Effect of carrier material on flow characteristics of date pulp feedstock
Bibliographic record
Abstract
Carriers are added with fruit pulp in several drying methods. The flow behavior of the feedstock is an important property in determining equipment design and drying condition. In this study, the effect of carrier type, carrier percentage, and concentration of date pulp feedstock (DPF) on the rheological properties were investigated. The DPF was prepared with Fardh dates cultivar with maltodextrin (MD), gum arabic (GA), and sodium caseinate (SC) carriers at two levels (dates:carrier −70:30 and 60:40, dry weight basis) and three concentrations (20, 30, and 40%). The carriers were found to be effective in reduction of the shear stress for the shear rate range 1–100 s−1. At shear rate 1 s−1, GA-blended DPF and at shear rate 100 s−1, MD-blended DPF offered the lowest shear stresses. Viscosity was decreasing while increasing the shear rate from 1 to 100 s−1, and decreasing the concentration from 20 to 40%. MD- and GA carrier-blended DPFs had exhibited similar viscous behavior. Shear-thinning behavior with GA- and MD-blended DPFs and shear-thickening behavior with SC-blended DPF were observed. Yield stresses of Herschel–Bulkley, Casson and Bingham were increasing with increase in concentration. Temperature dependency of the viscosity was found higher in control than in the carrier-blended DPF. The activation energy was calculated using Arrhenius relationship between temperature and viscosity while heating as well as cooling. The activation energy was increasing while increasing the DPF concentration during heating and reciprocal results were found during cooling. No significant influence of carriers or carrier levels (p < 0.05) was found on activation energy during heating. But while cooling, the activation energy was found higher for the MD-blended DPF than other two DPFs.
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 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 | 0.001 |
| 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".