Advanced model-based control for continuous process industries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Advanced control is an economical method to maximize return on existing capital investment in plants while minimizing the production. The challenge is to also minimize the cost of the material and human resources needed to implement advanced control on real world applications. This paper describes an innovative adaptive process controller based on an unstructured process modeling technique called dynamic modeling technology (DMT). This modeling method reduces the effort required to implement advanced control strategies. The controller is able to automatically determine the structure of the process model as well as adapt the model parameters as operating conditions change. The problems associated with existing classical model based methods, such as unmodeled dynamics, long setup time, changing dynamics and dead time, and the need for detailed process knowledge are greatly reduced. The advantages of an adaptive controller (AC) based on DMT include the ability to control processes with long and changing time delays and the ease of incorporating adaptive feedforward compensation into a control strategy. The limitations of adaptive control strategies that exist today will be reviewed and the results of applications in the glass manufacturing and the oil and gas industries will be presented.
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.
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 | 0.000 |
| 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 it