Bypass control of heat exchanger network under uncertainty
Bibliographic record
Abstract
Abstract The dynamic control of the heat exchanger network is important for developing energy‐efficient and safe industrial processes. In such a system, the control is achieved through the bypass stream around the heat exchanger. This work aims to track the setpoint temperature of the mixed stream by manipulating the bypass fraction of the cold stream around the heat exchanger. The implemented control is in a non‐linear model predictive control (NMPC) framework. The first‐principles model of a shell and a tube heat exchanger is used. The orthogonal collocation technique is used to discretize the first‐principles model into the system of algebraic equations. In this work, uncertainty is also considered in the inlet temperature of the hot stream. The uncertain optimal control problem is dealt with by using a scenario tree‐based approximation along with the affine policy‐based method. The results show that, under different scenarios of uncertainty, the controlled variable efficiently tracks the setpoint. In comparison, considering the same scenarios of uncertainty used, the deterministic optimization approach shows significant deviation in the controlled variable from the setpoint as time passes.
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.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".