Process Design and Technoeconomic Analysis of Integrated Electrochemical CO <sub>2</sub> Conversion to Ethanol
Bibliographic record
Abstract
The pressing need to mitigate anthropogenic CO 2 emissions from hard-to-abate sectors, such as cement manufacturing, has intensified the pursuit of carbon capture and utilization (CCU) technologies. This study addresses a critical gap in the system-level integration of CO 2 capture, electrochemical conversion, and downstream product separation for ethanol production. An integrated process model was developed, coupling rate-based Aspen Plus simulations of postcombustion CO 2 capture from cement flue gas with a custom Excel-based electrolyzer model employing Cu 3 Sn catalyst membrane electrode assemblies. Electrochemical conversion was simulated at a Faradaic efficiency of 64.3% for ethanol at an industrially relevant current density of 900 mA cm –2 . Downstream separation, including extractive distillation, was used to achieve fuel-grade ethanol purity (≥99.5 wt %). Technoeconomic analysis revealed limited economic viability under baseline assumptions, with a net present value (NPV) of −2008 USD per tonne of CO 2, driven primarily by high electricity demand (120 GJ per tonne ethanol). Sensitivity analyses demonstrated that reducing the cell voltage and securing electricity prices below 25 USD/MWh could shift the process into profitability under certain conditions. These findings highlight the necessity of co-optimizing capture, conversion, and separation technologies in tandem, and highlight the critical role of electrolyzer efficiency and electricity pricing in determining the commercial feasibility of CO 2 -to-ethanol pathways.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".