A novel modelling method for plate heat exchanger to predict the outlet cooling water temperature
Bibliographic record
Abstract
Abstract In this paper, a modelling method for a plate heat exchanger (PHE) is proposed to predict the outlet cooling water temperature. First, the mechanistic model of PHE is developed based on the theory of heat and mass transfer. The unknown parameters in the model are identified by solving an optimization model with the sequential quadratic programming (SQP) method. Then, to improve the output precision, a data‐driven model based on the kernel partial least squares (KPLS) algorithm is developed and added to the mechanistic model in parallel to compensate the deviations between the mechanistic model outputs and the real values of outlet cooling water temperature. With the PHE running, its performance deteriorates due to the aging of the device, fouling on the plates, and other unknown factors. Therefore, a performance assessment for the model is performed, and the model updating is carried out according to the assessment results. Finally, the feasibility and efficiency of the proposed modelling method are validated by application to the PHE in a circulating cooling water system. The established model lays an important foundation for the energy‐saving optimization of the circulating cooling water system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".