BETSY - BioEthanol from Synthesis Gas. Production of Ethanol from Solid Non-food Biomass via Thermochemical Route: Presentation held at 3rd International Symposium on Gasification and its Application, Vancouver, Canada, October 14th-17th, 2012
Bibliographic record
Abstract
The process of producing bio-ethanol from non-food lignocellulosic biomass in industrial scale consists of biomass gasification, synthesis gas cleaning, gas compression, synthesis gas conversion to ethanol and product stream make-up. While gasification, synthesis gas cleaning and product stream make-up can be made up from state-of-the-art technology, synthesis gas conversion to ethanol needs further investigation concerning improved catalysts and process conditions. Catalyst development is done by project partner by modifying the preparation process of a methanol catalyst thereby adding Fischer-Tropsch functionality to create the C-C bond. Samples of new catalysts will be tested in a small reactor (capacity 1 g) and afterwards promising catalysts will be tested in a pilot plant fixed bed r eactor (capacity 30 g). The reactors are fed with a mixture of H2, CO, CO2 from bottles. The small reactor is equipped with an online GC/MS-FID system for the analysis of the reactor effluent. At the reactor exit of the pilot plant, the gas is cooled down and the condensate is separated from the remaining gas. The gas is expanded and analyzed for H2, CO, CO2 and CH4 in an online IR-system. The liquid product is analyzed offline with GC/FID.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".