Study of gas–liquid mixing in stirred vessel using electrical resistance tomography
Bibliographic record
Abstract
Abstract This study presents a full operation and optimization of a mixing unit; an innovative approach is developed to address the behaviour of gas–liquid mixing by using electrical resistance tomography. The validity of the method is investigated by developing the tomographic images using different numbers of baffles in a mixing unit. This technique provided clear visual evidence of better mixing that took place inside the gas−liquid system and the effect of a different number of baffles on mixing characteristics. For optimum gas flow rate (m3/s) and power input (kW), the oxygen absorption rate in water was measured. Dynamic gassing‐out method was applied for five different gas flow rates and four different power inputs to find out mass transfer coefficient (KLa). The rest of the experiments with one up to four baffles were carried out at these optimum values of power input (2.0 kW) and gas flow rate (8.5 × 10−4 m3/s). The experimental results and tomography visualizations showed that the gas−liquid mixing with standard baffling provided near the optimal process performance and good mechanical stability, as higher mass transfer rates were obtained using a greater number of baffles. The addition of single baffle had a striking effect on mixing efficiency, and additions of further baffles significantly decrease mixing time. The energy required for complete mixing was remarkably reduced in the case of four baffles as compared with without any baffle. The process economics study showed that the increased cost of baffle installation accounts for less cost of energy input for agitation. The process economics have also revealed that the optimum numbers of baffles are four in the present mixing unit, and the use of an optimum number of baffles reduced the energy input cost by 54%. © 2016 Curtin University of Technology and John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".