Quality Prediction from Hydroprocessing through Infrared Spectroscopy (IR)
Bibliographic record
Abstract
A fast, reliable, and inexpensive way to monitor the quality of hydroprocessed products from a heavy bitumen distillate is presented in this work. Predictive models for density, nitrogen and sulfur contents, and weight percentage of lumped products, in this case, the distillation cuts IBP–235, 235–280, 280–343, and 343+ °C, were obtained through Fourier transform infrared spectroscopy (FTIR) and partial least squares regression (PLS-R). In addition, two structural parameters also derived from FTIR spectroscopy are proposed for chemical understanding of the former process. Two sets of hydroprocessing experimental runs were conducted under various operating conditions to evaluate the nature of the hydrocarbon (HC) products from two different catalysts. The statistic validation of the models demonstrated the capability to predict accurately physicochemical properties of a wide range distillation cut. Simplicity and accuracy make FTIR-PLS a promising tool for online estimation of process conversion as well as products properties at pilot scale and even in refinery facilities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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 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".