Bibliographic record
Abstract
Petroleum coke (petcoke) is a low value by-product from oil and gas refinery. The production of oil sand petcoke has been continually increasing over the last 20 years. However, only 11-20% of the petcoke produced has been utilized as site fuel. The remainder has been largely stockpiled in northern Alberta. As oil sand petcoke contains higher carbon but a lower ash content compared to conventional crude oil petcoke, this project was designed to prepare porous carbon materials from petcoke. The aim of this thesis was to develop methods to convert by-products from oil refinery (eg. petcoke and asphaltenes) to value-added porous carbon materials. In order to combine nanoscale pores and macroscale pores into one monolithic structure, activation was proposed to develop micro and mesopores on oil sand petcoke as a first step. Both chemical activation (using KOH/NaOH) and chemical steam co-activation were studied to prepare activated carbon (AC) from petcoke. A salt template was then utilized to form macroscale pores between AC particles for hierarchical porous carbon (HPC) preparation. The co-activation of KOH and steam reduced the chemical agent amount without compromising pore volume. Before steam was introduced into the system, a molten phase around petcoke particles is presumed to be formed. A greater amount of chemical agent corresponded to a thicker molten chemical layer, which restricted the rate of steam gasification. By lowering the activation temperature to 500 ˚C, a 0.34 cm3/g pore volume and 800 m2/g surface area were obtained with an AC yield of 94%. Since there was almost no carbon consumption, the pores developed at 500 ˚C were most likely due to the opening of initial closed pores of petcoke. Finally, by using asphaltenes as natural binders to connect non-washed AC particles, HPC was fabricated with multiple scale pores after washing away the salts. The experimental results in this thesis provide feasible approaches to prepare porous materials from petcoke and asphaltenes. A better understanding of pore development during the activation process will help to optimize the process and control the properties of the final product.
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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 | 0.000 | 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) | 0.001 | 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 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".