Using tomography and computational fluid dynamics to study the performance of a maxblend impeller for solid-liquid mixing operations
Bibliographic record
Abstract
A thorough literature review suggests that no comprehensive research work has been done regarding the characterization of the local solid concentrations in a slurry reactor equipped with a Maxblend impeller. The aim of this research work was to assess the mixing performance of a Maxblend impeller in a slurry reactor through electrical resistance tomography (ERT) and computational fluid dynamics (CFD). The mixing efficiency of the Maxblend impeller for solid-liquid mixing operation was compared to those measured for the A200 (an axial-flow impeller) and the Rushton turbine (a radial-flow impeller). The tomography images were employed to assess the particles distribution inside the slurry reactor. The CFD model was created using the Eulerian and Eulerian (E-E) method, standard k-ε turbulence model, and sliding mesh (SM) technique for simulating the two-phase fluid flow, turbulence effects, and stirrer rotation, respectively. The validated CFD model was utilized to obtain the particle concentration profiles and to determine the local particle distributions attained by the Maxblend impeller. The data were utilized to analyze the impacts of various important parameters such as the agitation speed, particle concentration, particle diameter, specific gravity of the particle, and the use of baffles on the mixing efficiency of the Maxblend impeller in terms of the extent of homogeneity and mixing index. The particle distribution in the slurry reactor furnished with a Maxblend impeller was also assessed through clouding height and just suspended agitation speed approaches in this study. The results from this study showed that the assessment of the optimum impeller speed is extremely important to enhance the local mixing quality in the mixing vessel. Experimental tests demonstrated that maximum homogeneity attained by the Maxblend impeller was higher than those for the A200 and Rushton impellers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".