Integration of process design and control of a pilot-scale recirculating aquaculture system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work presents an optimization formulation to integrate design and control for Recirculating Aquaculture Systems (RAS). The key is to find a feasible and dynamically operable RAS with the optimal equipment sizing, control strategies, and batch time that maximizes the annual profit. Fish welfare was explicitly considered by enforcing limits on toxic components and taking into consideration the effects of water quality on the dynamic fish growth and mortality rates. A dynamic optimization control strategy was employed to ensure an optimal rearing environment. A pilot-scale rainbow trout RAS farm was selected as our case study . The proposed simultaneous design and control scheme was able to significantly enhance RAS profitability by running shorter batches in larger fish tanks with optimal control actions. Temperature effects and a disturbance scenario involving the feeding rate were investigated to gain further insights and advance the adoption of these emerging systems in aquaculture.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it