Hydrogen-Rich Gas Stream from Steam Gasification of Biomass: Eggshell as a CO<sub>2</sub> Sorbent
Bibliographic record
Abstract
The present study investigates the steam gasification of biomass with an in-process CO 2 capture. The work is aimed at achieving hydrogen enrichment while reducing the carbon dioxide (CO 2 ) concentration in the gas stream. A perceived waste resource, eggshell, was utilized as the source of the CO 2 sorbent, while sawdust was used as the feedstock. The eggshell was calcined at 900 °C to activate it for the carbonation process. The gasification tests were conducted in a bubbling fluidized bed reactor with the calcined eggshell (CES) as the bed material in addition to being a CO 2 sorbent. Thermogravimetric analysis conducted on the eggshell showed that 900 °C is sufficient to fully convert the calcium carbonate (CaCO 3 ) in the eggshell to calcium oxide (CaO). The complete conversion was also evident in X-ray diffraction peaks. The effects of key process parameters, steam to biomass ratio (SBR) and calcined eggshell to biomass ratio (CEBR), were examined. Increasing the CEBR provided more CaO to the process, promoted the CO 2 uptake via the carbonation reaction, and accordingly enhanced hydrogen enrichment. An increase in SBR in the CES-based tests improved the hydrogen concentration in the gas stream. A minimum CO 2 volumetric concentration of 3.3 ± 0.4% and a maximum hydrogen concentration of 78 ± 3.6% were obtained in this study at a temperature of 650 °C, an SBR of 1.2, and a CEBR of 1.0. Additionally, results of the CES-based experiments showed that the water gas shift reaction is more important for the enhancement of hydrogen production than the other gasification reactions.
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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) | 0.000 | 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) | 0.001 | 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".