Pulsation Assisted Fluidized Bed for Potash Drying to Eliminate Agglomeration and Enhance Energy and Exergy Efficiency
Bibliographic record
Abstract
Bubbling fluidized beds are widely used for drying solid materials and agricultural products. Accurate prediction of bubbling behavior in fluidized beds has been a challenge due to the higher degree of bubble coalescence and break up and high probability of forming slugging regime, and partial fluidization. Among different techniques, electrical capacitance tomography (ECT) has been deemed as one of the most appropriate tools for imaging fluidization and bubbling behavior in fluidized beds due to non-invasive and in line measurement nature. Average bubble velocity, average bubble size, and bubble frequency in both bubbling and slugging regimes were measured and compared at two heights of potash bed using a twin-plane ECT system. The experimental data for bubble diameter and bubble rise velocity were validated by the model of Agu et al. with average absolute deviation (AAD) of 25% and 7% for the bed height of 49 cm, and 13% and 17% for the bed height of 53 cm, respectively. In addition to hydrodynamic investigations of dry potash, strong cohesiveness of wet potash particles during the drying process poses significant challenges to fluidization of such particles due to formation of aggregates. In order to improve fluidization behavior, pulsed airflow was employed to break agglomeration and eliminate channeling in a fluidized bed. The effects of pulsation frequency, pulsed air to steady flow ratio (r), and relative humidity of the inlet air on minimum fluidization velocity and bubbling behavior were investigated. A frequency of 1.0 Hz and an r of 0.33 with 100 ms opening time were found to be the best operating condition in our system, that leads to the lowest minimum fluidization velocity and generation of more homogeneous bubbles in size and shape. A new theoretical model was developed to predict the minimum fluidization velocity (umf) of wet particles in a pulsation-assisted fluidized bed by considering both the liquid bridge force and resonant force resulting from the pulsation. The average deviation percentage values between the experimental data of umf and the new developed model were 13.8 and 20.7 for dry and wet potash particles, respectively. Furthermore, the effect of different operating conditions including inlet drying gas temperature (40°C, 50°C, and 60°C) and pulsation frequency (1.0 Hz and 2.0 Hz) on the energy and exergy efficiency of drying potash particles was investigated. The results showed that the highest energy and exergy efficiency of potash drying, 28.6% and 27.8%, respectively, was achieved when the fluidized drying of potash particles was performed at T = 40°C and f = 1.0 Hz. A drying model based on the thin-layer theory was employed to fit the experimental data of drying of potash particles. The Midilli and Kucuk model provided the best agreement between the experimental data and the predicted values at both the constant rate and falling rate period of potash drying. Finally, synchrotron-based X-ray tomography 3D-imaging technique was for the first time employed to investigate the solid bridge formation between potash particles, quantitatively. The results showed that by increasing the moisture content of particles (3% to 5%), solid bridge length between potash particles was enlarged from 28 μm to 44 μm due to saturation of particles surface with KCl and higher recrystallization growth. This phenomenon led to a decrease in the external porosity of potash particles at the end of drying process from 25.3% to 19.5% for 3% and 5% moisture content, respectively.
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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 | 0.000 | 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) | 0.001 | 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".