MIXING PATTERNS AND RESIDENCE TIME DETERMINATION IN A BUBBLING FLUIDIZED BED SYSTEM
Bibliographic record
Abstract
Fluidized bed gasification can be used to convert the solar energy stored as carboneous compounds in biomass into a carbon neutral fuel with reduced emissions. Canada produces 20.57 million tonnes of wheat straw annually which could be used for green energy production. Wheat straw residue gasification has the capability of replacing 7.5 % (0.62 EJ) of Canada's annual fossil fuel consumption. To achieve efficient gasification in a fluidized bed proper fuel mixing and residence time must be achieved. The aim of this study was to investigate the effects of sand particle size, distributor plate shape and angle, bed height and fluidizing velocity on particle mixing and residence time in the fluidized bed reactor. Greater values of the residence time were obtained with course sand whereas lower values were obtained with fine sand. An increase in the angle of convex or a decrease in the angle of a concave of the distributor plate resulted in an increase in the residence time. Both the concave and convex distributor plates achieved vertical upward and downward movements of the bed material resulting in continuous bed material turnover and, thus, good mixing. However, the concave plate achieved longer residence time which will result in better conversion efficiency. To improve the mixing properties of the binary mixture, which has great tendency for segregation due to density differences, an angled distributor plate (concave or convex) should be used. Considerable increases in the residence time were recorded with increases in the bed height. Increasing the fluidizing velocity decreased the residence time due to the increase in the bubble velocity. However, since the conversion efficiency is affected by the degree of mixing, it will also be improved by increasing the fluidization velocity. A velocity above 1.50 U mf is recommended for better fluidization and improved mixing.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".