Co-Production of Dimethyl Carbonate, Dimethoxymethane and Dimethyl Ether from Methanol: Process Design and Exergy Analysis
Bibliographic record
Abstract
Dimethyl carbonate is an important green chemical that has been widely used in the chemical industry. In the production of dimethyl carbonate, methanol oxidative carbonylation has been studied, but the conversion ratio of dimethyl carbonate using this method is too low, and the subsequent separation requires a large amount of energy due to methanol and dimethyl carbonate being azeotrope. In this paper, the strategy of "reaction instead of separation" is proposed. Based on this strategy, a novel process is developed to combine the production of DMC with that of dimethoxymethane (DMM) and dimethyl ether (DME). The co-production process was simulated using Aspen Plus software, and the product purity was up to 99.9%. The exergy analysis of the co-production process and the existing process was carried out. The exergy destruction and exergy efficiency were compared with those of the existing production processes. The results show that the exergy destruction of the co-production process is about 276% less than that of the single-production processes, and the exergy efficiencies in the developed co-production process are significantly improved. The utility loads of the co-production process are significantly lower than that of the single-production process. The developed co-production process increases the methanol conversion ratio to 95%, with a reduced energy requirement. It is proved that the developed co-production process can provide an advantageous option over the existing processes with improved energy efficiency and material savings. The strategy of "reaction instead of separation" is feasible. A new strategy is proposed for azeotrope separation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".