Performance Evaluation of a Photovoltaic-Thermal Collector Coupled Stepped Solar Still for Indian Climatic Conditions
Bibliographic record
Abstract
Freshwater scarcity is increasing across many parts of the globe; to meet this demand, seawater desalination is the best choice, and the electrical energy consumption is escalating due to urbanization and industrialization. Sustainable production of electricity and freshwater can be met by an integrating photovoltaic-thermal (PVT) module with stepped solar still (SSS). The present study focuses on the theoretical modeling of the PVT-SSS desalination system for evaluating thermal efficiency, energy efficiency, freshwater productivity, and electrical power generation. The solar still productivity will be influenced by the depth of water, insulation thickness, glass cover material, thickness and inclination, and operational factors like preheating the input water supply and water salinity. A comparative analysis has been made of summer, winter, and rainy climatic conditions of Vellore town (12.9165° N, 79.1325° E), Tamil Nadu. In the present work, a thermodynamic model based on mass and energy balance is developed for the PVT-SSS system, and it is solved by a numerical method. A Runge-Kutta technique of 4th order is employed using a Python program for solving the thermodynamic simulation model. The results from the model depict that for summer, winter, and rainy climatic seasons, the freshwater productivity of PV/T-SSS was determined to be 12.18 kg/m2day, 6.67 kg/m2day, and 2.77 kg/m2day. Also, it is found that electrical efficiency for summer, winter, and rainy seasons is 8.91%, 9.135%, and 9.53%, respectively. A maximum and minimum freshwater production of 1668 kg/m2 and 1218 kg/m2 are observed for a depth of 2 cm and 5 cm, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".