Using Liquid-Only Cans (Equipped with a Single Particle) to Quantify Heat Transfer Phenomenon During Thermal Processing
Bibliographic record
Abstract
Abstract This study utilizes liquid-only cans (fitted with a single particle) to gain insight into the heat transfer phenomenon during the novel process of reciprocating agitation thermal processing (RA-TP) for sterilization of food. Overall heat transfer coefficient (U) across the can-wall was evaluated for cylindrical cans, filled with different concentration of glycerin and treated with reciprocating agitation thermal processing (RA-TP). Thermocouple-equipped single spherical particle (diameter = 0.019 m) of various densities were also kept inside the cans to obtain preliminary insights into the heat transfer phenomenon at the liquid–particle interface (h fp ). Seven process variables, viz. operating temperature (110–130 °C), reciprocation frequency (1–4 Hz), reciprocation amplitude (0.05–0.25 m), can headspace (0.006–0.012 m), liquid viscosity (0.001–0.942 Pa.s) and particle density (830–2,210 kg/m 3 ), were varied according to three full-factorial designs and corresponding U & h fp were reported. Depending on the processing condition and product composition, U and h fp varied in the range 197–1,240 W/m 2 K and 210–1,230 W/m 2 K respectively. Higher heat transfer was observed at both can wall and liquid–particle interface with increasing temperature, headspace, frequency and amplitude and decreasing liquid viscosity. The order of heat transfer coefficients for processing conditions was: Frequency > amplitude > headspace > temperature; while for product composition was: Frequency > liquid viscosity > product density. This study is relevant for providing data for process modeling of reciprocating agitation thermal processing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".