Energy saving and management of water pumping networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main consumption of energy in water systems is the pumps. Due to the different tariff of energy consumption during the one day, the operation of these pumps should be controlled to minimize their consumption and consequently decrease the cost of operation. This paper utilizes an optimization algorithm to control the on/off operation of water pumps to minimize the cost of energy consumption and number of pump switching of water networks. This objective function is subjected to some optimization and hydraulic constraints such as the tanks upper and lower limits, and water network pressure limit. The proposed methodology is an iterative combination process between an optimization algorithm and EPANet hydraulic simulator where optimization algorithm generates the schedules and the hydraulic simulator is used to check the feasibility of these schedules. The suggested optimization method is the artificial electric field algorithm (AEFA). This methodology is applied to three water networks; EPANet practical example network, Richmond network and a part from Toronto network with a variable energy consumption tariff. The AEFA is tested and trained to select the best values of its controlling parameters for each network. The results show that the energy consumption cost is significantly decreased by the optimal schedules of water pumps. Also AEFA is compared with other optimization algorithms such as the genetic and particle swarm algorithms on the same networks and energy tariff and the results show the superiority of AEFA in the convergence and saving of the cost of energy consumption.
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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 it