Neural Network Model Identification and Advanced Control of a Membrane Biological Reactor
Bibliographic record
Abstract
System identification with different input-output structures, for a membrane biological reactor (MBR), was performed using artificial neural networks (ANN) black-box modeling. The ANN models were able to capture the dynamic flux experimental literature data. Sensitivity analyses were applied on the ANN models to quantify the effects of variation in the process inputs (backwash pressure, vacuum pressure, backwash and vacuum time) on the process output (flux rate. Sensitivity analysis was applied on the developed NN in order to find the optimum backwash scheduling. The maximum flux was attained at around 165 (L/m2·day) that corresponded to an optimum vacuum-to-backwash time ratio of 10 minutes vacuum to 2 minutes backwash. Advanced control strategy using neuro-model predictive control (NN-MPC) methodology was applied to control the MBR system. The NN-MPC parameters were tuned to attain an optimum performance. The NN-MPC was efficient in tracking the flux set-point changes by adjusting vacuum-to-backwash time ratio within the operation constraints.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".