Yth. Peserta ZOA-BIOTEK-2001,

Sekalipun sebentar lagi sesi Bioteknologi Industri ini akan berakhir,
dan diganti sesi Bioteknologi Lingkungan, izinkan kami memforwardkan 
makalah yang mungkin terkait dengan bioteknologi industri.

Makalah Poster yang satu ini, bisa dikatakan salah satu aspek penelitian 
interdisiplin, karena melibatkan ilmu kontrol dan ilmu bioteknologi.
Makalah ini berjudul "ADAPTIVE FUZZY-BASED EXPERT SYSTEM ON  PALM 
OIL CRYSTALIZATION PROCESS", yang dibawakan oleh Bapak Son Kuswadi,
dkk dari Institut Teknologi Sepuluh Nopember (ITS) Surabaya. Bapak 
Son Kuswadi, dengan pengalaman publikasi ilmiah yang begitu banyak,
kini tengah menjalani studi doktoralnya di Tokyo Institute of Technology 
(TITech) Japan.

Baik, silakan menyimak makalah beliau! Namun, sayang sekali dalam 
modus teks email, mungkin persamaan matematis dan gambar tidak dapat 
terlihat jelas. Untuk makalah lengkap disertai gambar-gambarnya, 
silakan melihat ke
http://sinergy-forum.net/zoa/paper/html/postersonkuswadi.htm

Moderator

Dedy H.B. Wicaksono

=================================================


ADAPTIVE FUZZY-BASED EXPERT SYSTEM ON  PALM OIL CRYSTALIZATION PROCESS

@

1,2Mohammad NUH, 2,3Son Kuswadi, 2Anang Tjahjono, 2Riyanto Sigit,
1Rusdhianto E.A.K.
1Electrical Engineering Department, Industrial Technology Faculty 
2Electronic Engineering Polytechnic Institute of Surabaya
Institut Teknologi Sepuluh Nopember, Surabaya Indonesia  
3Tokyo Institute of Technology, Tokyo, Japan

Abstract: 

In Crude Palm Oil (CPO), there are two main substances: olein and 
stearin. Olein as palm oil should be fractionated from stearin through 
crystallization process, which temperature is the main control variable.
To obtain the good quality of olein, the change of temperature of 
CPO should follow time-varying crystallization curve as references,
namely Bernardini protocol [1]. Crystallization process is accomplished 
by controlling CPO temperature according the above curve.  This paper 
will describe the implementation of AFS-based expert system to replace 
the manual control done by skilled operator. Operator using his/her 
experience will control to maintain CPO temperature according to 
the crystallization curve and then will produce the good crystal.
AFS-based expert will elearnf from the operator for some operating 
condition. After esmartf enough, AFS could automatically control 
the process. In addition, the modification was done to reduce the 
using of three kinds of cooling water into two. Therefore, the power 
consumption is reduced significantly. Laboratory test result shows 
that olein produced by this process is meet the quality required.


Keywords: process control, adaptive-fuzzy-system, palm oil crystallization,
human-based expert

1. INTRODUCTION

Palm (Elaeis guineensis Jack) is one of the important Indonesiafs 
non-oil exports not less than US$ 203.5 million/year. However, in 
palm oil form, Indonesia just produces 2,007,000 tons/year, comparatively 
small than Malaysia did (6,084 ton/year). In fact, if we consider 
the land available, that figure could be increased. The constraints 
are, among others, only few palm oil processing industries available 
and its dependency to the foreign technology. The using of accurate 
and robust process automation will increase the palm oil productivity.
Most of existing processes (in this case the crystallization process) 
still employ manual control by well-experienced operators, hence 
its quality and quantity consistency are questioned. Therefore, we 
conduct the research on an implementation of crude palm oil process 
control to improve the process. In this research, the using of computer-
based control system of palm oil processing is studied, especially 
its protocol crystallization process development and its adaptive-
fuzzy system (AFS)-based expert system. AFS controller was chosen 
because of its ability on modeling of operator's expertise, meaning 
that AFS could ecopyf the operatorfs expertise. In other words,
AFS controller at first will learn from operator how to control 
the process. After they esmartf enough, AFS will take over the 
controlling process from operator. The research result shows that 
the developed system could produce the better product thanks to its 
ability on continuous learning, fuzzy reasoning process and quality 
consistency. The experimental result shows that the AFS-based learning 
process of Bernardini protocol by only 100 epochs (every epoch need 
only 3 minutes), 15 fuzzy rules and 0.5 constant step size could 
be realized well. 

