4th DAAAM International Conference on
Advanced Technologies for Developing Countries
September 21-24, 2005
Slavonski Brod, Croatia
OPTIMIZATION OF PROCESS CONTROL IN DRY SUGAR BEET
PULP PRODUCTION
V. Galzina and T. Šarić
Optimization, process control, neuro-fuzzy logic, rotary drying
1. Introduction
Sugar beet pulp is one of extra product and one of last stages in sugar production from sugar
beet. Sugar beet pulp is used in two forms: as wet (raw) pulp and as dried pulp. There is various
usage of this product; it is mostly used as highly nutritious cattle food in both forms and wet pulp is
used as acidity regulator for soil. It can be used as source of celluloses for paper industry, as
alternative fuel and many others. For conserving sugar beet pulp generally are used silage and
drying. There are few drying technologies developed for drying of sugar beet pulp. The most
common drying technology used in dry sugar beet pulp production is direct rotary drying.
Constant drive of making production more automated and better controlled in all aspects is
drag force for development of more sophisticated control systems. One of approaches for achieving
these goals is improving existing process control systems.
Conventional control theories are well suited in applications where process itself can be
described in advance to the reasonably acceptable level. But if process is hard to describe and
characterize or process is subject of not fully determined internal and external disturbances it
becomes very hard to apply conventional control technologies. Drying process is one of those:
problem of heat and mass transfer and the mechanical phenomena in moist materials are highly
non-linear and non-stationary even in stable drying control conditions because the mechanism of
moisture transport changes itself during drying process [3].
Neural and fuzzy control and their hybrids are proven to be well suited control technologies in
many applications, especially in cases of mathematical non-determination and process unknowns
[1, 2, 4, 7]. Purpose of this work is to demonstrate potential of neuro-fuzzy control in drying
process optimization.
2. Operation of direct rotary direct co-current dryer
Rotary dryer or in this case “tumbling dryer” is used for drying of wet sugar beet pulp. Term
“tumbling” designates here that material is mechanically turned over during the drying process in
purpose of more even drying of particles from all sides. Driving force for material transportation is
vacuum achieved by usage of ventilator at the exit side of rotating drum. In this case dryer itself is
unheated and heat needed for vaporization of water is transported by means of preheated air (air
and products of burned fuel) in direct manner – usually called adiabatic convective drying. Word
co-current is describing longitudinal movement of material to be dried and drying stream in same
direction (see Fig. 1.). What is there to control? We can measure following continuous values:
Temperature: supplied wet pulp, input airs, drums temperature on different longitudes,
exit temperatures,
Flow: airs and fuel,
Quantity: feed and exit,
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-
Quality: dryness or wetness of input and output pulp,
Speed: rotation of drum.
Variable governing the whole process is exit dryness of dried beet pulp – that is the final
product. Normally, not only the dryness of product has to be achieved – minimal energy
consumption (fuel and electrical energy) with maximum possible speed of production are also
required. In order to develop control model this variables with disturbances and interactions have to
be taken into account.
wet sugar beet pulp feed
Exhaust air
Fuel
Primary Air
Rotation
Heated air stream
Burning
chamber
Beet pulp
Dried beet pulp
Secondary Air
Figure 1. Direct rotary co-current dryer scheme
2.1 Basis of drying process
As it was pointed in introduction drying process thermodynamics and mechanics are covered
in various works [3] and represent one of “not yet” completely solved problems. For even getting
near exact mathematics of drying approximations and simplifications are required. Nevertheless,
for control purposes some basic considerations are necessary. Let’s say that wet sugar beet pulp is a
porous solid. In that case when wet porous solid is treated with heat we have three constituents:
porous solid (sugar beet pulp), liquid (water) and gas (mixture of water vapour and dry air). In case
of adiabatic dryers where the heated air is source of heat for vaporization of moisture we can say
that its enthalpy (energy content) does not change, because the sensitive heat loss is equalled by
rise of moisture content during the process of evaporation. For longitudinal co-current adiabatic
dryers temperature profile is given in Fig. 2. [5].
Temperature
TI
Air
TO
E
TPO
D
Solids (const. rate phase)
Twb
TPI
C
B
A
Figure 2. Temperature profiles in longitudinal co-current adiabatic dryer
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Heated air stream enters rotating drum at some high temperature (TI) and leaves at an outlet
temperature (TO). Bulk of wet beet particles enters drum at some lower temperature (TPI) and leaves
with outlet temperature (TPO). It should be emphasized that for this type of product and this type of
dryer, we can keep enter temperature of air high because the solids temperature is limited to wetbulb temperature (Twb) as long as surface of pulp is wet.
