MODELING AND OPTIMIZATION OF CONSTANT RETORT TEMPERATURE (CRT) THERMAL PROCESSING USING COUPLED NEURAL NETWORKS AND GENETIC ALGORITHMS
Bibliographic record
Abstract
ABSTRACT Coupled artificial neural network (ANN) models and genetic algorithms (GA) were applied for developing prediction models and for optimization of constant temperature retort (CRT) thermal processing of conduction heating foods. ANN prediction models were developed for process time (Pt), average quality retention (Qv), surface cook value (Fs), equivalent energy consumption (En), final temperature difference (Tg) at can center, and lethality ratio (p, heating/total lethality). The processing conditions as inputs for ANN models were as follows: retort temperature (RT = 110–140C), thermal diffusivity (α= 1.1–2.14*10−7m2/s), volume of can (V = 1.64–6.55*10−4m3), ratio of height to diameter of can (Rdh= 0.2–1.8), total desired lethality value (F0= 5–10 min) at can center and quality kinetic destruction parameters: decimal destruction time (Dq– 150–300 min) and their temperature dependence (zq= 15–40C). The data for training and testing ANN models were obtained from a finite difference computer simulation program. A second order central composite design was used for constructing the experimental data for training ANN models, while an orthogonal experimental design with 6 factors and 3 levels was used for the generalization of trained ANN models. ANN model linked Genetic Algorithms (GA) were employed for searching for the optimal quality retention and corresponding retort temperature, and for investigating the effects of main processing factors. ANN‐based prediction models successfully described the various outputs of CRT thermal processing (correlation coefficients: R2 > 0.98; relative errors: Er ≤ 3%). The coupled ANN‐GA models, verified under several typical processing conditions, could be effectively used for optimization of CRT thermal processing. The main processing conditions and their interactions in the order of their importance with respect to the optimal quality retention and corresponding retort temperature were: V >zq >Rdh >; and zq >Fd > Rdh >V, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".