Temperature and water activity control in a lipase catalyzed esterification process using nonlinear model predictive control
Bibliographic record
Abstract
Abstract Controlling the batch esterification process is a difficult task because the kinetics of the process incorporate intrinsic nonlinearity, process uncertainty, and model mismatch. The model predictive control (MPC) was created and used in the lipase‐catalyzed esterification process in this study. The Autoregressive with Exogenous Input (ARX) and Nonlinear Autoregressive with Exogenous Input (NARX) models were embedded in the MPC. The controller's goal is to manipulate the jacket flow rate and air flow rate, respectively, to control reactor temperature and water activity. To identify the best controller performance, the ARX‐MPC and NARX‐MPC parameters of horizon time (P), number of control moves (M), and weighting factor (wk and rk) were tuned in tracking set point. In terms of set point tracking, disturbance rejection, and robustness test, the best‐tuned ARX‐MPC and NARX‐MPC controllers were assessed and compared. Due to smaller integral square error (ISE), quicker settling time, streamlined response, and manipulated variables remaining within their permitted constraints, the NARX‐MPC controller outperformed the ARX‐MPC controller.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".