Model for a solid‐liquid airlift two‐phase partitioning bioscrubber for the treatment of BTEX
Bibliographic record
Abstract
Abstract BACKGROUND: Airlift solid–liquid two‐phase partitioning bioreactors (SL‐TPPBs) have been shown to be effective for the treatment of gas streams containing benzene, toluene, ethylbenzene and o‐xylene (BTEX). The airlift SL‐TPPB is a low‐energy system that utilizes a sequestering phase of solid silicone rubber beads (10%v/v) that will uptake and release large amounts of BTEX in order to maintain equilibrium conditions within the system. This increases mass transfer from the gas phase during dynamic loading periods and improves degradation performance. This study discusses the development and analysis of a steady‐state, tanks‐in‐series mathematical model, arising from mass balances on BTEX and oxygen in the gas, aqueous and polymer phases to predict the performance of the airlift SL‐TPPB over various gas flow rates and BTEX loadings. RESULTS: An estimability analysis on model parameters determined that the parameters to which model output is most sensitive are those that affect biological activity, which were targeted for estimation. The developed tanks‐in‐series model was able to predict the removal of BTEX components and dissolved oxygen concentrations over various inlet loadings (20, 60 and 100 mg L −1 h −1 ) and gas flow rates (2,3 and 4 L min −1 ) that resulted in a range of system performance from effective BTEX treatment to oxygen limiting conditions. CONCLUSIONS: The model developed, with estimated parameters, provides a valuable tool to determine operating conditions that will result in favourable performance of the airlift SL‐TPPB. Copyright © 2009 Society of Chemical Industry
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".