A two‐phase partitioning airlift bioreactor for the treatment of BTEX contaminated gases
Bibliographic record
Abstract
This investigation characterizes a novel 11 L airlift two-phase partitioning bioreactor (TPPB) for the treatment of gases contaminated with a mixture of benzene, toluene, ethylbenzene, and o-xylene (BTEX). The application of the TPPB technology in an airlift bioreactor configuration provides a novel technology that reduces energy intensity relative to traditional stirred tank TPPB configurations. The addition of a solid second phase of silicone rubber beads (10%, v/v) or of a liquid second phase of silicone oil (10%, v/v) resulted in enhanced performance of the airlift bioreactor relative to the single phase case, with 20% more BTEX being removed from the gas phase during an imposed transient loading. During a 4 h loading step change of three times the nominal loading (60 g m(-3) h(-1)), overall removal efficiencies for the airlift TPPBs containing a liquid or solid phase remained above 75%, whereas the single phase airlift had an overall removal efficiency of 47.1%. The airlift TPPB containing a silicone rubber second phase was further characterized by testing performance during steady-state operation over a range of loadings and inlet gas flow rates in the form of a 3(2) factorial experimental design. Optimal operating conditions that avoid oxygen limitations and that still have a slow enough gas flow rate for sufficient BTEX transfer from the gas phase to the working volume are identified. The novel solid-liquid airlift TPPB reduces energy inputs relative to stirred tank designs while being able to eliminate large amounts of BTEX during both steady-state and fluctuating loading conditions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".