Control-oriented dynamic model of an inductively coupled plasma torch by artificial intelligence methodology
Bibliographic record
Abstract
Abstract Inductively coupled plasma (ICP) torches have been widely used in various materials processing. To improve the control of their processes, there has been a growing demand for simplified numerical models which can rapidly predict the dynamics of plasma jets subject to time-varying inputs or external disturbances. In this paper, control-oriented dynamic models of an ICP torch are developed as an alternative to the complex, high-level 2D time-dependent numerical model (i.e. a model based on magneto-hydrodynamic equations). Prior to model development, the detailed dynamic and nonlinear nature of the ICP torch to its time-varying operation conditions was numerically investigated using a 2D numerical model to gain insight into choosing appropriate dynamic model structures. Linear ARX (AutoRegressive with eXogenous input) and ARX-type neural network models were selected in the model identification, and their parameters or weightings were determined using the input/output data obtained from the 2D time-dependent numerical model. The dynamic behaviours of the ICP torch predicted from the developed models were in good agreement with the data from the 2D time-dependent numerical model. Using the developed models, a simple control system for the plasma temperature and axial velocity regulations was also designed and tested. The feedback control simulations demonstrated good set point tracking and disturbance rejection performances, indicating that the developed approach can be directly applied to the model-based control system design of an ICP torch as well as other thermal plasma torches.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".