OME Worked Example for the TEA Guidelines for CO2 Utilization
Bibliographic record
Abstract
This document contains worked examples of how to apply the accompanying “Techno Economic Assessment & Life Cycle Assessment Guidelines for CO2 Utilization” for oxymethylene ethers (OME). The Guidelines can be downloaded via http://hdl.handle.net/2027.42/145436. These worked examples are not intended to be a definitive TEA or LCA report on the process described, but are provided as supporting material to show how the TEA and LCA methodologies described in the guidelines can be specifically applied to tackle the issues surrounding CO2 utilization. The goal of this study was to identify economic opportunities and barriers for OME3-5, derive R&D pathways and benchmark values. The OME3-5 production process included seven system elements: membrane carbon capture, PEM water electrolysis as well as the synthesis of methanol, formaldehyde, trioxane, methylal (OME1) and OME3-5, combining and adjusting the findings of two prior studies from Michailos et al. (2018) and Schmitz et al. (2016).[1,2] Conventional diesel fuel and OME3-5 from conventional methanol are selected as benchmark products. The results are judged to be uncertain relating to -30% to +50% due to the low technical maturity of membrane carbon capture and OME3-5 conversion. The results are found to be sensitive to location and time related factors (currency, CEPCI, location factor) as well as to the technical and economic specifications of the water electrolysis process, especially electricity consumptions, electricity price and electrolyser capex. Under the optimistic assumptions of free electricity and electrolyzer capex of 330 MW, the COGM of OME3-5 becomes competitive in Germany but not in the United States due to the higher diesel prices in Germany.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.158 | 0.086 |
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".