A study of a self-organizing controller is implemented in a way that response to controlled system follows the desired given by the model. The self-organizing controller has proven to be a valuable tool in sterilization equipment in order to verify the capacity of the response to any change in the pressure or temperature. Basically, this type of controller is based on the Self-Organizing Map (SOM) that is a neural network algorithm of unsupervised learning. The new ideas include clustering visualization, interactive training and one-dimension arrays.

1. Practice Article
Technical note: Advantages of the self-organizing controller
for high-pressure sterilization equipments
V. Pilipovik a
, C. Riverol b,n
a
AIChemEng Research and Development Group, J.C Engineers & Partners, Av. Andres Bello, Edif. Las Rozas, Urb. La Florida, Caracas, Venezuela
b
Chemical Engineering Department, University of the West Indies, St. Augustine Campus, Trinidad, Trinidad and Tobago
a r t i c l e i n f o
Article history:
Received 9 February 2012
Received in revised form
3 July 2013
Accepted 7 July 2013
Available online 29 August 2013
This paper was recommended
for publication by Dr. A.B. Rad
Keywords:
Spores
SOM
Sterilization
Adjustment mechanism
Controller
a b s t r a c t
A study of a self-organizing controller is implemented in a way that response to controlled system
follows the desired given by the model. The self-organizing controller has proven to be a valuable tool in
sterilization equipment in order to verify the capacity of the response to any change in the pressure or
temperature. Basically, this type of controller is based on the Self-Organizing Map (SOM) that is a neural
network algorithm of unsupervised learning. The new ideas include clustering visualization, interactive
training and one-dimension arrays.
& 2013 ISA. Published by Elsevier Ltd. All rights reserved.
1. Introduction
High-pressure processing has emerged as an attractive processing
technology for preservation of foods. Since the introduction of high
pressure processing in 1990 [1,2] the range of products has gradually
expanded. The effect that this type of processing has on microorgan-
isms is similar to pasteurization while the product retains its fresh or
just-prepared appearance and nutritional quality [1,2]. High pressure
processing causes minimal changes in the ‘fresh’ characteristics of
foods thereby eliminating the processing by thermal degradation.
Compared to thermal processing, high pressure sterilization (HPS)
results in foods with fresher taste, and better appearance, texture and
nutrition. The technology is especially beneficial for heat sensitive
products.
A recent innovation in sterilization of food products using high
pressure is the complete inactivation of vegetative micro-organ-
isms, as well as spores, resulting in ambient stable products
shown in [2,3]. High pressure sterilization is a combined process
where both pressure and temperature contribute to sterilization.
A good control system is necessary for keeping the temperature
and pressure in the adequate values with less adverse effects
on product quality. In this paper a self-organizing controller is
introduced as a hierarchical structure in which the inner loop is
a table-based controller and the outer loop is the adjustment
mechanism. The Self-Organizing Map (SOM) is especially suitable
for data analysis [4] where the application in process control is
focused on training neural networks [4–6]. It creates a set of
prototype vectors representing the data set and carries out a
topology preserving projection of the prototypes from the
d-dimensional input space onto a low-dimensional grid. This
ordered grid can be used as a convenient visualization surface
for showing different features of the SOM. In its basic form, it
produces a similarity graph of input data. It converts the nonlinear
relationships into simple geometric relationships [7,8].
2. Laboratory set-up and results
Destruction of Clostridium sporogenes spores by high pressure
treatment was used as the study case. In the laboratory, a
sterilizing stainless steel tank of 4 L capacity and 1.5 m length
was used, see Fig. 1. The tank has 3 PT100 for monitoring the
temperature at the center of the tank. One of the PT100 is located
at the top of the tank, another at the middle and the last one at the
bottom. The accuracy of the sensor is 70.1 1C. The controller
received one value (the average of the three temperatures)
because the values from the top to the bottom do not differ more
than 1 1C.
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ISA Transactions
0019-0578/$ - see front matter & 2013 ISA. Published by Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.isatra.2013.07.007
n
Corresponding author. Tel.: +1 8686622002.
E-mail address: Carmen.Riverol@sta.uwi.edu (C. Riverol).
ISA Transactions 53 (2014) 186–188

