Thermodynamic equilibrium analysis of oxy‐dry reforming of biogas with CO<sub>2</sub> sequestration using Aspen HYSYS
Bibliographic record
Abstract
Abstract Hydrocarbon reforming route has gained immense attention for producing energy‐effective renewable fuels and/or chemicals in the recent era. In this simulation study, O2 addition and CH4 recycling's effect on the dry oxidative reforming reactor's performance was investigated. Aspen HYSYS simulation software was used to assess a biogas‐to‐syngas conversion process's feasibility by integrating with the CH4 recycling loop and CO2 sequestration in a dry oxidative reforming process. Simulation runs were performed by varying the O2/CH4 ratio content from 0 to 0.20 and temperature (450 to 700°C). Results illustrated that CH4 conversion improved by re‐injecting of unreacted CH4, followed by CO2 sequestration to a great extent. In dry oxidative reforming, at stoichiometry O2/CH4 ratio of 0.17, CH4 conversion (21.7%), and CO2 conversion (3.3%) were obtained at 500°C, with 1.2 H2/CO ratio. Further, 0.308 kg mole/h of H2 could be produced with biogas (1.0 kg mole/h) at optimal parameters 500°C (temperature) and 0.17 (O2/CH4 ratio) by using recycling loop in one cycle. Thus, dry oxidative reforming coupled with the CH4 recycling unit provides a better option for syngas/H2 production and remarkably addresses high energy demand in the dry reforming process.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".