Pulsation Assisted Fluidized Bed for Potash Drying to Eliminate Agglomeration and Enhance Energy and Exergy Efficiency
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».