Excel-Based Tool to Analyze the Energy Performance of Convective Dryers∗
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An algorithm to examine the energy performance of convective dryers was developed and transformed into an Excel-based calculation tool. Provided with the input data for a given industrial dryer, this tool allows the energy use to be quantified in terms of the specific energy consumption and energy efficiency. The energy use can then be compared with the corresponding values for an ideal adiabatic dryer to identify the potential for energy savings. The algorithm accounts for direct and indirect dryers as the single- and multistage units operated in closed or open cycles. In addition, the maximum energy efficiency can be determined for nonhygroscopic materials through the sorption isotherms. The tool permits the identification of the major sources of dryer inefficiency and allows energy savings to be calculated for several low- and medium-cost measures such as dryer insulation, partial recycling of exhaust air, feed preheating, and others. The tool is built as a modular system comprising the following main components: dryer identification, calculation of actual energy consumption, comparison with the theoretical energy consumption, identification of sources of energy inefficiency, and analysis of options to reduce energy consumption.
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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 | 0.001 |
| 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 it