Analytical solutions of reaction–diffusion phenomena by porous cylindrical pellets with finite length
Bibliographic record
Abstract
Abstract Analytical solutions were derived to predict transient change of intra‐particle concentration inside cylindrical pellets with finite length immersed in an infinitely large medium. After the derivation of steady‐state concentration by eigenfunction expansion, a transient solution was obtained by the separation of variables. Effects of the Thiele modulus ( Φ ) and aspect ratio of the pellet ( H / R ) were studied by calculating the average intra‐particle concentration at a stationary state. The effectiveness factor ( η ) was also affected by H / R and Φ , since irreversible first order kinetics was assumed. As Biot number (Bi) increased, both intra‐particle concentration and η increased due to the decrease of the mass transfer resistance of the surrounding film. Good agreement between analytical and numerical solutions was confirmed for both steady‐state and transient solution by comparing the analytical results with the numerical solutions by finite element method. To extend the results to a batch reactor with finite volume, Duhamel's theorem was applied by assuming time‐dependent boundary conditions to reflect the change in bulk concentration as a function of time. A method was proposed to measure intra‐particle diffusivity for batch adsorber by setting Φ = 0. To investigate the effect of H / R , η could be included in material balance of batch, continuously stirred tank reactor (CSTR), and fixed bed reactors to predict reactor performance assuming a pseudo‐steady state. First‐order reaction by egg‐shell catalysts as well as nonlinear reaction with two reactants could be solved numerically assuming finite cylindrical pellets. By solving coupled differential equations of material and energy balances, dynamic catalysis was confirmed from the enhancement of conversion, assuming forced oscillation of inlet temperature in CSTR.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".