Robust processes through latent variable modeling and optimization
Bibliographic record
Abstract
Abstract A data‐based approach for developing robust processes is presented and illustrated with an application to an industrial membrane manufacturing process. Using historical process data, principal component analysis and partial least squares are used to extract models of the process and of the sensitivities of the process to various disturbances, including raw material variations, environmental conditions, and process equipment differences. Robustness measures are presented to quantify the robustness of the process to each of these disturbances. The process is then made robust (insensitive) to the disturbances over which one has some control (e.g., by modifying the equipment units to which the process is sensitive and imposing specification regions on sensitive raw materials). It is also made robust to disturbances over which one has little control (e.g., environmental variations) by optimizing the process operating conditions with respect to performance and robustness measures. The optimization is easily performed in the low‐dimensional space of the latent variables even though the number of process variables involved is very large. After applying the methodology to historical data from the membrane manufacturing process, results from several months of subsequent operation are used to demonstrate the large improvement achieved in the robustness of the process. © 2010 American Institute of Chemical Engineers AIChE J, 2011
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".