3. Proposed control solution
A conventional control for this type of dryers is closed-loop control with use of PID algorithm
controllers. This type of control is insufficiently flexible in case of process and demands
fluctuations and demands. This causes constant need of re-adoptions of controller’s setpoints
during the work [6]. In some situations inexperienced operators make wrong control decisions and
cause energy waste, product lost and in worst cases damage to process equipment. Proposed control
solution is designed based on schematics and variables given in Head.2.
3.3 Fuzzy basis of solution
Since extensive works on theory of fuzzy logic are already done, this paper will just touch
theoretical fuzzy foundations of proposed control solution. Use of fuzzy logic is preferable when
there is no precise mathematical model of process and expert knowledge of process operation
exists. Fuzzy logic uses input variables in multi-dimensional manner subjecting them to linguistic
rules (IF-THEN rules) and producing output variables. For this way of using input and output
variables (quantities) fuzzy logic via techniques called “fuzzyfication” on one side. Fuzzyfication is
conversion of “non-fuzzy” input variables to fuzzy degrees of membership values (continues real
numbers from 0.0 to 1.0). And on the other side “defuzzification” is performed in reverse way.
Fuzzy degrees of membership resulting real values (form 0.0 to 1.0) are subjected to normalization
and conversion to the real output variable span.
First successfully implemented fuzzy control system for sugar beet pulp production [6] shows
how new approach is promising but still not doing the job completely right. Complexity and time
consuming of derivation fuzzy control rules, important role of expert knowledge (in some cases
and situations disputable) make fuzzy control hard to implement. Since of difficulties in
transforming human experiences and knowledge we must expand our search for better, more
optimal solution. One way is expanding fuzzy logic and bringing in the learning abilities.
3.1 Neural networks basis
A neural network is a computational structure developed as approximation of biological
neural structure found in human brain. Neural network consists of number of processing nodes
(interconnected elements that form the net) that produce outputs in dynamic response to inputs. In
this study we used multi-layered feed-forward model (see Fig. 3.).
wij
h1
X1
wjk
O1
O2
h3
Xi
Output vector
Input vector
h2
X2
Ok
hj
Figure 3. A tree-layer neural network with input, hidden and output layer
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For artificial neural network to give any results it must be trained with series of examples and
conditions. During the training neural network “learns” the governing relationships in given data
sets e.g. input vectors to produce right solutions e.g. output vectors. For this purpose, backpropagation training algorithm is used. It is an iterative algorithm for minimizing the mean square
error between predicted and desired output values. Back-propagation learning algorithm can be
summarized in this pseudo-code:
Initialize all weights and threshold values // small random values between -1 and 1
While ((max number of iterations < than specified) and (output layer error is > than specified))
1. For every i set the value input vector Xi //normalized to values between -0.9 to 0.9
2. For every k set the value desired outputs Ok//normalized to values between -0.9 to 0.9
3. For every j in hidden layer compute output hj
4. For every k in output layer compute output Ok
5. For every k compute output layer error δk //difference between computed and desired
6. For every j in hidden layer compute error δj
7. For every j and every k in output layer compute new weights and thresholds values
8. For every i and every j in hidden layer compute new weights and thresholds values
3.3 Neuro-fuzzy approach (ANFIS model)
For more control and replacing the human factor in more scale neuro-fuzzy hybrid control
scheme called Adaptive Neuro-Fuzzy Inference System (ANFIS). This system is developed by
Jung in early 90s [2] and in recent works shown to be reasonably acceptable [1, 4, 7].
Keeping in mind discussion above, we can say that the learning algorithm for ANFIS system
is composed of two phases (learning and optimization):
Forward pass: node output values are going forward until they reach Layer 4 (see Fig.
5.) and computed parameters are identified by the least square method,
Backward pass: calculated errors are backwards propagated and parameters are in that
way updated.
Possible ANFIS architecture is shown in Fig. 5. This arrangement represents five-layered
feed-forward neural network with two fuzzy if-then rules.
