Bioethanol fuel quality assessment using dielectric spectroscopy
Bibliographic record
Abstract
This study first aims to determine the physical characteristics of bioethanol from its dielectric properties during the production process. For this purpose, bioethanol samples were produced from sugarcane molasses under different fermentation conditions such as temperature and reaction time. The main permittivity properties, including dielectric constant and loss factor at various frequencies, were measured as the inputs, whereas flash point and octane number, as significant physical parameters, were considered as output variables. Linear regression (LR) models were developed to predict the relationships between predictors and targets. Next, four learning algorithms – multivariate regression splines (MARS), M5 tree, multilayer perceptron (MLP), and support vector regression (SVR) – were deployed to improve the models’ performance. The results from LR models revealed that the flash point had a direct relationship with the dielectric constant and loss factor. However, the octane number was indirectly proportional to the dielectric properties. Also, analysis of the testing data set of learning algorithms indicated that the best algorithm for predicting the responses was MARS, whereas the SVR model performed with the lowest accuracy. The findings from this paper suggest that dielectric spectroscopy is a valuable approach for estimating the physical features of bioethanol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".