Improvement of Syngas Quality in Fixed Bed Gasifier Using CaMg(CO3)2 Catalyst
Bibliographic record
Abstract
This paper deals with the experimental investigation of lignite (L) and wood (W) feedstock in a pilot-scale downdraft gasifier. The study aims to check the compatibility of CaMg(CO3)2 [dolomite (D)] catalyst, 5% (W/W), with lignite and wood (L+D, W+D) feedstock as an additive to enhance the performance of a 10 kWe atmospheric pressure downdraft gasifier system. Fuel consumption and gas flow rate were found to be 10.01-11.6 kg h -1 and 26.76-29.57 kg h -1 , respectively, for lignite and wood feedstock (with and without catalyst). In lignite, CO and H2 concentrations were increased by 6.81 % and 4.9 %, respectively, whereas in wood, their concentrations were increased by 8.88 % and 5.1 % when the catalyst was employed with feedstock. The producer gas LHV and cold gas efficiency were increased by 6.02% and 5.75% in lignite and 6.97% and 6.61 in wood, whereas specific fuel consumption decreased by 5.92% (in L), 5.17 (in W) with dolomite feedstock. Tar and Total Particulate Matter (PM) concentrations in the producer gas were measured and found to have a noticeable decline with catalytic gasification for both feedstocks. The study concludes that adding dolomite offered better results in terms of higher efficiency and lower tar-PM concentrations.
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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".