Layer 1
Layer 2
Layer 3
Layer 4
Layer 5
x
w1
w1
w
1f
1
A2

X1, X2
B1
X2
w
x
w2
O1
Output
Input vector
A1
X1
f2
2
w2
B2
Figure 5. ANFIS architecture with two fuzzy if-then rules
Assuming there are two fuzzy rules under consideration, with two inputs (X1 and X2 - Fig. 5.)
and one output (O1 – Fig. 5.) respectively, we would have rules, Eq. (1) and (2):
Rule 1: If X1 is A1 and X2 is B1 then f1, f1 = f (X1, X2)
(1)
Rule 2: If X2 is A2 and X2 is B2 then f2, f2 = f (X1, X2)
(2)
Layer 1: All nodes in this layer are adaptive. Output of each node is the degree of
corresponding inputs membership to the fuzzy membership function represented by the node.
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Layer 2: All nodes in this layer are non-adaptive. They are used as multipliers and their
outputs represent firing strength of the rule.
Layer 3: All nodes in this layer are non-adaptive. They are used for normalization of the
firing strength from previous layer.
Layer 4: All nodes in this layer are adaptive. Their output is product of normalized values and
corresponding functions (they are also called consequence parameters).
Layer 5: Here we have only one node and it performs the function of summing up outputs
from previous layer. The resulting output O1 is given by Eq. (3):
O1  w1 f1  w2 f 2 
w1
w2
f1 
f2
w1  w2
w1  w2
(3)
If parameters of fuzzy membership’s functions are fixed we have simple linear combination of
variable parameters – what enables us to use last-square method for identification of optimal values
of parameters of functions f1 and f2. Normally, if this is not the case, and we allow these parameters
to vary, convergence will consequently become slower.
3.4 Preliminary results
For this preliminary testing purposes reduced number of variables, fuzzy membership
functions, training and testing data sets was used (following recommendation that number of
training samples is minimally at least large as number of unknowns, i.e. number of variables in the
network). System is presented as first order Sugeno type system. Other constrain is that there is just
single output (obtained by using weighted average defuzzification (with linear output membership
functions)). Comparisons with conventional neural network system results and classical PID
control system using mean-square error (MSE) criteria (see Table. 1. [7]), show this systems
advantages. However, it has to be proven in real sugar drying application and than again evaluated.
Table 1. Comparison between three control systems
Control system
PID
Neural Network
ANFIS
Description
Closed-loop configuration
Feed-forward
First order Sugeno fuzzy model
Results (MSE)
0.505
0.321
0.186
4. Conclusions and future work
In this paper, a neuro-fuzzy control system and its application to the sugar beet pulp drying
were discussed. Using of fuzzy logic system alone has shown its advantages, but it’s dependable on
real process that was made for. If you want to use finished fuzzy system on somewhat different
process configuration someone will probably face incompatibility. As preliminary results has
shown this approach have his advantages: usage of already present sensors and data collected,
getting the best of operator’s knowledge of process, flexibility in terms of sudden and unexpected
changes in process, ability of using different control configurations in different work regimes.
Conventional neural networks require lengthy learning (or training) then hybrid algorithms. One of
those is introduced ANFIS system, and preliminary results have shown as a good path to fallow. As
mentioned earlier, this type of control system does not require any information about controlled
system itself and shows good robust results for non-linear and non-stationary of subjected process.
The goals of this work were accomplished: we found better – more optimal control system witch
could replace existing conventional control system of a dryer observed. Naturally, this is not the
final solution and this model need to be further tested and probably some modifications have to be
made in learning department and rules reduction. A future research includes expansion of neurofuzzy control model and testing it on real installed industrial system and laboratory sized rotary
drum co-co-current dryer.
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Acknowledgement
The authors gratefully acknowledge the Sladorana d.d. Županja Sugar Factory staff (especially, Šimo
Kladarić, B.E.E.) for their data support and Mechanical Engineering Faculty in Slavonski Brod for their
support.
References
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[2]
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[4]
[5]
[6]
[7]
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Vjekoslav Galzina, B.Sc.Mech.Eng., Assistant
Machanical Engineering Faculty/University in Osijek, Trg I.B. Mažuranić 18, Slavonski Brod, Croatia,
Telephone: 0038535446718, Telefax: 0038535446446, e-mail: [email protected]
Tomislav Šarić, Ph.D.Mech.Eng., Assistant Professor
Machanical Engineering Faculty/University in Osijek, Trg I.B. Mažuranić 18, Slavonski Brod, Croatia,
Telephone: 0038535446718, Telefax: 0038535446446, e-mail: [email protected]
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