All wastes/residues are not equal for hydrogen production via gasification: Impacts of feedstock properties on process parameters
Bibliographic record
Abstract
Eight different waste streams are evaluated in a gasification process with carbon capture modeled using Aspen Plus®. The influence of the feedstock properties on various process parameters is analyzed and quantified. The waste properties that have the largest impact on the process are heating value, moisture content, and carbon content. The first two properties directly impact the feedstock amount and process conversion efficiencies. The carbon content influences the process parameter requirements for the gasifier and shift reactor, product yield and carbon emissions from the process. Energy requirements are dictated by the carbon content and net heating value of the feedstock. Based on these findings, correlations are developed for extrapolation to any waste residue for hydrogen production via gasification. Feedstocks with high carbon content (85–86 wt%), high heating value (>39 GJ/t dry) and low moisture content (<5 wt%) positively impact the hydrogen production process. • Feedstock LHV and moisture content dictate amount of feedstock required. • Linear relationship between syngas yield and feedstock carbon content. • Conversion efficiency increases as LHV increases and moisture content decreases. • Energy requirements are dictated by feedstock net heating value and carbon content. • CO 2 emissions decrease as feedstock carbon content increases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".