Optimization of the effective volume of urban sewage pumping station based on the fuzzy optimization method
Bibliographic record
Abstract
ABSTRACT To improve the operational economy of sewage pumping stations, this paper combines the total volume of the drainage network and sewage pumping station to optimize the effective volume of the pumping station reservoir to reduce operating costs without overflow. Taking a residential area in Ningbo city as the study area, the drainage system was simulated based on Infoworks ICM to obtain the changes of sewage under different volumes of pumping station reservoirs. By combining the daily electricity costs of the pump station, pump start-stop times, and flood depth, the fuzzy optimization method was applied to optimize the effective volume of sewage pumping station reservoirs, selecting the optimal solution to reduce operating costs. A sensitivity analysis of the three factors, pump flow rate, the total population, and drainage network volume, was conducted, and the results showed that under the optimal scenario, the optimal effective volume of the pumping station reservoir increases with the increase in pump flow rate, drainage network volume, and total population. This study can provide theoretical support for the optimization of drainage pumping station reservoir volume as well as economical operation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".