OPTIMIZATION AND SENSITIVITY ANALYSIS OF AN EXTENDED DISTRIBUTED DYNAMIC MODEL OF SUPERCRITICAL CARBON DIOXIDE EXTRACTION OF NIMBIN FROM NEEM SEEDS
Bibliographic record
Abstract
ABSTRACT In this article, supercritical extraction of nimbin from neem seeds has been studied. In order to investigate the effect of parameters on nimbin extraction yield, a partial differential equation model based on mass conservation principles. The model was solved using MATLAB software. The results were successfully validated with available laboratory experimental data. The optimum values of the operating parameters were obtained using gradient search strategy. Optimization routine was employed to maximize process profit. The optimum value of temperature, pressure, CO2 flow rate and particle diameter were found to be 305K, 177.339 bar, 0.9660 cm3/min and 0.0575 cm, respectively. Finally, a sensitivity analysis was carried out on the different model parameters, and found that process profit is mostly sensitive to neem price. PRACTICAL APPLICATIONS This work uses mathematical optimization as a computational engine to arrive at the best solution for neem extraction in a systematic and efficient way. In the context of neem supercritical fluid extraction (SFE) systems, coupling optimization with suitable simulation modules opens a new avenue of possibilities. It saves money and provides economical benefits. In neem SFE process, measuring parameters and understanding the process are difficult. In this case, modeling can provide virtual environmental for operator practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".