Thermodynamic and Economic Assessment of Oxy-Combustion CCS System Applied to Gas Turbine-Driven Compressor Stations
Bibliographic record
Abstract
Government policies currently in place or in development will require the reduction of greenhouse gas emissions from industry, including gas transmission systems. Most of the natural gas transmission systems are powered by gas turbines of sizes up to 30 MW per unit. A typical gas turbine of this size can emit 6 ktonnes of CO2 per MW-year, equivalent to about 180 ktonnes per year. Reduction in greenhouse gas emissions can be accomplished through post-combustion systems (separation of CO2 from flue gases through chemical absorption, physical adsorption, membrane or cryogenic systems) or through oxy-fuel combustion, where fuel is combusted in pure O2 leading to sequestration of CO2 by compression and dehydration of the exhaust gases. The purpose of the current work is to explore the application of the latter concept, simplify and enhance the cycle, and to provide an economic valuation of the cost per tonne of CO2 abated. Innovations explored for enhancement include elimination of excess power production, simplification of capital equipment requirements, and optimization of the power to the booster produced from the gas turbine and steam turbine. The paper presents various innovation options arrived at, detailed thermodynamic parameters, and a cost and economic evaluation of these options. Particular emphasis was placed on the application of this technology to a typical compressor station on a natural gas transmission system as this application is vastly different than a typical power plant contemplating or employing carbon capture and storage (CCS) systems, most notably from the small power size, the remote location, and the self-containment perspectives.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".