Solvent‐free quantification and composition/source analysis of total oil and grease in cooling water
Bibliographic record
Abstract
Abstract Total oil and grease (TOG) in water is measured in industrial process waters to determine the concentration of petroleum products present. A common application for TOG measurement is to detect a hydrocarbon leak in circulated cooling water systems. A hydrocarbon leak from a heat exchanger has a negative effect on the stable operation of upgrading and refinery units. Detecting and quantifying a hydrocarbon leak is straightforward; however, identifying the source of the leak can be very time consuming and require a lot of trial and error. In this study, a solvent‐free TOG method using ClearShot extractors and Fourier transform infrared spectroscopy (FTIR) was developed and optimized for the quantification of bitumen derived hydrocarbon analytes in water. This was further expanded upon to develop a rapid method for hydrocarbon identification in the cooling water by combining chemical fingerprinting with a discriminant analysis classification model. Following the optimized solvent‐free TOG method, chemical fingerprints for six different hydrocarbon classes in water were analyzed by FTIR. The classification model for these hydrocarbon classes was constructed using a discriminant analysis algorithm with a 100% classification rate at a TOG mass loading greater than 150 μg. The optimized solvent‐free TOG method decreases exposure risk and ergonomic strain for technologists, improving overall safety and environmental performance. The hydrocarbon fingerprinting enables rapid prediction of a leak source which reduces the time and analysis required to isolate a leak as well as reducing the cost and environmental impact associated with blowdown and purge (disposal and treatment) of contaminated water.
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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".