Economic Model Predictive Control of a Recirculating Aquaculture System
Bibliographic record
Abstract
The recirculating aquaculture systems (RAS) has been proposed to reduce the water consumption of commercial aquaculture. RAS removes organic and particulate waste from fish tank water while also aerating; as such the treated water can be recycled back into the tanks. In this work, we treat the RAS as a batch process such that economic model predictive control (EMPC) can be applied using a mechanistic process model. The EMPC, which considers fish production profits as well as material utility and electricity costs for RAS, is deployed for various water temperatures such that its effect on the fish growth and economics are quantified. Moreover, batch length is also determined through tracking of the process profit trajectory. The results show that the EMPC-operated RAS can substantially increase the fish sales price with time-varying control decisions. Moreover, the EMPC is shown to adjust its operating policy mid-batch to accommodate for temperature disturbances.
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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".