Dual-Rate Adaptive Control for Mixed Separation Thickening Process Using Compensation Signal Based Approach
Bibliographic record
Abstract
The mixed separation thickening process (MSTP) of hematite beneficiation is a strong nonlinear cascade process with frequency of slurry pump as input, underflow slurry flow-rate (USF) as inner-loop output, and underflow slurry density (USD) as outer-loop output. The model parameters such as settling velocity of slurry particles and slurry height are unknown and nonlinear. Moreover, these model parameters vary from flotation middling, sewage, and magnetic separation slurry. In this paper, the unknown change of the above dynamic characteristics are described by the previous sample unmodeled dynamics and its change rate. A novel adaptive controller using compensation signal based approach is developed. Inner-loop closed-loop control system equation and lifting technology are adopted to develop dual rate adaptive control method. Two compensation signals are constructed and added onto the linear proportional-integral (PI) controller. Such two compensation signals aim at eliminating the effects of the previous sample unmodeled dynamics and tracking error, respectively. The stability and convergence analysis is given and a simulation experiment on hardware-in-the-loop simulation system of MSTP based on industrial data is carried out, where it shows that the USD, USF, and its changing rate can be controlled well inside their targeted ranges when the system is subjected to unknown variations of its parameters.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".