Scale-Dependent Techno-Economic Analysis of CO<sub>2</sub> Capture and Electroreduction to Ethylene
Bibliographic record
Abstract
The decarbonization of the chemical industry is essential to mitigate carbon dioxide (CO 2 ) emissions. Ethylene (C 2 H 4 ) is the highest production petrochemical globally. When powered by renewable electricity, the electrochemical conversion of CO 2 to C 2 H 4 offers a promising route to low carbon C 2 H 4 production. We perform a detailed techno-economic assessment (TEA) of the CO 2 reduction reaction (CO 2 RR) process, converting CO 2 from an industrial point source to polymer-grade C 2 H 4 . We pair the CO 2 electrolyzer with industrially mature upstream and downstream separation technologies in an Aspen Plus model. This comprehensive approach enables us to assess the valorization of both gas and liquid byproduct streams at commercial specification and assess the viability of these processes as a function of scale. We demonstrate that a minimum plant size of ∼3,000 tonne C 2 H 4 /year is needed to achieve economies of scale among the upstream and downstream processes. This minimum plant size is ∼200-fold smaller than that of conventional C 2 H 4 plants, coincides with that of typical utility-scale solar installations (∼25 MW), and could enable a more distributed model of chemical production going forward. We further highlight technical and economic enablers that would increase the profitability of the CO 2 RR to C 2 H 4 technology.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".