Bibliographic record
Abstract
Abstract There is an abundance of natural gas being discovered and produced that is slightly sour. According to a US Department of the Environment (DOE) survey that includes Canada, about 80% of current and new gas has a hydrogen sulphide (H2S) concentration of 1% or less. Of course, this must be treated to remove the H2S to meet sales gas specifications. For small scale (less than 50 – 100 kg) and large scale (greater than 20 tonne/d) of equivalent sulphur, current technologies appear reasonable. Conversely, for intermediate range (0.1 – 20 tonne/d) equivalent sulphur, current technology has proven to have high capital and/or operating costs and some processes are difficult to operate. Therefore, there is a need for an intermediate scale (0.1 to 20 tonne/d) process with lower capital and operating cost than those currently available. The applications of such a process range from the removal of H2S from acid gas at low pressure produced from the amine process to high pressure raw sour gas. There are additional challenges from sour gas associated with heavy oil thermal projects in that it is at low pressure and contains substantial C7+. The elemental sulphur produced should be of sales grade quality such that the handling of the product can fit into the existing sulphur infrastructure and sold into existing markets. Otherwise, disposal of the product becomes costly and in some cases becomes another environmental problem. In answer to this need, Xergy Processing Inc. has developed a gas phase direct oxidation process for the above applications as well as treating heavy oil off-gas, fuel gas, power generation gas. The process has relatively low capital and operating costs and is easy to operate, with no equipment that is unfamiliar to the petroleum industry. Conversion to sulphur depends on the process configuration and pressure but ranges from 80% to 99.9+% based on lab and field data.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".