Simulation of Solid Oxide Fuel Cell - Based Power Generation Processes with CO<sub>2</sub> Capture
Bibliographic record
Abstract
The Solid Oxide Fuel Cell (SOFC) is a promising technology for electricity generation. It converts the chemical energy of the fuel gas directly to electricity energy and therefore, very high electrical efficiencies can be achieved. The high operating temperature of the SOFC also provides excellent possibilities for cogeneration applications. In addition to producing power very efficiently, the SOFC has the potential to concentrate CO2 with a minimum of an overall efficiency loss. Concentration of CO2 is a desirable feature of a power generation process so that the CO2 may be subsequently sequestered thus preventing its contribution to global warming. The primary purpose of this research project was to investigate the role of the SOFC technology in power generation processes and explore its potential for CO2 capture in power plants. This thesis introduces an AspenPlusTM SOFC stack model based on the natural gas feed tubular internal reforming SOFC technology. It was developed utilizing existing AspenPlusTM functions and unit operation models. This SOFC model is able to provide detailed thermodynamic and parametric analysis of the SOFC operation and can easily be extended to study the entire process consisting of the SOFC stack and balance of plant. Various SOFC-based power generation cycles were studied in this thesis. Various options for concentrating CO2 in these power generation systems were also investigated and discussed in detail. All the processes simulations were implemented in AspenPlusTM extending from the developed natural gas feed tubular SOFC stack model. The study shows that the SOFC technology has a promising future not only in generating electricity in high efficiency but also in facilitating CO2 concentration, but the cost of the proposed processes still need be reduced so SOFCs can become a technical as well as economic feasible solution for power generation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".