Bibliographic record
Abstract
The aim of this work has been to minimize the thermal energy required to dry malt in deep beds while maintaining malt quality, and without increasing the drying time more than one hour. Malt drying usually takes place in deep bed (. 7-1 m) driers by forcing hot air through the bed. Measurements of inlet and outlet relative humidity, temperature, and airflow at a drier at Great Western Malting Company's Vancouver, Washington facility were made to find average moisture content versus time. The measurements were used to develop a wetted surface model of a malt bed. However, the model was not detailed enough to accurately fit the drying data taken from the kiln. Thus it was necessary to consider a more complex model. A diffusion based mathematical model of malt drying was coded using malt properties and drying equations found in the research of Bala (Ph.D. thesis, 1983). This program calculates moisture content and malt temperature in horizontal layers of a malt bed. Energy saving drying tests by airflow reduction methods were simulated with the program. The methods were designed to take advantage of the malt's internal drying mechanism, and they were effective at reducing energy consumption. However, model verification was necessary, and maintaining malt quality was essential. A deep bed experimental malt drier was built at Portland State University to allow malt temperature and average moisture content data collection. Drying experiments were performed at constant airflow, for several different drying temperature cases, and the highest experimental temperature with acceptable malt quality was found to be 7 5 C. Drying at 70 C (158 F) rather than at 63 C (145 F) was found to cause a 20% reduction in the thermal energy consumption, but higher temperatures did not significantly improve efficiency. The experimental moisture contents and grain temperatures generally compared well with diffusion model simulations of the experiments. Airflow reduction experiments decreased thermal consumption by 20% compared to typical drying schedules. These experiments were based on the airflow reduction methods learned from the diffusion model. However, diffusion model simulations using the experimental conditions showed thermal energy reductions of 11 %.
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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) | 0.000 | 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) | 0.000 | 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".