Parametric sensitivity of <scp>pH</scp> and steady state multiplicity in a continuous stirred tank bioreactor (<scp>CSTBR</scp>) using a lactic acid bacterium (<scp>LAB</scp>), <i>Pediococcus acidilactici</i>
Bibliographic record
Abstract
Abstract BACKGROUND A continuous stirred tank bioreactor (CSTBR) represents an open dynamic system with high probability to show nonlinear behavior such as parametric sensitivity and multiplicity of steady state. A priori determination of this behavior is needed to decide on the strategy of reactor operation. RESULTS The growth of a lactic acid bacterium, namely, Pediococcus acidilactici in a 2 L CSTBR is used to demonstrate the existence of parametric sensitivity of pH and the multiplicity of steady state in the system. A mathematical model has been developed and a dimensionless multiplicity criterion, ω, has been derived to indicate the set of values of input parameters corresponding to multiple steady states. Experiments have been conducted to study parametric sensitivity of pH with respect to input variables, namely, dilution rates and concentrations of nutrient and alkali stream for pH control in the regions of multiple and unique steady states. The CSTBR exhibited parametric sensitivity of pH over the entire region of operation under study. The experimental trends of parametric sensitivity of pH are also in agreement with those of theoretical parametric sensitivity of pH. CONCLUSION The nonlinear behavior of a CSTBR has been thoroughly portrayed in this article. The present study will add to the knowledge of control and operational strategies of CSTBRs. © 2015 Society of Chemical Industry
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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 | 0.000 | 0.002 |
| 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 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".