Gas Turbine Integrated High-Efficiency Oxy-Fuel Combustion Process With CO2 Capture
Bibliographic record
Abstract
Fossil fuel energy conversion processes are the primary source of anthropogenic greenhouse gas emissions. New approach to utilization of fossil fuels through near-zero emission energy conversion systems represents an emerging opportunity for developing new concepts and designs, increasing the efficiency of the baseline combustion processes and reducing their environmental footprints, including greenhouse gas emissions, through CO2 capture and storage. Oxy-fuel combustion process provides an elegant way to address the environmental issues, in particular CO2 emissions, associated with current combustion systems. In this process nearly pure oxygen (instead of air) is burned with fuel. The resulting flue gas is composed mainly of CO2 and H2O, and other trace contaminants (e.g., SOx, NOx and particulates). The challenge faced in the development of oxy-fuel systems is the inability of current design configurations and materials for combustors, boilers, and turbo-machinery, to operate at the high temperatures resulting from burning the fuel in pure oxygen. Recent development at CANMET has been focused on design of a new generation of advanced oxy-fuel systems. These systems are very compact and can be integrated with a modified gas turbine to generate electricity, while the products of combustion can be sent to another turbine for recovery. The resulting CO2-rich stream at the outlet of the turbine is then sent to a CO2 capture and compression unit to separate and compress CO2 for pipeline transport. In this paper we present this proposed gas turbine integrated high-efficiency oxy-fuel combustion process and its main components, including the gas turbine and heat recovery system design. Moreover, we will present the results of the overall system integration, performance modeling and simulation to develop the tools required to asses the efficiency and viability of the overall integrated system and its components.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".