The Exergy Underground Coal Gasification Technology as a Source of Superior Fuel for Power Generation
Bibliographic record
Abstract
Underground Coal Gasification (UCG) is a gasification process carried on in non-mined coal seams using injection and production wells drilled from the surface, converting coal in situ into a product gas usable for chemical processes and power generation. The UCG process developed, refined and practiced by Ergo Exergy Technologies is called the Exergy UCG Technology or εUCG® Technology. The εUCG technology is being applied in numerous power generation and chemical projects worldwide. These include power projects in South Africa (1,200 MWe), India (750 MWe), Pakistan, and Canada, as well as chemical projects in Australia and Canada. A number of εUCG based industrial projects are now at a feasibility stage in New Zealand, USA, and Europe. An example of εUCG application is the Chinchilla Project in Australia where the technology demonstrated continuous, consistent production of commercial quantities of quality fuel gas for over 30 months. The project is currently targeting a 24,000 barrel per day synthetic diesel plant based on εUCG syngas supply. The εUCG technology has demonstrated exceptional environmental performance. The εUCG methods and techniques of environmental management are an effective tool to ensure environmental protection during an industrial application. A εUCG-IGCC power plant will generate electricity at a much lower cost than existing or proposed fossil fuel power plants. CO2 emissions of the plant can be reduced to a level 55% less than those of a supercritical coal-fired plant and 25% less than the emissions of NG CC.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".