Simultaneous Design and Control under Uncertainty Using Model Predictive Control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work presents a new methodology for the simultaneous process flowsheet and control design of dynamic systems under uncertainty using a model predictive control (MPC) strategy. Although several methodologies that include structural decisions in the analysis have been reported, only a few have considered a model-based control strategy such as MPC. An iterative decomposition framework that includes a dynamic flexibility analysis, a robust dynamic feasibility test, a nominal stability analysis, and a robust asymptotic stability test is presented for optimal process flowsheet selection. While previous methodologies have formulated the dynamic feasibility and stability analysis as a mixed-integer nonlinear programming (MINLP) problem, the present method formulates these analyses as convex problems for which efficient numerical algorithms exist. The simultaneous process flowsheet and MPC design method was tested on a system of Continuous Stirred Tank Reactors (CSTRs) with multiple inlet streams. The results show that the optimal design obtained by the present method remained feasible and asymptotically stable in the presence of the critical realizations in the disturbances. Comparisons between the designs obtained by the present MPC-based method and those obtained with other design approaches, i.e., optimal steady-state design and simultaneous design and control using a multiloop proportional and integral (PI) control scheme, are presented and discussed in this work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it