Simulation of Storage Conditions of Mixed Biomass Pellets for Bioenergy Generation: Study of the Thermodynamic Properties
Bibliographic record
Abstract
Experimental and mathematical modeling of the moisture sorption isotherms for biomass pellets during storage is performed in this study. The tested pellets are a mixture of 50% wood: spruce or pine, and 50% switchgrass agricultural biomass. Storage conditions, i.e., temperature and humidity, are tested by varying the environment conditions in a conditioning chamber. The experimental results show that the moisture sorption isotherms are not affected by the temperature. Nevertheless, the equilibrium moisture content depends on the kind of the tested pellets. Mathematical modeling of the experimental isotherms is performed using four common models: the Oswin, GAB, Henderson and Peleg models. The Oswin model is defined as the most appropriate model to predict the moisture sorption isotherms of the spruce–switchgrass pellets. It presents a coefficient of determination equal to 0.998, a standard error around 0.049 and a chi-square approaching 0.007. On the other hand, Henderson and GAB models show the best results for pine–switchgrass pellets, with a coefficient of determination varying between 0.998 and 0.997, a standard error range 0.054–0.065 and chi-square error between 0.008 and 0.009. The thermodynamic properties, which include the net isosteric of heat and the entropy changes of sorption, are also determined for all tested samples.
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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".