Deep-learning-aided modifier adaptation: synergies with process intensification
Bibliographic record
Abstract
Deep learning allows for functions, and their gradients, to be approximated to a high accuracy. Modifier adaptation is a real-time optimization method, which is used to optimize process economics online, and requires gradients to make first-order model corrections. In this work, backpropagated gradients are computed from neural networks trained on historical steady-state data, thus not explicitly requiring any gradient data for training. Data curation and convergence properties are discussed for the proposed method. The deep-learning-aided modifier adaptation is tested in analogous simulated integrated and intensified reactor-separator systems, where it is shown to reconcile plant and model optima in the presence of model mismatch. The case studies show better economics and constraint satisfaction when using the intensified system and the deep-learning-aided modifier adaptation. Further, intensification and deep-learning-aided modifier adaptation are observed to work in tandem as both accelerate the convergence of the plant to its true optima. The proposed method shows how historical data logs can be leveraged to address epistemic uncertainty and improve performance in model-based optimization, especially in intensified systems.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".