Sugeno and Murakami [2] proposed the fuzzy-based human expert modeling,
to control model car. Several researchers propose an application 
of adaptive fuzzy system for the expert modeling, see for example 
the truck backer-upper control problem [3], flexible arm structure 
[4], automatic docking of vessel [5] and four-fan helicopter control 
[6].

In this study we will use a adaptive fuzzy system structure proposed 
by Wang [3] since it is easy to implement and easy learning process.


2. REVIEW OF ADAPTIVE FUZZY SYSTEM

Following Wang [3], the adaptive-fuzzy system (AFS) and its back-
propagation learning will be introduced briefly. 

Suppose that we are given an input-output pair (xp,dp), xpU Rn, dpVR;
our task is to determine a fuzzy logic system f(x) as shown in Fig.
1 in the form :



such that :



is minimized. We assume that ail = 1 and M is given; therefore, the 
problem becomes training the parameters , , and  such that ep of 
(2) is minimized. In the following we use e, f, and d to denote ep,
f(xp), and dp, respectively.

To train we use:



where l=1,2, ....., M, k=0,1,2,........, and a is a constant step 
size. From Fig. 1 we see that f(and hence e) depends on only through 
a, where f=a/b , and

 



Hence using the chain rule we have



Substituting (4) into (3) we have 



where l=1,2, ..., M, and k=0,1,2,.......

To train  we use



where i=1, 2, .... n, l=1,2,....., M, and k=0,1,2,..... We see from 
Fig. 1 that f(hence e) depend on  only through zl; hence using the 
chain rule, we have



Fig. 1 Adaptive-fuzzy system representation



Substituting (7) into (6), we obtain training algorithm for: 



where i=1,2,......n, l=1,2,......, M, and k=0,1,2,........

Using the same method as previously, we obtain the following training 
algorithm for 



where i=1,2,....n, l=1,2,...., M, and k=0,1,2,...........

3. HUMAN-BASED EXPERT LEARNING STRATEGY

The main idea of learning strategy by employing human-based expert 
is simply using operatorfs skill as teaching pattern to any adaptive 
systems (whether ANN, fuzzy or combination of them) [7].

In the learning stage, operator will control the plant meanwhile 
the AFS is learn how the operatorfs control action based on predetermined 
input to both AFS and operator (of course, input to operator just 
only to imitate the input-output relation of operatorfs action),
see Fig. 2.



Fig. 2 Learning stage: AFS learn from human expert (operator)fs 
skill to control a plant

After considering that the AFS is esmartf enough, meaning that 
the learning process is long enough (by trial and error method), 
and then AFS is ready to control the plant by itself. We shall call 
it as eindependentf stage. See Fig. 3.



Fig. 3 Independent stage: AFS control a plant by itself.

4. PALM OIL PROCESS CONTROL

The quality of palm oil is depending on fractionation process that 
consists of crystallization and filtration activities. Crystallization 
is a cooling process of CPO that makes some of its part changed into 
crystal form and then this process continues to separate it into 
two forms. First, known as olein (to be used as cooking oil) in liquid 
forms. Second, it remains in crystal form known as stearin to be 
used as foodstuff and chemical industry. 

Initially CPO is preheated up to 700C and then stored at crystallization 
tank through oil pipe. 

There are two ON-OFF valves to control the water inlet to chiller.
To make the process to be controlled in almost same state as possible 
(to avoid the distributed process control that is impractical and 
expensive), mixer is used, and simple controller controls its speed.


The main variable to be controlled in the crystallization is, of 
course, temperature in cooling tank. Temperature to be controlled 
should decrease following a curve that known as Bernardini protocol 
[1], see Fig. 4.

In conventional method, operator will control the tank temperature 
by changing the cooling waterfs valve manually according to the 
above protocol. It is clear that the said process could not provide 
any guarantee of product quality; since it is depend on emoodf 
of operator.



Fig. 4 Bernardini protocol, the cooling trend should follow this 
decreasing pattern.

In our proposed method, in other hand, will record the operator control 
action to be used in learning stage. Fig. 5 shows the experimental 
set up to implement the proposed idea.

