Evaluating sensor accuracy and repeatability in gasketed plate heat exchangers
Bibliographic record
Abstract
Alfa Laval has connected sensors to their gasketed plate heat exchangers (gphe) to extend their customer service offering. To develop this service further the flow through the GPHE is needed. In this thesis, the objective was to evaluate an inexpensive flow switch and two sensor kits that measure pressure and temperature. The sensors were assessed by installing them on GPHEs in Alfa Laval’s lab and running seven test sessions. The flow switch performed well but experienced “jumps” in the output and a temperature dependence that could be compensated for through multiple regression. The sticker sensor's temperature measurements were accurate, but more calibration is needed to get the pressure. Regarding the in-house kit, the pressure measurements were reliable, but the temperature measurements suffer from lag and thermal inertia. The thesis has offered valuable insight that will propel Alfa Laval’s pursuit of a cost-effective way of measuring the flow and improving their service capabilities
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".