The development of the production cost of oxymethylene ethers as diesel additives from biomass
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Oxymethylene ethers (OMEs) are important diesel additives because of their ability to reduce soot loading, particulate matter (PM) emissions, and NO x 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 OME n (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 OME 1–8 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.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it