Analysis of the Heat Transfer Coefficient, Thermal Effusivity and Mathematical Modelling of Drying Kinetics of a Partitioned Single Pass Low-Cost Solar Drying of Cocoyam Chips with Economic Assessments
Bibliographic record
Abstract
This study examines the heat and mass transfer coefficient, thermal effusivity, and other thermal properties of solar-dried cocoyam chips, as well as the drying kinetics. The research also assessed the economics of the solar dryer. For these reasons, a solar dryer with a partitioned collector was developed that creates a double airflow travel distance to delay the airflow inside the collector. The partitioning of the collector delays the airflow and helps to create more turbulence for the airflow with increased energy. The solar dryer was locally developed at the Michael Okpara University of Agriculture and tested during the humid crop harvesting period of September for the worst-case scenario. The obtained drying curves and kinetics for cocoyam drying are subjected to the vagaries of weather conditions. The drying rate showed declining sinusoidal characteristics and took about 25 h to attain equilibrium. Analysis of the airflow velocity showed gravitation between laminar and turbulent flow, ranging from 171.69 to 5152.77. Specific heat capacity, thermal conductivity, and effusivity declined with moisture content while the thermal diffusivity increased. However, the values of thermal effusivity ranged from 12.2 to 47.94 W·s1/2·m−2·K−1, which is within the range of values for insulators. The heat and mass transfer coefficient varied as a function of the airflow velocity. Fitting the drying curve into semi-empirical models showed that the two-term model was the best-fitted model for the experimental data from drying cocoyam. Using the solar dryer in Nigeria can save $188.63–$1886.13 in running costs with a payback period of 0.059–0.59 years (21.54–215.35 days) at a rate of 10–100% of usage.
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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 | 0.000 | 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".