Biomass-derived oxymethylene ethers as diesel additives: A techno-economic assessment
Bibliographic record
Abstract
Conversion of agricultural biomass such as wood chips, wheat straw and forest residue for the production of fuels can help in reducing GHG emissions since they are considered as nearly carbon neutral. Around the world there is a significant amount of forest and agricultural-biomass available which could be used for the production of liquid fuels that can be blended with the petroleum-based diesel. Oxymethylene ethers (OMEs) can be derived from biomass via gasification, water-gas shift reaction and methanol production. The addition of OMEs to conventional diesel fuel has great potential to reduce soot formation during the combustion in diesel engines. Unlike methanol and dimethyl ether (DMM) which can also reduce soot formation, the physical properties of OMEs allow the use in modern diesel engines without significant change of the engines infrastructure. In this study, a detailed and data intensive process simulation model was developed to simulate all the unit operations involved in the production of OMEs from biomass. The unit operation considered include biomass drying, gasification, gas cleaning, water gas shift reaction, methanol production and OMEs synthesis. The simulation results were then utilized to conduct a detailed techno-economic assessment study of the whole biomass conversion chain to determine the most attractive pathways for OMEs production. Our recent study shows that the key parameters affecting the OMEs production are equivalence ratio, H2/CO ratio and optimal air flow. Overall, the cost of production ($/liter) of OMEs from different biomass feedstock in Alberta will be determined
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".