Indirect adaptive model predictive control of a mechanical pulp bleaching process
Bibliographic record
Abstract
The classic way to control a process, in a model based framework, is to obtain a model of the system and then use it for the design of a controller. A time-varying process can require use of a real-time indirect adaptive controller, and a process with variable time delay may also call for a delay-time predictor. This paper describes a particular structure for such a controller and demonstrates its application to a pulp bleaching process. The variable delay time predictor constitutes the novel contribution of theis work. We discuss aspects of controlling the pulp bleaching process at Irving Paper Ltd., which is an extension on the work done in Sayda and Taylor [1]. The bleaching process was thoroughly studied, and single-input single-output process models identified on-line. This investigation showed that the process was accurately modeled as a first-order system plus a variable delay time. This is a difficult process to control, since the delay time varies substantially with pulp flow into and out of the bleaching vessel. The efficacy and robustness of our new technique is demonstrated by controlling the pulp bleaching process using an indirect adaptive model predictive control algorithm with a recursive least squares identifier and a variable delay time predictor embedded in that controller. KEY WORDS Indirect adaptive control, predictive control, on-line identification, pulp bleaching process, variable time delay. 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".