N] EVALUACIÓN DE REPERCUSIONES SOBRE LA RED NATURA 2000
OBJETIVOS DE CONSERVACION POTENCIALMENTE AFECTADOS 6310 – Dehesas
The basic objective of thermal processing is to meet the safety requirements while trying to reduce quality degradation to a minimum. Theoretically, it is possible to design an optimal processing protocol for any food product, but in practice, it is difficult to obtain truly optimal results since considerable deviations exist in process parameters. In cases where deviations go beyond a certain critical level, there can be underprocessing or overprocessing. The former indicates that pro- cessed products cannot meet the sterility requirements for safety and consump- tion, while the latter means that quality destruction is more than optimal. There- fore, it is important to identify critical factors, to assess the effect of their deviations on the process calculations, and to establish control actions during thermal processing to avoid process deviations.
Thermal processing is a complex system, and standard processes are estab- lished based on achieving a target process lethality (F value) at a critical point, usually the package center. The required process time (PT) for a given product depends on the retort temperature (RT), product initial temperature (Ti), cooling water temperature (Tw), and several product-related properties, such as heating rate index ( fh), heating lag factor ( jh), and cooling lag factor ( jc). It is necessary to understand and estimate the influence of these process parameters and the deviations from their expected values on the required process time. Chen and Ramaswamy33 developed ANN models for (1) evaluating the relative order of importance of different critical control variables with respect to process calcu- lations, and (2) developing predictive models to compensate for their deviations. The critical variables studied were retort temperature, initial temperature, cool- ing water temperature, heating rate index, heating lag factor, and cooling lag factor. Their ranges of deviation from a set point were selected as –2 to 2°C for RT, –5 to 5°C for both Tw and Ti, –2 to 2 min for fh, and –0.2 to 0.2 for both jc and jh. ANN models were developed and used for analysis of different critical variables with respect to their importance on the accumulated lethality, process time, cooling time (CT), and total time (TT) under the given processing conditions. By use of ANN models, the relative orders of importance of critical variables within the deviation ranges were as follows: for F, RT > fh > jh > Ti× Tw > Ti > Ti× fh > RT × Ti > jc > RT × jh; for PT, RT > fh >> jh > Ti > jc > Ti×
Tw; for CT, jc > Tw > fh; and for TT, RT > fh > jh > jc > Tw > Ti > Ti× jc > Ti×
Tw. The accepted deviation ranges for various input variables under given control ranges were predicted by NN models, one of which is shown in Figure 4.9.
Based on these graphs, it can be easily determined that when the desired F value was set at 6 ± 0.5 min, the maximum acceptable deviation ranges of different variables were ±0.3°C for RT; ±4°C for Ti; ±0.1 for jh; ±0.8, ±1, and
±1.2 min for fh at fh = 20, 40, and 60 min, respectively; and ±0.4 for jc. Neural network models were also used for analysis of the combination effect of mul- tiple deviations on F, PT, and CT (shown in Figure 4.10). By use of this graph, the maximum changes in F and PT for different deviation combinations could be easily determined.
4.6 CONCLUSIONS
As confirmed by a variety of applications reported, the modeling capability of ANNs is not a question; they can be used for complex cases with multiple variables and nonlinear relationships usually too difficult for conventional meth- ods. In food thermal processing, application of ANN is still relatively new to other academic areas.Although a few studies have been reported, as mentioned in this chapter, about ANN for modeling, optimization, and critical control points analysis of thermal processing, most of them are still on the hypothesis level, meaning that these results have not been used for industrial applications. Fur- thermore, the online use of neural networks in the thermal processing area is still blank. Therefore, there is more room for researchers to make efforts on application of neural networks in the thermal processing area.
It should be noted that ANNs are not without limitations. First of all, neural networks work like a black box; thus, ANN models cannot give clear internal relationships between input and output variables as provided by other models
FIGURE 4.9 Acceptable deviation ranges predicted by ANN models for heating rate index, fh.
0 1 2 3 4 5 6 7 8 9 -3 -2 -1 0 1 2 3 Deviation of fh(min) F (min)
20 min 40 min 60 min
20 min 40 min 60 min
based on conventional methods. Therefore, neural networks should be used as a tool for practical purposes rather than theoretical ones, focusing on devel- oping and understanding the intrinsic relationships of various variables. For the practical application, the objective of developing ANN models is that they are to be used for different purposes such as optimization, online control, identification, etc. In order to achieve this goal, neural networks must be combined with other techniques, for instance, fuzzy logic, expert systems, and genetic algorithms or other search techniques. Therefore, the future trend for application of neural networks should be developing hybrid methods by using neural networks and other techniques that may have more potential for direct use for industrial purposes, instead of staying at the level that only confirms the feasibility of ANN modeling, as most current works have done. Second, the training ANN model needs enough data, which is the most important factor affecting the performance of ANN models. It is impossible to obtain an ANN model with a good performance using limited or bad distribution data. Thus, neural FIGURE 4.10 The comprehensive effects of multiple deviations predicted by ANN models:
(a) lethality value, and (b) heating time.
(a) 2.92 4.3 4.91 5 5.17 -1.98 -2.61 -2.89 -2.98 -3 -4 -2 0 2 4 6 RT, fh RT, fh,jh RT, fh,jh, Ti RT, fh,jh, Ti, jc RT, fh,jh, Ti jc, Tw Types of combination of deviations
Changes of F value (min)
+ - (b) -5.1 -7.3 -8.6 -8.5 -8.9 9.5 8.4 8.1 5.6 9.1 -15 -10 -5 0 5 10 15
Changes of process time (min)
+ -
RT, fh RT, fh,jh RT, fh,jh, Ti RT, fh,jh, Ti, jc RT, fh,jh, Ti jc, Tw Types of combination of deviations
networks are only suitable for problems with a large amount of experimental data, or those that can generate data using a separate computer simulator. In addition, like all other models, trained ANN models can only be used for predictions within the ranges of the variable being investigated. Otherwise, the precision of prediction results by ANN models might not be guaranteed.
NOMENCLATURE
Variables
CT Cooling time, min
D Decimal deduction time, min
Dq Decimal destruction time for quality, min
En Equivalent unit energy consumption, kJ/kg
Er Relative average error, %
E Time-specific particle concentration function or total square error
F Accumulated lethality value, min, or cumulative
particle concentration function
Fs Surface cook value, min
F Heating or cooling rate index, min
j Heat or cooling lag factor
g Final temperature difference between can center and
retort, °C
h Transfer coefficient, W/(m2°C)
H Height of the can, mm
PT Process time or heating time, min
Qv Average quality retention for whole can, %
R Correlation coefficient or ratio of diameter to height of can
RT Retort temperature, °C
T Temperature, °C
T1–T4 Step temperature for MRV function, °C
U Overall heat transfer, W/(m2°C)
V Volume, m3
w Weight (neural network)
y Output value
Subscripts
c Cooling
di Desired output values
dh Diameter to height
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h Heating
i Index, or initial
j Index
m Microorganism, or mean value
max Maximum min Minimum o Desired value q Quality w Cooling water Greek Symbols
a Thermal diffusivity, m2/sec
q Threshold value (neural network)
r Density, kg/m3, or lethality ratio
e Error
Abbreviations
ANN Artificial neural network
CCP Critical control point
CDT Come-down time
CRT Constant retort temperature
CUT Come-up time
GA Genetic algorithm
MRV Multiple-ramp variable
RTD Residence time distribution
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