Bibliographic record
Abstract
This article is dedicated to the investigation of the possibility of the monitoring process physical parameters occurring in process vessels containing so-called dead zones.Modern production requires a qualitative, uniform throughout the volume of capacity, the technological process.During the working cycle of the processes flow in process vessels there are parts, the dynamics of processes in which differs from the dynamics of processes in other, more active parts of process vessel.Such activity is due to the circular liquids movement.Processes, chemical reactions in different turbulence areas proceed with a difference, and the results of measurements can vary significantly.The chemical reactions require control.An important issue is the possibility of equalizing process parameters in all parts of the vessel, including in dead zones.The author of the article proposes an monitoring technique of a process vessel entire working volume by means of a special measuring module in the form of a sleeve with a built-in measuring instrument.Two versions of this device are considered: stationary and mobile.It is pointed out the importance of selecting the shape of the device in order to avoid mechanical resistance during the process fluid movement.The principle of operation is proposed for both implementations of the device comparison of the reference signals of the resonant sensor with the signal obtained from the trial measurement.The article is recommended for engineers involved in controlling of technological processes physical parameters in modern automated production.Modern automated productions require continuous monitoring of all the processes main technological parameters and especially a precise connection between sensors and control systems, including both the central processing systems and the control computers.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".