Application of time domain nuclear magnetic resonance (TD‐NMR) for study of the distillation curve of petroleum
Bibliographic record
Abstract
Abstract Crude oil distillates are a highly useful industrial product, mainly for energy generation. Unfortunately, they are rarely studied, mainly due to the low accessibility to products directly obtained from the distillation process, which is a laborious, expensive, and time‐consuming operation. This work presents and discusses the use of time‐domain nuclear magnetic resonance (TD‐NMR) as a simple, affordable, and straightforward tool for the development of correlations supported on the transverse relaxation time (T 2 ) and boiling temperature. The results point out a high convergence between TD‐NMR experimental data and the ASTM D2892 method for distillates from light, medium, and heavy oils, with up to 52.20% of accumulated mass and boiling point temperature ( T b ) up to 400°C. Furthermore, an unprecedented relationship between T 2 values and the accumulated mass of the distillates is first demonstrated. This new insight opens new perspectives for future prediction of accumulated mass for unknown crude oils, placing the TD‐NMR relaxometry as an appeal spectroscopy approach with a potential to meaningfully contribute to the daily refining petrochemical industry field operations.
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 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".