Adaptive Process Monitoring for Multimode Industrial Processes Through Machine Learning
Bibliographic record
Abstract
In complex industrial processes, real-time monitoring of critical variables is essential for ensuring operational safety and efficiency. Traditional process monitoring models often struggle with processes characterized by multiple operating modes, leading to decreased prediction accuracy and reliability. Existing methods typically require prior knowledge of the number of operating modes and cannot adapt to new modes that emerge over time, limiting their applicability in dynamic industrial environments. To address these challenges, we propose an adaptive process monitoring framework that automatically identifies operating modes using change point detection and classifies data using Gaussian mixture models. Specialized subsoft sensor models are then constructed for each identified mode. This approach eliminates the need for prior knowledge of operating modes and enables the system to adapt to new operating conditions in real time. The effectiveness of the proposed methodology is demonstrated through a case study on the fluid catalytic cracking unit at the Parkland Refinery. The results show that our adaptive segmented model achieves a root-mean-square error (RMSE) of 2.645 and an R2of 0.819, significantly outperforming the nonsegmented model with an RMSE of 5.037 and a negative R2of −0.597. This adaptive framework enhances operational safety and efficiency by providing a robust and flexible monitoring solution for dynamically changing industrial processes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".