Thin Layer Drying and Modelling of Poultry Litter Briquettes
Bibliographic record
Abstract
Drying is an energy consuming process influenced by humidity, air velocity and temperature and is defined as a heat conveyance process wherein the product is heated hence removing moisture. Thin layer drying equations are used to estimate drying times of products and generalizing their drying curves. In this study, mathematical modelling and prediction of drying behavior of poultry litter briquettes (PLB) was investigated through open sun drying (OSD) and solar tunnel drying for moisture content (MC) calculations. A solar tunnel dryer (STD) having a: black painted collector unit, drying unit and black painted vertical bare flat-plate chimney was used. MC results were converted to moisture ratio and fitted into 12 different thin layer drying models, using Microsoft Office Excel, which were compared according to their coefficients of determination to estimate drying curves of PLB. The most accurate model was selected based on three statistical parameters: correlation coefficient (R2), chi-squared (χ2) and Root Mean Square Error (RMSE). Solar insolation of between 220 and 1005 W/m2 resulted in air temperature of up to 64oC at the collector unit, up to 60oC at the drying unit and an ambient temperature of up to 31oC. Exposure of PLB with an average initial MC of 61% (w.b.) to these conditions resulted in a final MC in a range of 0.2-11.2% (w.b.) in 31-55 hours. PLB was dried to similar final weight from whichever drying method although OSD took longer than STD. The Logarithmic model was found to satisfactorily describe the drying curves of PLB with R2 of 9.93E-01-9.99E-01; χ2 of 1.36E-11-6.50E-14; and RMSE of 2.94E-02-1.30E-02.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".