Bibliographic record
Abstract
High-recirculation airlift reactors (HRARs) are efficient for treating waste water. They use air to push a mixture around a reactor and to provide oxygen (O2) for biological microorganisms. Design methods have been limited in functionality and accuracy and have needed significant expert input and interpretation. This paper describes the creation of new structured methods that are faster and more efficient. Models and calculations are described. Improvements are made by analysing and improving the steady-state models of fluid dynamics within an HRAR. The models also deliver information about reactor design, in particular which parameters to modify to reach a steady-state result. Two-phase flow of water and air is modelled for an airlift bioreactor and applied to HRARs. Tests show that varying superficial gas velocity or simultaneously varying down comer and riser diameters can create a steady-state solution. The research investigated an HRAR and the associated Imperial Chemical Industries design program, created a new design program to replace it and then improved it using simple models of steady-state fluid dynamics. Mathematical models are used to forecast steady-state situations in the HRAR for specific gas or liquid flow rates and for various constructions. Experimental relationships forecast mass transfers between gas and liquid phases, and they predict flow.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".