Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision-Making—Part 2: Framework Application Considering Activity-Based Costing Concepts
Bibliographic record
Abstract
Operating time series data collected and stored in historian must be managed to extract their full potential. Part 1 of this paper proposed a structured way (a sophisticated approach) to process industrial data; this first part explains in detail the data processing framework used as the basis for the costing analysis present in the second part of this series. The framework considers the analysis scope definition, data management steps, and operating regimes detection and identification. The added value of this proposed framework is demonstrated in Part 2 via the use of cost accounting for operational problem-solving (debottlenecking), i.e., its practicality is validated via its application alongside a cost analysis on the brownstock washing department of a kraft pulp mill. The traditional debottlenecking approach assumes a single operating condition considering that operating regimes allow for a much more sophisticated debottlenecking study of the washing department. With the use of operations-driven cost modeling (contingent on activity-based costing concepts) and processed time series data corresponding to steady-state operation, incremental profit can be assigned to each operating regime in order to identify the most cost-efficient one. The overall objective of this two-part series is to convert processed industrial steady-state data and cost information into knowledge that can be used to optimize the washing department of a chemical pulp mill. More specifically, different operating regimes are assessed, and the most suitable operating strategy is defined. The application of activity-based costing on a large amount of historically processed industrial data led to the improvement in the operation. The identified optimal way to operate (pulp throughput, pulp conductivity, defoamer and bleaching chemical quantity, etc.) led to a profit of CAD 49 M per year. Lastly, a contribution analysis of the regimes based on PCA highlighted how the process was operated when the preferred performances happened.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".