Carbon dioxide utilization in methanol synthesis plant: process modeling
Bibliographic record
Abstract
Abstract The conversion of CO 2 to methanol holds great promise, as it offers a pathway to reduce CO 2 level in the atmosphere and also produce valuable components. In this study, a typical methanol synthesis plant for CO 2 conversion was numerically modeled. Effect of fresh feed to plant parameters (i.e., pressure and CO 2 concentration) as well as the influence of recycle ratio on the reactor performance was investigated. Hence, all essential equipment, including compressor, mixer, heat exchanger, reactor, and liquid–vapor separator were considered in the model. Then, at the best operating conditions, thermal behavior and components distribution along the length and radius of the reactor were predicted. Finally, the effect of inert gases was investigated in the methanol production process and the results were compared with the conventional route (CR), which uses natural gas for methanol synthesis. The results revealed that in the absence of inert gases and by employing a recycle stream in the process, CO 2 hydrogenation leads to 13 ton/day production of methanol more than CR. While in the feedstock containing 20% inert gases, which is closer to the realistic case, methanol production rate is 45 ton/day lower than CR. These findings prospect a promising approach for the production of green methanol from carbon dioxide and hydrogen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".