Overview of Foreign Boiler Designs for Ultra Supercritical (USC) Boilers and Prospects for Development of USC Power Units in Russia
Bibliographic record
Abstract
Abstract In the coming years, global electricity generation will largely be carried out using coal as fuel (coal generation). Certain European countries, the United States, Canada, and Japan are trying to cut down the number of coal-fired power units with their complete disposal by 2030. At the same time, the countries of the Asia-Pacific region, mainly China and India, are extensively developing a technology for the coal generation of steam at ultra supercitical (USC) conditions, which improves the efficiency of electricity generation and reduces harmful atmospheric emissions. The world power industry presently uses steam conditions of approximately 30 MPa and 610/620°C. The efficiency is as high as 47%. An overview is presented of the designs of USC coal-fired power boilers from the largest foreign manufacturers of boiler equipment in Europe (Alstom), Japan (Mitsubishi Hitachi Power Systems and Ishikawajima-Harima Heavy Industries), and China (Harbin Boiler Co., Ltd, Dongfang Boiler Co., Ltd, and Shanghai Boiler Works, Ltd.). Russia ranks tenth in the world as to the total coal-fired power generation. The percentage of coal-fired generation in Russia was approximately 13.5% in 2016. The development of engineering solutions for the USC power unit was undertaken in Russia at the beginning of the 21st century. Boiler equipment manufacturers worked out projects of boilers designed to operate on various coal types for a 660-MW power unit. The construction of the USC power unit prototype requires joint efforts of the government, power engineers, metallurgists, research organizations, and equipment manufacturers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".