DATA PRE-PROCESSING TECHNIQUES FOR MULTIVARIATE ANALYSIS TO TREAT INDUSTRIAL OPERATING DATA FOR RETROFIT DESIGN
Bibliographic record
Abstract
Multivariate Analysis (MVA), a statistical design tool for dealing with very large datasets, was applied to historical data from a Thermo-Mechanical Pulp (TMP) newsprint mill in Eastern Canada. Partial Least Squares (PLS) type MVA models were created to identify significant correlations between operating parameters in the woodchip refining section and variations in pulp quality. Understanding these relationships is of crucial importance to any eventual retrofit design for this process. This paper focusses on pre-selecting and pre-treating the raw process data, including infrequently measured variables, to maximize the realism and usefulness of the MVA black-box models. Key methods explored were ways of selecting low-production periods for removal, techniques for identifying and eliminating major outliers using MVA outputs, and noise filtering. A major conclusion of this work was that the PLS models were significantly improved by pre-treating the data. This paper recommends an overall design approach for applying MVA to industrial operating data, involving stringent removal of dubious periods of operation such as aberrant process behaviour, and an aggressive Exponentially Weighted Moving Average (EWMA) filtering of all dependent and independent variables.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".