Single feed droplet–catalyst particle collision in a liquid containing gas–solid fluidized bed to convert fructose to value-added chemicals
Bibliographic record
Abstract
Carbohydrates, comprising C6 sugars, dehydrate to form furfural (FUR) and 5- hydroxymethyl furfural (HMF). HMF is an intermediate for value-added specialty chemicals like 2,5-diformyl furan (DFF) and 2,5-furandi carboxylic acid, a monomer for polyethylene furaonate. Atomizing sugar solutions into catalytic fluidized beds operating beyond the caramelization temperature, T carm , accelerates reaction rates while avoiding humins, which are color forming agents characteristic of liquid phase processes. However, droplets partially coat particles in the spray zone and agglomerate due to liquid cohesive forces, which reduces heat transfer rates at the micro-scale and degrades reaction rates at the macro-scale. Here, we developed a CFD model to study the collision between a single feed droplet and a catalytic particle above T carm . We focused on evaluating the evaporation rate and heat transfer between the droplet and particle to promote vaporization the liquid feed, and assessing the impact of bed temperature, liquid feed rate, and superficial gas velocity. The highest DFF and furfural selectivity obtained are 17 % and 24 %, respectively, and these values are correlated to the maximum coke formation and agglomeration of 0.6 % and 12 g. However, the corresponding heat transfer and mass transfer are among the lowest values of 40 % and 0.5 W. • A CFD model simulated droplet–particle collisions dynamic in a hot environment. • Increasing N We , reduces tension force domination (droplets wrap particles entirely). • Higher Weber number increases dynamic, heat transfer and vaporization. • The Leidenfrost effect drops the heat transfer and evaporation in 0.1 ms. • Higher heat transfer/vaporization, less product selectivity/coke formation/agglomeration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".