2. The first step is to justify the SOM ability analytically. The first
approach was to restrict the consideration to one-dimensional
linear unit with which each scalar valued input signal is connected.
The SOM control system (SOMCS) adapts the system in accordance
with the desired response [7–9]. Each sample is recording and the
deviation of desired state is evaluated. An example of the best
match is shown in Fig. 2. According to the kinetics of the spore, the
adiabatic temperature can increase from 2.2 to 9.3 1C/100 MPa
approximately; thus the equipment was tested using three pres-
sure pulses of 100, 300 and 600 MPa for 1, 3 and 5 min each, as
reported in Table 1. Nevertheless, the importance of the minimum
product temperature for the degree of spore inactivation confirms
that the temperature should be monitored during high pressure
sterilization.
On the other hand, the SOM neural network was designed
using cluster membership for test data sets that include various
levels of data dispersion combined with outliners.
A clustering Q means partitioning a data set into a set of
clusters Q [10–12]. In crisp clustering, each data sample belongs to
exactly one cluster. Fuzzy clustering is a generation of crisp where
each sample has different degrees of membership. However, this
leaves much room for variation within and between cluster
distances and can be defined as follows:
dsl ¼ minjjxiÀxjjj ð1Þ
dcl ¼ maxjjxiÀxjjj ð2Þ
dml ¼
∑i;jjjxiÀxjjj
NiNj
ð3Þ
outliner ¼ jjciÀcjjj ð4Þ
where N is the total value inside the cluster i or j, dsl is the single
linkage, dcl is the complete linkage, dml is the average and outliner
is the distance between centers shown in [9] and [10].
An example of the cluster definition results is shown in Fig. 2
where the deviation is indicated (d). The idea behind the adapta-
tion is to let the adjustment mechanism update the values in the
control table F on the basis of the current performance of the
controller as reported in Fig. 3. The adjustable neural network
taught in previous simulation is inserted in the SOC using the
model M. This model was empirically calculated. The adjustable
neural network is tuned with each experimental result in the plant
(P). Adiabatic heating is the uniform temperature rise within the
product, which is solely caused by pressurization [7]. The control
Fig. 1. The equipment used in the laboratory.
Fig. 2. Cluster definition results.
Table 1
Experimental set-up and temperature control (SOC).
Pressure
(MPa)
Target
temp (1C)
Initial
temperature
(1C)
Mean
temperature
achieved (1C) SOM
Mean
temperature
achieved (1C) PI
100 70 40 70.8 71.2
85 55 85.6 85.9
100 60 101 99.6
300 70 41 70.1 71.2
85 58 84.9 84.6
100 62 100.6 101.3
600 70 51 71.2 71.6
85 56 85.3 85.9
100 66 100.3 100.7
V. Pilipovik, C. Riverol / ISA Transactions 53 (2014) 186–188 187

3. system was implemented using MATLAB 7.0, real time toolbox and
MF624 multifunction I/O card. A SOM was trained using the
sequential training algorithm [8]. All maps were linear. In the
agglomerative clustering (see Fig. 2), single, average and complete
linkages were used in the construction phase. For additional
information about SOM, readers are referred to [5]. In this article
the training time was 32 h using 10,000 values collected from
2005 to 2010. The input values were obtained during the last
5 years because this equipment worked 35 years without any
control system.
How to tune the gains does deserve attention, since they may
finally stand in the way for a successful implementation. The SOC
works ‘surprisingly well’ [13]. For example, the output gain is
lowered between two training sessions. The adjustment is com-
pensated by an F-table with numbers of larger magnitude [13–15].
Therefore it is possible to start with a linear F-table, and set the
gains loosely according to a PID tuning rule or hand-tuning. This is
a good starting point for self-organization.
The SOM network can improve the quality of decisions in
cluster analysis especially in non-uniform cluster densities
[5,6,13]; for example, temperature is a parameter that changes
very fast; thus some of empirical data can become “messy data”.
Fig. 4 depicts an example in which the SOM controller offers a
better performance than the PI controller. Basically, the overshoot
is reduced and the setting conditions are attained early. The
highest temperature with the SOMCS is about 19.5 1C lower than
that with PI as reported in Fig. 4. The same behavior was observed
24 times during the testing sections. The reduction of the
overshoot can be translated in a reduction of the steam consump-
tion; thus some hundreds of dollars can be saved. Also an over-
shoot over 10 1C is not desirable because the food can become dry
and lose its texture because of which designing of new controllers
may continue in this area. Table 1 indicates that the SOMCS offers
a better performance over PI in this system and shows that the
results are not sensitive to the initial learning coefficients at
different initial conditions. In summary, the new control system
shows a good performance and can also improve the traditional PI
controller; nevertheless, future research should focus on reducing
the training time for the SOM because its implementation on the
equipment consumes a long time.
3. Conclusion
This paper presented the development of a SOM controller and
its application to a high pressure sterilization equipment. The
algorithm presented a satisfactory performance and efficiency. The
simulation showed that it is possible to achieve a good behavior
after few steps of learning using a simple model. The implementa-
tion in the sterilization equipment improves the response capacity
of the control system although the implementation of the self-
organizing system consumes time and is complex. The adjustable
control table (F) can be increased using more data for improving
the accuracy; however a good computer should be used for
reducing the time consumption (the computer used in this article
has a 2GB RAM only).
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Fig. 3. SOC control system. The solid line indicates the field data and the dash lines
indicate the calculated values.
0 5 10 15 20 25 30 35 40 45 50
0
20
40
60
80
100
120
Time (min)
Temperature(oC)
PI
SOM-NN
Fig. 4. Behavior of the temperature using different controllers. P¼600 MPa.
V. Pilipovik, C. Riverol / ISA Transactions 53 (2014) 186–188188