Modelling and Validation of Evaporation Systems; Mass & Energy Balances
Bibliographic record
Abstract
To evaluate the performance of evaporation systems, performance parameters such as overall heat transfer coefficient and evaporation rate need to be evaluated. These parameters are difficult measure and therefore need to be calculated using other measured data. Alfa Laval Technologies AB have evaporation systems connected to the cloud that record measured data for evaluation. In this degree project 3 models for 3 different evaporation systems, a 1- effect system, a 2-effect system, and a 3-effect system, were developed in python. These models should be able to process the measured data and with it calculate and deliver information about system performance. The models were based on simple dynamic mass & energy balances for an evaporation system that were solved using the python function ”scipy.ompimize.minimize” in the python package ”scipy”. In the end the models all models fulfil the requirements however, the 1-effect system cannot collect data from the cloud and can only be run with a specific set of measured data points. All systems were validated by comparing model results to validated results, the 1-effect system using measured data and the 2- and 3-effect systems using an existing program for determining system performance. The validation showed that the 2-effect models results were very accurate. The model results for the 1- and 3-effect systems were less accurate but still okay.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".