Profiling Alkyl Phosphates in Petroleum Samples by Comprehensive Two-dimensional Gas Chromatography with Nitrogen-phosphorus Detection (GC×GC-NPD)
Bibliographic record
Abstract
The profiling of alkyl phosphates in petroleum samples is of particular interest to refineries that process conventional crude oil derived from the Western Canadian Sedimentary Basin and other similar geologies. This is due to alkyl phosphate-based additives used during crude oil recovery processes and the subsequent contamination of the produced oil. Phosphate contamination causes numerous problems for refineries, including equipment fouling, the poisoning of catalysts, and potential impacts on downstream processes or consumers if these phosphates enter petroleum product streams. These issues have occurred at a number of facilities across Canada with the impacts measured in the tens of millions of dollars. In response, the Canadian Association of Petroleum Producers and the Canadian Crude Quality Technical Association have specified a limit of 0.5 μg/mL total distillable phosphorus in feedstock. This limit is monitored using inductively coupled plasma – optical emission spectroscopy (ICP-OES), a technique plagued with poor precision and a high limit of detection (0.5 ± 1 µg phosphorus/mL), thus making the current specification difficult to enforce. Furthermore, this method cannot provide speciation information, which is critical for developing an understanding of the challenge of alkyl phosphates at a molecular level. This thesis approaches these challenges using comprehensive two-dimensional gas chromatography with post-column Deans switching to allow for effluent flow switching between a flame ionization detector and a nitrogen-phosphorus detector (GC×GC-FID/NPD). Using trimethylsilyl derivatization, splitless injection, and concurrent backflushing, the GC×GC-FID/NPD method developed and optimized herein, represents the only analytical technique currently capable of: 1) separating the alkyl phosphates from each other and from the crude oil matrix; and 2) speciating and quantifying the phosphates reproducibly in petroleum samples at trace levels (levels two to three orders of magnitude below those achievable by ICP-OES). Overall, this work presents a significant step towards a routine, robust method for profiling trace alkyl phosphates in industrial petroleum and process samples in a production environment. In addition, this thesis presents preliminary results from the first-ever detailed study of alkyl phosphate contamination in a refining environment. The final results of this study will hopefully provide the chemical information needed to contemplate future mitigation strategies for handling the “phosphate problem”. This will be important to refineries across Canada that struggle with issues of equipment fouling. A mitigation strategy would be, quite literally, a multimillion-dollar idea for the Canadian petroleum industry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".