Complex Mixture Analysis by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry: Applications for the Fuel Industry
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».