Optimisation of Methyl Tert‐Butyl‐Ether (MTBE) Synthesis Processes using Aspen‐Plus
Bibliographic record
Abstract
Abstract Four different configurations for the production of MTBE were simulated and optimised using AspenPlus All process configurations were simulated and costed using AspenPlus The total annual cost (TAC), i e the sum of the individual annualised capital and operating costs, was calculated using models consistent with the conceptual design level Optimisation of each process configuration using AspenPlus simulation models was performed to determine the optimal design and operating variables The optimised configurations were compared on an economic basis It was possible to achieve iso‐butylene conversions in the range of 90 to 97% with a single conventional reactor followed by a distillation tower However, by using a reactive distillation column it was possible to achieve iso‐butylene conversions > 99% The optimal configuration was found to be a combination of an isothermal reactor followed by a catalytic distillation column The optimal TAC of this configuration was 22% lower than its nearest competitor, the catalytic distillation column It was found that the net MTBE reaction rate remains high until a substantial amount of iso‐butylene is converted, and then decreases quickly as the reacting liquid composition approaches the reaction equilibrium Therefore, it is not surprising that the best means of synthesis is to carry out the bulk of the initial conversion using a conventional reactor and to apply reactive distillation to the reactor effluent that is close to equilibrium at the exit temperature
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".