Optimal design of a four‐zone simulated moving bed process for separation of homoharringtonine and harringtonine
Bibliographic record
Abstract
Abstract A simulated moving bed (SMB) technology was applied to the separation of homoharringtonine (HHT) and harringtonine (HT), which were known to have the potentiality of being used as anti‐cancer agents. First, a series of pulse injection experiments were performed for estimation of the adsorption isotherm and mass‐transfer parameters of HHT and HT. The estimated parameters were utilised in the SMB optimisation tool based on the standing wave design method. From the optimisation tool prepared, the SMB operating parameters (zone flow rates and step time) that led to the highest throughput were obtained under the constraints of product purities (=99.0%) and pressure drop (≤1000 psi). Such an optimisation work was then extended to determine an optimal size of the adsorbent particle for the SMB of interest. The results showed that a particle size of 29 µm was the optimal one for maximising the SMB throughput under the conditions that the column configuration was 2–2–2–2 and the length of each column was 25 cm. If the SMB had the particle size other than 29 µm, its throughput was limited by either the maximum operating pressure or the mass‐transfer efficiency. Finally, an efficient procedure of removing a mobile‐phase additive (ammonium formate) from the product stream of the aforementioned SMB system was developed using a liquid–liquid extraction (LLE) technique. From the results of this study, it was confirmed that the SMB process coupled with a LLE procedure could be highly effective in separating HHT and HT with high throughout and high purity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".