Excel-Based Tool to Analyze the Energy Performance of Convective Dryers∗
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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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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".