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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".