions for improved performance. In conclusion, the study highlights the potential of the PVT–double compression heat pump system to integrate solar PV technology into industrial processes, enhance energy efficiency, and promote sustainable operations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".