Toluene Removal in an Automated Cyclical Bioreactor
Bibliographic record
Abstract
A control scheme was developed for the automation of toluene removal in a cyclical bioreactor. Toluene was added to the self-cycling fermentor by diffusion across a silicone membrane. Transient dissolved oxygen, carbon dioxide evolution, and oxidation-reduction potential (ORP) were screened as potential control variables. Through experimentation, ORP was deemed most effective. Control algorithms based on real-time estimates of the first and second derivatives of the ORP signal were tested. Although both approaches resulted in stable operation of the reactor, average toluene removal efficiencies of 95% were realized when control was based on the second derivative. This was significantly higher than the 77% efficiencies obtained when the control scheme centered on the first derivative of the transient ORP signal. The system developed was self-regulating, ensuring that a high toluene removal rate, on the order of 1.1 g h(-1), was maintained from cycle to cycle.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".