Experimental Assessment, Model Validation, and Uncertainty Quantification of a Pilot-Scale Gasifier
Bibliographic record
Abstract
This contribution presents a new set of petroleum coke dry gasification tests performed on pilot-scale gasifier. Dry gas composition and flow rate, temperature distribution, conversion, and pollutant formation taken from the experimental tests and respective calculations were used to validate the prediction capabilities of a reduced order model (ROM) developed for the same gasifier. The ROM predicted the experimental observations for conversion in the range of 48–90%. This study confirms that a systematically developed ROM (with a fixed framework) can predict the behavior of a gasifier under different operating conditions with reasonable accuracy. Moreover, this study investigates the variability in the ROM’s key outputs in the presence of uncertainty in the feed and model parameters, i.e., the volatile percentage of the fuel, solid particle diameters, angle of multiphase flow jet, and recirculation ratio. These parameters affect the feedstock’s properties and the mixing/laminar flows within different zones of the gasifier. Insights gained from the uncertainty quantification study revealed significant variability in the conversion, peak temperature, and steam percentage in the syngas; while the dry syngas composition does not seem to be significantly affected by the uncertainty of the parameters considered.
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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".