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Enregistrement W3034788608

Petrochemistry-2015: Processing of heavy oils and oil sands

2019· article· en· W3034788608 sur OpenAlexaboutno aff
З. А. Мансуров

Notice bibliographique

RevueArchives in Chemical Research · 2019
Typearticle
Langueen
DomaineChemistry
ThématiquePetroleum Processing and Analysis
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésOil sandsPetroleumAsphalteneExtraction (chemistry)Environmental scienceChemistryUnconventional oilLight crude oilEnvironmental chemistryWaste managementPulp and paper industryPetroleum engineeringFossil fuelGeologyMaterials scienceOrganic chemistryAsphalt
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Abstract: One of the most important achievements of recent years is the creation of the technology for extraction of “heavy” oil from oil sands (OS) that is intensively developing in Canada. Huge deposits of OS of Republic of Kazakhstan which are characterized by content of organic part that ranges from 9 to 95% according to type and depth of each deposit are a major candidate as an alternate source of hydrocarbons. It is notable that we will obtain organic products with various physical and chemical properties counting on the tactic of processing of OS. Heavy oil may be a sort of petroleum that's very viscous, meaning that it's thick and doesn't flow easily. This is caused by both a coffee hydrogen to carbon ratio within the molecular make-up and therefore the presence of other minerals like asphaltenes, resins, sulfur and metals such as vanadium and nickel, which all increase its density. Nearly all the deposits of heavy oil are degraded remnants of conventional oils. Degradation begins when oil migrates toward the earth’s surface and encounters water containing oxygen and bacteria. A tar-like material is made at oil-water contact that eventually invades the whole oil accumulation. A process referred to as “water washing” removes the more water-soluble, light hydrocarbons, leaving an important oil accumulation. Heavy oil accumulations may represent as little as 10 percent of the first conventional oil. Due to their high density and viscosity, special extraction methods are needed to recover heavy oil efficiently. These methods include: surface mining, cold production and thermal recovery. Heavy oil can also require additional processing, usually mentioned as upgrading, after being produced so as to be transported and refined. Large amounts of energy are put into the extraction and production of heavy oil - about 20% to 30% of the energy that's actually produced. In reference to the above, within the Laboratory of Oxidation Processes of hydrocarbon of Institute of Combustion Problems (ICP) the event of following main directions of processing of OS in order to supply commercial oil products is carried: • Extraction of organic a part of OS of Kazakhstan deposits using different organic solvents with subsequent oxidizing it to bitumen, that is used for road construction; • Thermal processing of OS of Kazakhstan deposits with obtaining of synthetic oils also as hydrophobic mineral part; • Ultrasonic method for separation of organic and mineral parts of OS using solutions of alkaline metals, serving as surfactants; Along with development of methods of OS processing a great attention is paid to improve the physical & chemical characteristics of road bitumen by creation of its composite with rubber crumb, also as a drag of recycling of rubber pollutants and wastes is solved. An important aspect of ICP research is ecology of oil and gas industry. Study is carried out as research in area of bio-remediation of oil-contaminated soils using bacteria. Heavy oil makes up a big portion of the world’s discovered petroleum resources, while only a really small fraction of those resources are produced thus far . High density and viscosity have traditionally made their recovery energy demanding as compared to lighter oils. Heavy crudes are expected to be an outsized contributor to the world’s energy needs within the future, as conventional supplies decrease. However, the technological costs to supply a barrel are currently much above with conventional resources. Additionally, the increased energy requirements and unconventional practices in production raise various environmental concerns like land disturbance, tailing ponds, and better greenhouse emission emissions. Biography: Z A Mansurov is a General Director of the Institute of Combustion Problems of the Ministry of Education and Science of the Republic of Kazakhstan, prominent scientist of Kazakhstan; Doctor of Chemistry; Professor; IHEAS Academician; Laureate of the State Prize of the Republic of Kazakhstan and of the Prize named after K. Satpayev. In 1974-1987, he worked as a junior and senior researcher and Head of the Laboratory of Physicochemical Methods of Research at S.M. Kirov Kazakh State University. In 1981, he was the first among scientists in Kazakhstan to become a research fellow at the UCL (UK). In 1990, he defended his Doctoral thesis at the Institute of Structural Macrokinetics, USSR AS. From 1992 to 2010, he served as Vice President for Research and First Vice-rector of the al-Farabi Kaz NU. He is a Chairman of Combustion and Plasma Chemistry and Physics and Chemistry of Carbon Materials International Symposiums, Chief Editor of Eurasian Chemico-Technological Journal and Combustion and Plasma Chemistry journals. In 2004, for services to Kazakhstan he was awarded Kurmet Order. Under his peer supervision, eight Doctors, 38 Masters and eight PhD theses were defended. He is the author of over 670 scientific papers, 6 monographs, 5 textbooks and 21 copyright certificates of the USSR and Kazakhstani patents.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,117
Score d'incertitude au seuil0,556

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,023
Tête enseignante GPT0,347
Écart entre enseignants0,324 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2019
Routes d'admission1
Résumé présentoui

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