Experimental methods in chemical engineering: Pressure
Bibliographic record
Abstract
Abstract Pressure and temperature are the most important state variables for monitoring physicochemical processes to detect deviations that might lead to explosions and to verify levels, flow rate, and solids/gas hold‐up. Pressure fluctuations in multi‐phase systems identify regime changes and flow anomalies. Pressure signals are the first indicator of a process upset and are tied into distributed control systems (DCS) to sound alarms when they drift to high or low and activate safety interlocks in the case of high, high‐high, low, and low‐low conditions. To maximize the information, it requires that pressure gauges (transducers) are installed and calibrated precisely. Pressure measuring devices include manometers, aneroid devices like bellows and Bourdon gauges, and electronic instruments—piezoresistive, piezoelectric, and capacitive. The electronic elements have the advantage of higher precision and faster response times to measure fluctuations. The Bourdon gauges are standard equipment for pressure regulators and are mounted on the exterior of vessels and pipes to facilitate visual inspections. Over 2 million articles indexed by the Web of Science Core collection mention pressure, and in 2021, chemical engineering ranks had over 7000 articles—only multidisciplinary material sciences and energy and fuels had more. A bibliometric analysis identified five research clusters: temperature, combustion, and kinetics; separation, membranes, and energy efficiency; carbon dioxide (capture and storage), water, and thermodynamics; methane, adsorption, and transport phenomena (e.g., diffusion and permeability); and modelling, optimization, and computational fluid dynamics (CFD).
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.009 |
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".