Use of underground coal gasification gas for co-production of electric power and synthetic liquid fuel
Bibliographic record
Abstract
The study is relevant due to increased interest to the underground coal gasification technologies (UCG). The interest is determined by the depletion of world oil and gas reserves, the significant amount of coal deposits in various countries of the world, the growing energy demand, as well as the threat of global climate change. The possibility to use technologies of underground gasification of low-grade coal with complex geological environment is huge. Recently, interest to UCG has grown dramatically. In contrast to all major programs of the 20th century, this unprecedented interest is mainly stimulated by private capital in response to high oil and energy prices. Thus, the studies of UCG are carried out. And more than 30 tests are planned in Australia, China, India, South Africa, New Zealand, Canada, and the United States. The development of competitive gas-based technologies of production of electricity and synthetic liquid fuels is a high-priority task. The studies have been carried out using a mathematical model of the unit for the production of electricity and methanol. To design a mathematical model, a software, or the system of machine programs development (SMPP) has been used. It has been developed at Melentiev Energy Systems Institute of Siberian Branch of the Russian Academy of Sciences (ESI SB RAS). The article presents the results of the study of the use of UCG for the coproduction of synthetic liquid fuel (methanol) and electricity. A detailed mathematical model of electricity and methanol production unit has been developed. Based on the model, technical and economic optimization of the schemes and parameters has been carried out. It made possible to estimate the competitiveness conditions of the proposed method of coal processing. In addition, the sensitivity of the economic indicators of the unit to changes in external conditions has been studied. Based on the results of the analysis of the cost of diesel fuel in the eastern regions of Russia, the authors have made the conclusion that at present methanol produced by the energy technological unit is as competitive as delivered expensive diesel fuel. The introduction of such systems is economically reasonable in the near future.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".