Biomass-derived Oxymethylene Ethers as Diesel Additives: A Thermodynamic Analysis
Bibliographic record
Abstract
Conversion of biomass for production of liquid fuels can help in reducing the greenhouse gas (GHG) emissions which are predominantly generated by combustion of fossil fuels. Adding oxymethylene ethers (OMEs) in conventional diesel fuel has the potential to reduce soot formation during the combustion in a diesel engine. OMEs are downstream products of syngas, which can be generated by the gasification of biomass. In this research, a thermodynamic analysis has been conducted through development of data intensive process models of all the unit operations involved in production of OMEs from biomass. Based on the developed model, the key process parameters affecting the OMEs production including equivalence ratio, H2/CO ratio, and extra water flow rate were identified. This was followed by development of an optimal process design for high OMEs production. It was found that for a fluidized bed gasifier with heat capacity of 28 MW, the conditions for highest OMEs production are at an air amount of 317 tonne/day, at H2/CO ratio of 2.1, and without extra water injection. At this level, the total OMEs production is 55 tonne/day (13 tonne/day OME3 and 9 tonne/day OME4). This model would further be used in a techno-economic assessment study of the whole biomass conversion chain to determine the most attractive pathways.
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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".