Bibliographic record
Abstract
The concerns on the significant negative environmental impacts of conventional fuels such as coal, oil and natural gas have fostered the shift of energy consumption towards renewable and environment friendly sources like biomass derived energy. Many technologies have been developed to generate energy from biomass; among them gasification is considered one of the most promising technologies, since and the generated syngas has many practical applications, such as a world-wide sustainable energy production or synthesis of fuels and chemicals. Unavoidable produced impurities during gasification can create severe problems in downstream applications; therefore, the cleaning of the produced syngas is essential. A major challenge in commercialization of syngas technology and its valorization is tar removal method. This review organizes the knowledge related to tar generated from biomass-derived syngas, and discusses the recent progress on clean technologies for tar elimination. The advantages and disadvantages of different tar removal methods are critically discussed. Primary treatment is able to optimise the gas composition for the secondary cleaning step but is not sufficient for further syngas applications. Among the secondary treatments, the catalytic cracking is most valued in terms of energy and yield efficiencies for syngas cleaning. This review discusses current technical barriers and future opportunities of technical development.
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) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".