Pumps: energy efficiency & performance indicators
Bibliographic record
Abstract
Pumping is a central component to many water supply and distribution systems, and one which consumes significant amounts of energy. Increased attention to energy conservation is a common theme globally and, in the context of water supply systems, the need to understand the energy efficiency with which pumps operate in situ, and the opportunity to improve upon any inefficiencies, is becoming increasingly recognized. This paper discusses two separate and independently conceived and delivered initiatives that, while taking very different approaches to raising awareness and improving the industry's state of practice in this regard, are rather synergistic when viewed in a holistic sense. Recent work in Mexico is engaging the numerous utilities across the country to begin the measurement of pump energy efficiency, having wide-reaching impact, while work in Canada is exploring the details of individual pump performance through accurate field testing. Both these initiatives use a common approach to measuring performance of pump efficiency, based on the normalization of energy consumption relative to the output of the pump, namely the flow and total dynamic head delivered. The exact performance indicators used are somewhat different, but very closely related, and this paper explores the nuances of these differences in detail. As well, results from both the Mexican and Canadian experiences are presented, and guidance on the use of the performance indicators is provided.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".