Multiobjective optimization of the operation of a liquid–solid circulating fluidized bed ion‐exchange system for continuous protein recovery
Bibliographic record
Abstract
Like most real-life processes, the operation of liquid-solid circulating fluidized bed (LSCFB) system for continuous protein recovery is associated with several objectives such as maximization of production rate and recovery of protein, and minimization of amount solid ion-exchange resin requirement, all of which need to be optimized simultaneously. In this article, multiobjective optimization of a LSCFB system for continuous protein recovery was carried out using an experimentally validated mathematical model to find the scope for further improvements in its operation. Elitist non-dominated sorting genetic algorithm with its jumping gene adaptation was used to solve a number of bi- and tri-objective function optimization problems. The optimization resulted in Pareto-optimal solution, which provides a broad range of non-dominated solutions due to conflicting behavior of the operating parameters on the system performance indicators. Significant improvements were achieved, for example, the production rate at optimal operation increased by 33%, using 11% less solid compared to reported experimental results for the same recovery level. The effects of operating variables on the optimal solutions are discussed in detail. The multiobjective optimization study reported here can be easily extended for the improvement of LSCFB system for other applications.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".