Thermal Food Processing

120 Thermal Food Processing: New Technologies and Quality Issues
E(t)
E(t)
C = 4%, V = 10 kg/min, S = 60 mm
0.4
0.35
Experiment
0.3
0.25
0.2
0.15
0.1
0.05
ANN model
0
0 1 2 3
Normal time
FIGURE 4.5 Comparison of ANN model predictions and experiments for E and F functions.
Genetic algorithms (GAs) are a combinatorial optimization technique, searching
for an optimal value of a complex objective function by simulation of the biological
evolutionary process, based on crossover and mutation, as in genetics.
Chen and Ramaswamy 30 found that the combination of GA and ANN models can
become an effective tool for optimization problems. This study might be the first
report on application of ANN and GA for thermal processing optimization. The
focuses of this study were on:
1. Developing ANN models for predicting process time (PT), average
quality retention (Qv), surface cook value (Fs), equivalent unit energy
consumption (En), temperature difference (g), and ratio of F value
from heating to total desired F value (ρ) under constant retort temperature
(CRT) processing conditions
2. Coupling ANN models and GA to search for the optimal quality retention
and the corresponding retort temperature
3. Investigating the effects of main processing parameters on both optimal
quality retention and retort temperature
Processing conditions as inputs for ANN models were selected as follows:
retort temperature (RT = 110 to 140°C), thermal diffusivity (α = 1.1 to 2.14 ×
10 –7 m 2 /sec), volume of can (V = 1.64 to 6.55 × 10 –4 m 3 ), ratio of height to diameter
F(t)
C = 5%, V = 10 kg/min, S = 60 mm C = 5%, V = 15 kg/min, S = 130 mm
0.3
1.2
0.25
Experiment
ANN model
1
0.2
0.8
Experiment
0.15
0.6
ANN model
0.1
0.4
0.05
0.2
0
0 1 2 3 4
0
0 1 2 3 4 5
Normal time
Normal time
F(t)
C = 4%, V = 10 kg/min, S = 60 mm
1.2
1
0.8
0.6
Experiment
0.4
ANN model
0.2
0
0 1 2 3
Normal time