Thermo-Economic Analysis of Integrated Hydrogen, Methanol and Dimethyl Ether Production Using Water Electrolyzed Hydrogen
Bibliographic record
Abstract
Carbon capture and utilization is an attractive technique to mitigate the damage to the environment. The aim of this study was to techno-economically investigate the hydrogenation of CO2 to methanol and then conversion of methanol to dimethyl ether using Aspen Plus® (V.11, Aspen Technology, Inc., Bedford, Massachusetts 01730, USA). Hydrogen was obtained from alkaline water electrolysis, proton exchange membrane and solid oxide electrolysis processes for methanol production. The major cost contributing factor in the methanol production was the cost of hydrogen production; therefore, the cost per ton of methanol was highest for alkaline water electrolysis and lowest for solid oxide electrolysis. The specific cost of methanol for solid oxide electrolysis, proton exchange membrane and alkaline water electrolysis was estimated to be 701 $/ton, 760 $/ton and 920 $/ton, respectively. Similarly, the specific cost of dimethyl ether was estimated to be 1141 $/ton, 1230 $/ton and 1471 $/ton, using solid oxide electrolysis, proton exchange membrane and alkaline water electrolysis based hydrogen production, respectively. The cost for methanol and dimethyl ether production by proton exchange membrane was slightly higher than for the solid oxide electrolysis process. However, the proton exchange membrane operates at a lower temperature, consequently leading to less operational issues.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".