Using estimation to optimize electric pump flow rates for spraying applications
Bibliographic record
Abstract
Electric pumps are widely used in applications such as sanitation, manufacturing and agriculture. Electric current is supplied to the pumps, which translates into a corresponding flow rate and therefore output pressure. This relationship between a pump’s pressure and flow rate is described as its performance curve. This conference paper uses estimation theory and cognitive system techniques to improve the efficiency of electric pumps. Specifically, using the perception-action cycle to observe the states, predict the system behaviour and then optimize it. The system states are estimated using sensor measurements and system dynamics, where the control system uses the states to find the optimal flow rate based on the performance curve and adjust the system accordingly. This methodology is validated using simulations. The simulation models a sprayer that is powered by a DC motor where the ideal spray angle is maintained based on the distance to the surface. Optimizing the electric pump performance, reduces energy consumption and optimizes fluid usage, which can provide savings in many industries and systems.
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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".