During the learning, operator will control the cooling waterfs valve 
manually by clicking the software process control interface trying 
to keep as close as possible with Bernardini protocol.

The critical stage in this process is after temperature of crystal 
reaching 320C by quick cooling process (by fully opening the cooling 
valve). Usually, only experienced operator could control this process 
to produce palm oil form its crystal form. Our concern is to make 
AFS could learn from operatorfs skill in this critical stage.

@

5. RESULT AND DISCUSSIONS

@

After implementing the control strategy previously described, Fig.
6 shows the experimental result of AFS-based control.

It was successfully learn from the operator how to control the process,
especially on the critical stage that produce palm oil. Fig. 7 shows 
the typical palm oil (olein) and its side product (stearin).

Laboratory test shows that the product is in acceptable level, considering 
its melting point (MP) and cloud point (CP) are 520C and 130C respectively.


The above result is better than the manual control produced by experienced 
operator. It seems strange that estudentf (in this case AFS) could 
outperform the eteacherf (in this case experienced operator). However,
several experiments conducted for different system [4,5] show that 
the said conclusion is always happen.

6. CONCLUSIONS

The implementation of AFS as controller of palm oil processing is 
proposed. The result shows that palm oil produced by the proposed 
control system is better than the experienced operator.

However, this result is still in preliminary stage, meaning that 
it should follow by careful practical consideration such as flexible 
human-machine interface, self-calibration of instrument and safety.


Moreover, the proposed scheme is depend on the experienced operator.
Even though this scheme maintaining the advantage of fuzzy system,
in term of expert modeling, but some time more sophisticated scheme 
is needed to avoid involvement of operator.

Our future work is to develop adaptive fuzzy learning control scheme 
by utilizing Bernardirifs curve as time-varying set point; hence 
the involvement of operator no longer needed. This proposed scheme 
is important especially for the newly built plant.



Fig 5. Experimental setup



Fig 6. Typical experimental result



Fig 7. Experimental product, olein (left) and stearin (right)

Acknowledgement

Part of this work was conducted in Knowledge-based Information Engineering 
Department of Toyohashi University of Technology when second author 
on sabbatical leave there in 1997. Thanks due to Prof. Osami Saito 
(then with Chiba University) and Dr. Li Xu (then with Asahi University).
This work also in part supported by a World Bank Project, University 
Research for Graduate Education (URGE) Batch IV (1998-2000)

Reference:

[1] Bernardini Ernesto, Vegetable Oils and Fats Processing, B.E. 
Oil -00128 Rome -VIA Failla 63, Italy, 1983

[2] M. Sugeno and K. Murakami, gAn experimental study on fuzzy parking 
control using a model carh, in M. Sugeno, eds., Industrial Applications 
of Fuzzy Control, Amsterdam, North-Holland, 1985.

[3] L.X. Wang and J.M. Mendel, gGenerating fuzzy rules from numerical 
data, with applicationsh, IEEE Trans. On Systems, Man, and Cybern,
, vol 22, no 6, pp. 1414-1427, 1992

[4] Son Kuswadi, Wahyu Widodo, Slamet Wahyudi, Mohammad NUH, Osami 
Saito, gFlexible arm structure control using adaptive fuzzy networkh,
Proceeding of 1999 International Symposium on Nonlinear Theory and 
Its Applications (NOLTAf99), Hilton Waikoloa Village, Hawai, USA,
November 28 ? December 2, 1999

[5] Son Kuswadi et.al., gReal-time Vessel Automatic Docking Using 
Adaptive Fuzzy System-based Controllerh, Proceeding 1999 Industrial 
Electronic Seminar (IESf99), Graha ITS, Surabaya, Indonesia, Nopember 
1999.

[6] Yamaguchi T., T. Takagi, T. Mita, gSelf-organizing control using 
fuzzy neural networks, in C.J. Harris (ed.), gAdvances in Intelligent 
Controlh, Taylor&Francis, London, 1994

[7] Son Kuswadi, Mohammad NUH, gImplementation of adaptive neuro 
fuzzy on control system by human-based expert schemeh, Proceeding 
Calibration, Instrumentation and Metrology, LIPI, Serpong, Indonesia 
22-23 September 1998, pp. 317-329, in Indonesian

@








Reply via email to