Evaluation of Energy Efficiency in a Grain Unloading Platform
Bibliographic record
Abstract
This paper aims to conduct an energy efficiency study in the tipping system of the unloading platform of a grain storage unit, based on the use of electrical devices to control the operation of the engine. For this purpose, two scenarios were established. The first scenario considers the platform in its current state, with engine start-up performed using a wye-delta switch. In the second scenario, wye-delta start is switched off, and a frequency inverter and a programmable logic controller (PLC) are coupled to control the electric motor. For both scenarios, the consumption of active and reactive energy in the discharge platform was measured, and the costs of the energy consumed were calculated over a period of one year. Finally, the discounted payback was calculated to evaluate the economic feasibility of installing the proposed equipment. The results obtained were as follows: For the current scenario, the energy consumed to tipping one metric ton of grain was 35.44.10-3 kWh; for the proposed scenario with the frequency inverter, the energy was 32.78.10-3 kWh. With an annual projection, the current scenario would consume the equivalent of 10 921.35 kWh, and the proposed scenario, 10 100.81 kWh, generating an annual savings, with the installation of the equipment, of R$721.49; the discounted payback found was approximately 31 years. It can thus be concluded that there is a reduction in electricity consumption from the use of the frequency inverter; however, the time of return of the invested capital is very long, making the proposal economically unfeasible.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".