Treating Drill Cuttings with Susceptors in a Single-mode Cavity Microwave
Bibliographic record
Abstract
The aim of the thesis was to evaluate the use of single-mode applicator in combination with susceptor technology for treatment of oil contaminated drill cuttings. Single-mode applicators allow samples to be exposed to high power density which increases the energy efficiency.\n\nOil separation was clearly enhanced with high power density for cuttings from Halliburton/North Sea. Less energy was used with high power density, where the result of OOC was determined to be 0,76%. For low power density, 2,24% OOC was achieved. This equals to a separation degree of 90,5 and 72,1, respectively, compared to initial OOC. The effect of susceptor was also tested on the Halliburton cuttings. The OOC was reduced to 0,16% after addition of MEG as susceptor and treatment in microwave.\n\nMore tests were conducted on a type of drill cuttings received from Canada. The effect of low and high power density, energy consumption, alternating cutting characteristics, susceptor quantity and dosing point was investigated in these experiments. The effect of high power density on oil separation was not evident without susceptor. The North Sea cuttings characteristics was to a high degree different from the Canadian cuttings. The best oil separation value for Canadian cuttings was 93,2%. This was an effect of using high power density, combined with dewatering in microwave before susceptor and salt was added. In some cases, 15% MEG showed to sufficient to remove oil below 1%, however some tests implied that increasing MEG concentration led to better oil separation.\n\nThe energy consumption was in general high for Canadian cuttings. Actions can be done to decrease the energy consumption, for example to pre-heat the susceptor and dose it on warm cuttings. Unfortunately, this was not included in this thesis and is still yet to prove.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".