A comparative economic assessment of freshwater versus chloralkali electrolysis for eMethanol production
Bibliographic record
Abstract
In recent years, renewable-powered eFuels syntheses have gained significant attention due to their potential to reduce the reliance on fossil fuels and consequently greenhouse gas (GHG) emissions. However, the cost of eFuels production is still a major concern and is mostly driven by two major components: the cost of non-fossil based carbon dioxide (CO 2 ) that is expected to be sourced from direct air capture (DAC) technology, and the cost of H 2 production from conventional freshwater electrolysis. This paper explores the substitution of freshwater electrolysis with saltwater electrolysis, which yields a product-basket of eMethanol (eMeOH), caustic soda, chlorine and desalinated water, which are feedstocks vital to modern industry. This improves the total revenue generated from the equipment and energy consumed by the system as compared to the Benchmark freshwater based eMeOH production. For the normalized production of ⁓1.0 tonne/hour (t/h) of eMeOH, this novel process route consumes ∼18 % more electricity than the Benchmark but also produces ∼6.8 t/h of chlorine gas and ∼7.7 t/h of caustic soda. Using the same price index to compare the processes, the gross revenue of the considered process route is estimated at ∼5290 USD/t-eMeOH product basket, exceeding by far the gross revenue calculated for the Benchmark freshwater based counterpart at ∼800 USD/t-eMeOH. Consequently, for every unit of energy consumed, the eMeOH-containing product basket produced through the chloralkali electrolysis route exhibits the capacity to generate 6.6 times more revenue than the Benchmark, and thus produces eMeOH at significantly lower net 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".