Research Progress on the Typical Variants of Simulated Moving Bed: From the Established Processes to the Advanced Technologies
Bibliographic record
Abstract
Simulated moving bed (SMB) chromatography is a highly efficient adsorption-based separation technology with various industrial applications. At present, its application has been successfully extended to the biochemical and pharmaceutical industrial sectors. SMB possesses the advantages of high product purity and yield, large feed treatment capacity, and simple process control due to the continuous operation mode and the efficient separation mechanism, particularly for difficult separation. Moreover, SMB performs well, particularly for multi-component separation or complicated systems’ purification processes in which each component exhibits similar properties and low resolution. With the development of the economy and technology, SMB technology needs to be improved and optimized to enhance its performance and deal with more complex separation tasks. This paper summarizes the typical variants or modifications of the SMB process through three aspects: zone variant, gradient variant, and feed or operation variant. The corresponding modification principles, operating modes, advantages, limitations, and practical application areas of each variant were comprehensively investigated. Finally, the application prospect and development direction were summarized, which could provide valuable recommendations and guidance for future research in the SMB area.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".