Tutkimus typen virtauksesta ja kulutuksesta teollisessa mittakaavassa
Bibliographic record
Abstract
For many chemical processes, it is important to prevent unwanted chemical reactions, remove contaminants, and ensure process safety. Due to its abundance in the atmosphere and inert properties, nitrogen gas provides a cheap and viable alternative to ensure that these factors are met. Depending on the applications and scale of consumption, selecting a suitable nitrogen production method grants the most economically feasible way to produce it. This bachelor’s thesis investigates the nitrogen gas consumption in production units at Borealis Polymers Oy’s Kilpilahti site. In addition, the thesis examines the different production methods for nitrogen production with the safety aspect in mind in the form of a literature review. The aim of this thesis was to investigate and determine nitrogen consumption and recognize any discrepancies in the Borealis’ mass balances. The mass balances were calculated with the help of online meters and P&ID charts. With the results it can be concluded that the average inflow to production units is ~1880 kg/h and the yearly consumed amount is 15 M kg with the price tag of ~3 M€ per year. With additional examination of mass flow an uncalibrated flowmeter was found. The discrepancies in the flowmeter acted differently in different flow ranges, but a depiction of the discrepancies was made with flow-group division and prediction intervals.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.165 | 0.063 |
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".