The development of the production cost of oxymethylene ethers as diesel additives from biomass
Bibliographic record
Abstract
Abstract Oxymethylene ethers (OMEs) are important diesel additives because of their ability to reduce soot loading, particulate matter (PM) emissions, and NOx emissions. Some research has been undertaken on the feasibility of producing OMEs from biomass but there is no techno‐economic assessment of OME production from biomass. In this study, we estimate the unit cost to produce OMEn (n = 1–8) from three different biomass types common to western Canada: whole‐tree woodchips, forest residues, and wheat straw. The techno‐economic model uses the OME production simulation results for 500 MT day−1 of dry biomass. The simulation results show that 97.70, 98.86, and 99.80 MT day−1 of OME1–8 can be produced from whole‐tree woodchips, forest residues, and wheat straw, respectively. The costs of producing OME per liter over 20 years of production are $1.93 ±0.15/L, $1.68 ±0.14/L, and $1.66 ±0.13/L, respectively, at a 95% confidence level for whole‐tree woodchips, forest residues, and wheat straw biomass. The sensitivity analysis results show that the internal rate of return, OME yield, capital cost, and biomass delivery cost significantly influence OME unit price. The production price versus capacity profile reveals that the optimum minimum price can be obtained at a plant capacity of 4000 MT day−1 of biomass; beyond this, the increase in capacity does not result in any appreciable decrease in production price. © 2018 Society of Chemical Industry and John Wiley & Sons, Ltd.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".