The Bitumen Extraction from Nigerian Tar Sand Using Dense Carbon Dioxide
Bibliographic record
Abstract
The need for alternative sources of energy has become even more acute in light of the recognition of the dwindling conventional world oil reserves. The interest in exploring other avenues of complimenting and/or eventually replacing this resource is growing quite rapidly. A ready alternative to conventional crude oil is oil sands which are abundant and vastly unexplored. The huge deposits of tar sand found in South-Western Nigeria remain untapped due to concerns about the environmental impact. The consequences of the methods in processing tar sand, ranging from water pollution to emission of greenhouse gases, especially in Canada bring in to sharp focus the urgent need for an alternative means of extracting oil from tar sand. A more effective and less environmentally damaging procedure could be the break through needed to open a new chapter in the exploitation of oil sands. The alternative recovery procedure is supercritical carbon dioxide extraction. Recent supercritical extractions use high temperatures and pressures. The upgrade in this research involves using high pressures and lower temperatures which saves energy and improves the process. The experimental study of the bitumen extraction from Nigerian tar sand by dense CO2 was carried out by high pressure extractor. The samples of tar sand were first heated in an oven at 120 °C to melt. A 50 g sample of melted tar sand with addition of 3 g of ethanol was placed into an extractor and heated to 80 °C to initiate the experiment. Carbon dioxide was injected in to the extractor to create 50 MPa of pressure in static mode for 20 min after which the extract was collected. In the presence of ethanol, the extract had a lighter colour than the usual black. Nigerian tar sand is known to be composed of 84 % sand, 17 % bitumen, 4 % water and 2 % mineral clay. Using this data, an extract of 19.47 % was calculated which makes the recovery achieved very encouraging. The experiment shows that recovery of bitumen from tar sand is possible under relatively low temperatures and can be possibly economically profitable.
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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".