Investigation of the drying kinetics of brewer's spent grain ( <scp>BSG</scp> ) using artificial neural networks and traditional empirical models
Bibliographic record
Abstract
Abstract Brewer's spent grain (BSG) is the main by‐product of the brewing industry. BSG can have diverse end‐users and has a high moisture level. To guarantee good conditions for storage and trade, it is necessary to remove the moisture from the material and use the proper method for the drying process. The phenomenon of water content removal is represented by mathematical models. Empirical and phenomenological models, as well as artificial neural networks (ANN) can be employed for this purpose. Here, we compare the fitted curves between empirical models (such as Page, Midilli–Kucuk, and Newton models) and an artificial neural network. The fits were investigated quantitatively by the analysis of the mean squared error (MSE) and determination coefficient (R 2 ). The residues generated were analyzed qualitatively by the plots of histograms and qqplots (quantil vs. quantil). From the results that were obtained, it is possible to conclude that the ANN model had the best performance when compared to the empirical and semi‐empirical models studied, with the lowest values of MSE and the highest values of the R 2 (0.999). The net presented adequate estimations for intermediate data as well, proving its use as a proper empirical model for the prediction of moisture data at several temperatures.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".