A Bayesian learning and data mining approach to reaction system identification: Application to biomass conversion
Bibliographic record
Abstract
The growing environmental concern over the use of fossil fuels calls for alternative sources of energy with smaller environmental footprint, and biomass-derived fuels have been extensively investigated as a substitute. In biofuels production, the development of reaction networks and kinetic models is unquestionably a major challenge due to the difficulty in characterizing the reaction products. Therefore, there is a need for a better way to retrieve the information about the reaction from the available experimental data. This study uses a data mining and Bayesian learning approach to estimate the reaction network of the acid and base catalyzed hydrous pyrolysis of hemicellulose from Fourier Transform Infrared (FTIR) spectroscopy. Cluster analysis is used to model the system in terms of lumps and a Bayesian network structure-learning algorithm is then used to device a reaction network. Three Bayesian network structure-learning algorithms were implemented to estimate the reaction network. The results from each were identical, indicating that the model representing the reaction network is most probably in the optimal equivalence space. The model was compared against expert-based reaction models and the agreement is encouraging. A useful aspect of this model is its self-updating capability, i.e., the reaction model can provide a quantitative description of the effect of the change in the operation condition from spectroscopic data. Hence, the model may be used for the real time analysis of the investigated process.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".