Complex Mixture Analysis by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry: Applications for the Fuel Industry
Bibliographic record
Abstract
As the world's reserves of light crude oil are depleted, the fuel industry will have to find other sources to generate transportation fuels. There are known supplies of unconventional crude oil (bitumen) in Canada, but it is more difficult and costly to generate fuels from bitumen. In addition, bitumen is still a fossil fuel, which means that the supply is finite. Ultimately, development of an alternative fuel from renewable resources, such as biofuel, would be the best option. Development of a cost efficient biofuel would lessen the demand for fossil fuels and benefit the environment at the same time. However, at this time, it is still more cost-effective to produce fuels from unconventional crude oils than biofuels. Petroleomics has utilized Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to successfully link the chemical composition of conventional petroleum crude oil to the behavior of that feed during production and processing. However, the chemical composition of unconventional crude oils and biofuels is still relatively unknown due to their complex nature and more recent usage compared to light crude oils. The ultrahigh resolving power and mass accuracy of FT-ICR MS can be used to determine the chemical composition of extremely complex unconventional crude oil and relatively unknown biofuels. From the insight gained by FT-ICR MS, predictions on the best sources for future fuels can be made. Chapter 1 presents the basics about petroleum needed to understand petroleomics, including classification, terminology, and composition. Some of the problems associated with the use of bitumen are also described. This information is presented before the basics of biofuels (Chapter 2), especially bio-oil, to gain an understanding of how and why bio-oils were analyzed as they are. The techniques (FT-ICR MS and ionization methods) used to analyze complex mixtures, specifically bio-oil and petroleum interfacial material, are described in Chapter 3. The first analysis of bio-oil by FT-ICR mass spectrometry is presented in Chapter 4. Here, the chemical composition of the oily and aqueous phases of the bio-oil generated from the slow pyrolysis of pine pellet and peanut hulls is determined. Chapter 5 presents an analysis of bio-oils generated from different source material under different pyrolysis conditions than the study in Chapter 3. The chemical composition of the final product (bio-oil) depends of the source material and pyrolysis conditions. Boron-containing compounds were also discovered for the first time in bio-oils by FT-ICR MS. Bio-oils are too polar in their raw form to be used as a co-feed alongside petroleum in refineries without reducing their oxygen content. Chapter 6 studies the changes that occur to a raw bio-oil as it is upgraded over zeolite catalysts to promote deoxygenation. To gain more understanding of the oxygenated species present within bio-oils, a fractionation technique was applied to bio-oil samples to generate fractions of increasing polarity (Chapter 7). Some of the most dominate peaks present in FT-ICR mass spectra were correlated to possible structures from compounds that had previously been identified by GC-MS. Chapter 8 takes the information gained from analysis of highly oxygenated species (bio-oils) and applies this knowledge to the analysis of petroleum emulsions. The species thought to exist at the oil/water interface have higher oxygen content than the species typically identified in whole petroleum crude oils. A new method for isolating interfacial material from petroleum crude oil is described and validated in this chapter.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".