Assessment of mixing quality for an industrial pulp mixer using electrical resistance tomography
Bibliographic record
Abstract
Abstract The quality of mixing of a pulp suspension and chlorine dioxide by a static mixer in an industrial chlorine dioxide bleaching stage was evaluated using electrical resistance tomography (ERT) as a function of process operating conditions, including chemical flow rate, suspension flow rate, and suspension mass concentration. The uniformity was quantified by a mixing index based on the coefficient of variation of the individual conductivity values in each image pixel. An increase in the mixing index, indicating lower mixing quality, was observed when the chemical flow rate increased. In addition, the mixing quality decreased with a decrease in suspension flow rate. On the other hand, a decrease in the suspension mass concentration at a constant volumetric suspension flow rate gave better mixing quality. The results show that ERT can be used to evaluate industrial‐scale mixer performance and to monitor the changes in the mixing quality as a function of process operating conditions. The results are in good agreement with those in the literature based on other measurement techniques for similar mixer installations.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".