Bibliographic record
Abstract
In this study, we investigate the feasibility of applying advanced control design strategies to the mixing tank process of the plant of Falconbridge Ltd. in Sudbury, Ontario. The mixing tank receives Strathcona slurry, Strathcona filter cakes, dry Raglan powder and water as its inputs. Our objective is to control the Raglan to Strathcona ratio, the mixing tank level and the pulp density of the output mixed slurry to their respective set points so that the effects of all model uncertainties could be reduced. Static optimization is first carried out to investigate the solution set of the existing process. Then linearization and analysis of the existing system at its equilibrium point are performed. Hereinafter, besides the existing Repulper tank process, a new Three-Phase alternative process is suggested so that all uncertainties can be handled properly. Different controllers are then designed to determine if they are able to perform satisfactorily while undertaking all the uncertainties. Nonlinear controllers of "Input-Output linearization" and switching control, which is a variant of sliding control, are used. Robust switching control is found to have the robust capability with guaranteed robust performance and stability for system that has linear parametric bounded uncertainties and has a diagonal or triangular g matrix, the matrix that maps from the state equations' inputs to the state equations' differentials. Linear MIMO controllers of state feedback pole placement and H2 optimal control are used. Comparison of performance and robustness is made among different nonlinear and linear controllers.
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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 | 0.000 |
| 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 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".