Optimal Operation of the Multi-reservoir Rainwater Harvesting System for Hydropower Generation
Bibliographic record
Abstract
Reservoir operations for single and multi-reservoirs of rainwater harvesting systems has been tested to address the deficit in supplying water and electric power for remote rural communities of semi-arid region of AL-Khoser watershed, Iraq.The main basin was divided into four sub-basins 1B, 2B, 3B and 4B.The Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) was applied to estimate water volumes of the above proposed reservoirs.To optimize reservoir operations for the objective function of maximized total annual hydropower generation, a technique was used to convert non-linear to linear problems.In which a linear approximation of the non-linear power production term can be expressed linearly by summing the vectors, the release and storage, where the relationship between the head and the storage is directly proportional.Total annual hydropower generation is maximized by optimized sustainable operation policies for single and multi-reservoirs.Dry, average, and wet rainfall seasons were selected for 1985-2020.The annual harvested water in the reservoirs of Main Basin, 1B, 2B, 3B and 4B ranged between: 0.7790-4.1788,2.1256-11.4010,and 5.10158-28.1985MCM for the three seasons.The capacity of hydropower generation was 82.59, 436.75 and 1034.70Kw for the three seasons.The increase in hydropower generation is achieved with multi-reservoirs operations by 110%, 66% and 41% respectively.The importance of hydropower generation increase is demonstrated by providing hydropower to additional families in the rural community as their primary source of power in addition to an increase in the irrigated areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".