Process development and simulation of a novel solar energy plant integrated with solid oxide fuel cell, hydrogen, heat recovery and carbon capture systems
Bibliographic record
Abstract
Solid oxide fuel cell (SOFC) releases significant high-temperature thermal energy during its operational mode. If this heat is not managed properly, it leads to thermal stresses, material shocks, and degradation. To effectively utilize such a high-temperature heat, this work presents a thermodynamic analysis and environmental assessment of a novel concept that synergistically integrates a benchmark SOFC with a four-step hybrid Cu-Cl thermochemical cycle. The developed system incorporates a SOFC unit for electricity generation, an afterburner for the complete oxidation of unreacted fuel (H 2 , CO), a thermochemical cycle for utilizing high-temperature heat, a supporting Rankine Cycle (SRC), and an H 2 and CO 2 compression unit. The system is simulated by solving mass, energy, and exergy balances at steady-state conditions. Pinch point analysis is conducted using MATLAB to assess the thermodynamic feasibility of H 2 production. Furthermore, the specific primary energy consumption per unit of CO 2 avoided (SPECCA) is calculated to assess the system's environmental impacts. It is found that the CO 2 and H 2 compression train exhibit an overall exergy destruction of 5.83 kJ/mol of CO 2 and 5.98 kJ/mol of H 2 respectively. The thermolysis reactor of the Cu-Cl cycle carries the highest exergetic losses, with a share of 34.39%. The system exhibits a SPECCA value of 8.27 with 0.114 MJ/kg CO 2 , considering the options with and without the Cu-Cl thermochemical cycle. The system's overall energy and exergy efficiencies are also 64.45% and 59.07% respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".