Spatial and Temporal Resolution in Data-Driven Process Modeling of an Integrated Newsprint Mill
Bibliographic record
Abstract
Like many globalized industries, the pulp and paper sector finds itself with an increasingly demanding clientele, who continually expect a better and cheaper product. An important design strategy being employed to address this objective is through an analysis of the vast quantity of process and product data accumulated in plant-wide data historians, in order to improve operations. Mill processes are multivariate, meaning that the interactions between the variables are as important as the variables themselves. Process relationships must therefore be modeled as a group, using an appropriate simulation technique like Multivariate Analysis (MVA), with suitable data pre-processing to account for process upsets and other disturbances. In a previous paper, using an Eastern Canadian newsprint mill as an industrial case study, we showed that it was possible to find statistically significant correlations between wood chip refiner operation, intermediate pulp quality, and final paper quality using data-driven models. This was true even though some important process parameters went unmeasured, process lags changed with time, and the operation of key equipment items changed gradually with use. The present study compares the use of different timescales and combinations of unit operations to determine which ones yield the best MVA simulations. Because plant operating data were used, and experimental design was not practical, it is possible that some of the correlations found could be attributable to coincidence. We therefore added and removed variables and time periods to explore the validity of the models. The best MVA models were obtained by using a shorter (1-hour) data timescale, although use of a weighted-average filter helped to bridge the gap between these faster readings and the slower paper quality trends.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".