Dynamic Modeling and Optimization of a Batch Reactor for Limonene Epoxidation
Bibliographic record
Abstract
A mathematical model for limonene epoxidation over PW-Amberlite in a batch reactor was developed and used for reactor simulation and optimization. The mathematical model was validated by comparison of predicted and experimentally determined limonene conversion under isothermal and nonisothermal conditions (23−50 °C) and for several limonene/oxidant molar ratios. By a sequential simulation and an optimization approach using genetic algorithms (GA), the temperature profiles minimizing the energy consumption and the variability of limonene conversion were obtained. Simulation of limonene epoxidation using the optimal temperature strategies showed that it is possible to achieve a limonene conversion of 80% in a shorter period of batch time when compared to typical isothermal conditions at 33 °C. The proposed model may also be used to scale up the catalytic system. As an illustrative example, an optimization formulation was proposed to estimate the minimum volume (18 L), the aspect ratio (height/diameter, H / D = 1.7), and the temperature profile that maximizes limonene conversion and minimizes energy consumption to obtain at least 1000 g of limonene epoxide.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".