Machine Learning and Derivative-Free Optimization for PID Tuning: Case Study of Improved Black Liquor Concentration Control
Bibliographic record
Abstract
Proportional-Integral-Derivative (PID) controllers are instrumental in managing industrial processes. Their effectiveness hinges on the precision of their tuned parameters. Consequently, it becomes essential to monitor their performance frequently and re-tune them regularly to ensure optimal operation. However, conventional tuning methods require significant effort and expertise. This research tackles this challenge by using historical data to construct machine learning (ML) models, coupled with optimization algorithms, to streamline PID tuning. Specifically, we propose integrating an Explainable Boosting Machine (EBM) as an ML model and harnessing Bayesian Optimization (BO) within a comprehensive PID tuning framework. EBM stands out for its ease of construction and accuracy. The synergistic combination of EBM and BO yields an effective solution, as demonstrated through a case study involving black liquor concentration control in a multiple-effect evaporator system within kraft pulp manufacturing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".