Peculiarities of Steam Treatment of Edible Plant Raw Materials and Simulation Thereof
Bibliographic record
Abstract
Abstract Nowadays, methods of treating dispersed edible raw materials with water steam under different pressures are widely used in the food processing industry. The conditioning, drying and blanching to achieve a certain humidity and/or temperature are typical non-steady processes taking place at either constant or varying (decreasing) pace. This article describes an analytical solution of a differential equation system for joint heat and mass transfer that describes distribution of heat and moisture fields on the stage of material heating. The following peculiarities of treating edible raw materials have been taken into account to set an appropriate boundary value problem: certain objects of edible processed horticultural raw materials (dried harvested potatoes, washed fruits or pre-treated sunflower seeds prior to sunflower-seed oil expression) have been classified as a group of products with a tight spherelike fruit (seed) envelope, which is why we consider them a single homogenous and isotropic particle with averaged thermophysical properties; the temperature difference between the heated air and the treated material results in condensation and liquid film formation on the surface of the objects, which is why heat is spontaneously transferred from the condensate film to the body primarily due to thermal conductivity and moisture distribution from the periphery to the center. The closely related issues of storing agricultural plant raw materials are equally important. The rational natural resource management globally recognized as the dominant economy development trend involves the fullest possible use of the treated human-consumed plant raw materials. For instance, in most countries plant proteins outweigh animal ones in food patterns. In Japan, this ratio is 78.3 to 21.7, in Ukraine - 72.3 to 27.7, in the USA - 65.3 to 32.9, in Great Britain - 61 to 32.4; in the Federal Republic of Germany - 65.3 to 34.7; in France - 60 to 40; in Canada - 68.4 to 31.6; in China - 87.3 to 12.7; in Italy - 74.6 to 25.4. Proper storage of the collected agricultural raw materials at the stage of preparation for processing significantly contributes to the solution of this issue. Given a significant amount of root vegetables, grain and seeds in edible plant raw materials, it is evident why their storage conditions and modes are of interest to manufacturers of foodstuffs. Modern warehouses for storing such raw materials are equipped with various tools to ensure required hygrothermal modes and even to regulate composition of the environment in the rooms for direct placement of bulk root vegetables, grains and seeds. Therefore, the described issues of analytical assessment of the storage conditions for various bulk agricultural raw materials during steam treatment are rather relevant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".