Technoeconomic Assessment of Carbon Dioxide Conversion Projects in a Canadian Context
Bibliographic record
Abstract
The emerging field of CO2 conversion has the dual goals of reducing anthropogenic greenhouse gas emissions and creating economic value from a waste. The technologies involved may enable the decarbonization of industrial process emissions through the displacement of incumbent production methods. However, in the literature, variable process scopes and assumptions reduce the value of direct comparisons between published results. To enable meaningful comparisons, this thesis integrates techno-economic models for amine-based CO2 capture and proton exchange membrane H2 production with unified financial assumptions to harmonize the inputs and scopes of proposed conversion technologies. The results of 19 process models are harmonized using regional utility prices and emissions intensities in six major industrial regions of Canada. The studied products include methanol, ethanol, formic acid, synthetic oil, and mineral carbonates. The results show that CO2 conversion projects are highly dependant on the price and emissions intensity of the electricity supply, but also that differences in simulation result in high variability for cost and emissions. For example, the levelized cost of production for methanol from CO2 ranged from $645 to $1775 USD/ton (2020) when harmonized, with an emissions range of -1.01 to 1.89 tCO2e/t product. Harmonization resulted in an average increase of 27% in levelized cost of production from the published estimates. In Canada, the provinces of Quebec and Manitoba were found to be the most appealing regions for the deployment of CO2 conversion technologies. In the Quebec scenario, 16 of the 19 models were found to be environmentally beneficial in comparison to the incumbent production method, while only four were found to have a lower cost.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".