A Feasibility Study on the Benefits of Feedback Aerator Control in Precision Aquaculture Applications for the Developing World
Bibliographic record
Abstract
Abstract Aquaculture is a growing source of food and income for many in the developing world. In developing countries, where more than 18 million people engage in aquaculture, yields have been low due to lacking infrastructure. Aeration has been shown to improve dissolved oxygen (DO) and increase yields, but its use has been low in many developing world environments due to high operating costs. Even when used, they are operated in an ad-hoc manner, resulting in higher than required costs. A potentially more effective implementation is the use of feedback control to maintain adequate DO and increase energy savings. To demonstrate the potential, a feasibility study was conducted comparing the energy consumption of a diffused aeration system, with and without the use of a feedback control system. The effect of the diffused aeration system was simulated for a 100 m3 pond in Bangladesh for extensive and intensive fish farming. The interaction between the aerators and the pond was simulated on ANSYS FLUENT and was used with a DO model to predict the oxygen dynamics of the pond. Results indicated that the addition of a feedback control system could result in 78.66%, and 52.48% in energy cost savings compared to continuous operation for extensive and intensive fish farming respectively. Further work in smart instrumentation has the potential to decrease the energy requirements of aeration technologies and improve production for farmers in the developing world.
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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) | 0.000 | 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) | 0.002 | 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".