Temperature-Based Control of an Anaerobic Reactor Using a Multi-Model Observer-Based Estimator
Bibliographic record
Abstract
This study presents a temperature-based control strategy for the stabilization of an anaerobic reactor during organic overloads. To prove feasibility of the proposed approach the rate of methane production was followed in batch activity tests and reactor runs during mesophilic-thermophilic transitions. Within the first 0.25-6 h of temperature augmentation, an increase in the rate of methane production was observed with higher rates measured under thermophilic (above 40 degrees C) conditions. However, 24 h after startup both in batch tests and reactor runs, the rate of methane production under thermophilic conditions was inferior to that under optimal mesophilic conditions (35 degrees C). Following these results, a control strategy based on short-term augmentation of the reactor temperature was proposed and tested in a 10 L UASB reactor. The control strategy employed a multi-model observer-based estimator to stabilize the effluent COD concentration during organic overloads. The temperature-based control resulted in an increased methanization rate and improved reactor stability overall.
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) | 0.000 | 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) | 0.000 | 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 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".