Simulation of high‐temperature steam‐only gasification of woody biomass with dry‐sorption CO<sub>2</sub> capture
Bibliographic record
Abstract
Abstract Over the last few decades, research on the abatement of carbon dioxide (CO2) gas has gained momentum, due to its increasing atmospheric levels. This study investigated high‐temperature steam‐only gasification of woody biomass for the production of high‐purity hydrogen integrated with CO2 capture in a moving‐bed gasifier. Extensive process modelling and simulation were performed using the superior solid handling features of the Aspen Plus process simulator software. After validating the model with experimental data from a demonstration plant available in the open literature, a reversible carbonation‐calcination reaction of calcium oxide (CaO) with CO2 was added to the system. Sensitivity analyses were conducted to verify the predictive accuracy of the model. The effects of steam‐to‐carbon (S/C) ratio on the resulting gas composition were thoroughly studied to delineate the complex process of gasification. Beyond the mitigation of CO2 emissions, the introduction of a CaO‐based sorbent in the process simulation significantly enhanced hydrogen production by simultaneously promoting the forward water‐gas shift reaction and reducing tars through increased tar‐cracking reactions. The results show that hydrogen of a higher purity was produced with the inclusion of dry‐sorption CO2 capture in the gasification process. Moreover, the addition of the sorbent increased the higher heating values (HHV) by 3 times and improved the cold gas efficiency by 34 %.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".