vacuum pan

8/11/2019 vacuum pan

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Expert Control of

Vacuum Pan

Crystallization

Jan Michal, Milo5 Kminek, and Pave1 Kminek

acuum pan crystallization

difficult to control by classi-

cal means. This paper pre-

sents a method of using a

rule-based expert system for

the control of such a process.

The structure of the expert

controller for adjusting the

characteristic conductivity is

shown to consist of a diagnos-

tic expert system and a dis-

crete dynamic model that is

used for conversion of seman-

tic results into numeric output

values. The expert controller

was implemented in the sugar

factory in Lovosice in cam-

paign 1993.

v.s a process that is very

Introduction

Crystallization process is

the key stage of sugar produc-

tion. The basic goal of the

vacuum pan operation is to

produce sugar crystals of a

specified size from a solution

Battery of vacuum pans sugar acto

containing sugar and non-

sugars. A typical vacuum pan with the instrumentation is shown

onFig.1.At the beginning of every individual batch the vacuum

is established and the crystallization pan is filled with juice and

syrup until the level is above the calandria into which the steam

is introduced. Low pressure exhaust steam from previous evapo-

ration stages is used to boil the juice in a vacuum pan. The syrup

is then boiled down, increasing the density and the supersatura-

tion of the syrup. Performing the operation in a vacuum allows

the water to

be

evaporated at a lower temperature, thereby

reducing the quality of required steam while minimizing the

formation of color in the growing crystals at the same time.

The composition of the feed to the pan is characterized by its

brix and its purity. Brix is the ratio of dissolved solids to the total

mass of solution while purity is the ratio of the amount of sucrose

to the total mass of dissolved solids. The purity affects the

A

version of this article wa s presented at the

1993

IEEE

Intema-

tional Conference on Svstems, Man, and Cybemetics. The authors

are with the Department of Computing and Control Engineering,

Institute of Chemical Technology, Technicka

5

166 28 Praha 6, The

Czech Republic. Email jan.mich al@ vscht.cz.

try

n

Lovosice).

solubility of the sucrose and thus the amount of sucrose required

to have a saturated solution. For crystallization to occur the

solution must be supersaturated. The precise effectsof purity on

saturation depend on the particular impurities, which are affected

by the quality of the sugar beet and its origin and which vary as

the campaign progresses. Once the correct supersaturation has

been reached the pan is seeded. This is done by introducing a

small amount of fixed quality seed crystals. The sugar juice then

begins to crystallize around these seed crystals, causing them to

grow. This suspension of sugar crystals is known as massecuite.

Once the grain is established the syrup feed is controlled to

increase the pan level and maintain the desired supersaturation

of the mother liquor. When the pan is full there is a period of

brixing up in which water is further evaporated with no additional

feed. Finally the vacuum is broken and the pan is discharged to

the receiver for centrifuging.

Control Strategy

The crystallization process control should guarantee the high-

est possible speed of crystal growth at the lowest energy con-

sumption. Early vacuum pans did not have a systemof automatic

0272- 1708/94/ 04.0001994IEEE

I EEE

Control Sys tems

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control; the operator drove the process using experience gained

over many years. Control actions and decisions were based on

practical knowledge, rules of thumb, intuitive feelings and some

other mysterious methods. He relied upon visual observation of

the crystals glittering, which could be seen through the vacuum

pan window, on the transparency of the juice sample observed

on the piece of glass, and on the speed of its flow. The only

measured values were temperature and pressure in the pan. It is

no wonder that operators were considered to be the most impor-

tant people in the sugar factory.

The main difficulty associated with the crystallization control

is in measuring of the parameters which we are trying to control.

There is no direct method of measurement of the supersaturation

of the liquor, nor of the crystal content in the pan. Today the

automation of the crystallization process is based on finding the

supersaturation indirectly-in most cases by measuring electri-

cal conductivity of the solution as the cheapest method. It has

been discovered that conductivity is an indication of the extent

of supersaturation; however, this way of measurement is far from

precise as conductivity is strongly influenced by the amount of

non-sugars in the solution. The relation between supersaturation

and conductivity is therefore changing during the campaign,

making a simple controller unusable. Crystallization

in the vac-

uum pan is a batch process usually controlled by a programmable

logic controller with a few analogue control loops. Not only is

there sequence control on the odoff valves but the continuous

control loops themselves are subjected to sequentially controlled

changes.

There are four key stages during the process which influence

the final result: boil down, seed, feed and brix-up. At the boiled

down stage the supersaturation must be reached as fast as possible,

which means the steam valve must be fully opened while the syrup

level in the pan is controlled by the feed valve.

As

soon as the

correct supersaturation has been reached the seed stage follows.

The correct supersaturation of the solution when the seeds are

introduced is very important, otherwise the seed crystals are

dissolved (supersaturation s too low) or a spontaneous nucleate

crystallization occurs (supersaturation is too high). In the feed

stage, the juices crystallize around the seed crystals decreasing the

supersaturation, n the same time water evaporation increases the

vacuum

seed

crystals

sugar crys tals mother liquor

trike receiver

Fig. 1 Vacuum pan fo r sugar crystallization.

October 1994

supersaturation.The juice and syrup feeds are controlled o main-

tain the supersaturation of the mother liquor in the predefined

range for which the crystals grow at maximum speed. An inter-

rupted feed is used to help proper mixing of the uice which is very

important in this stage. The massecuite level in the pan increases

until the pan is full. In the brix-up stage water is further evaporated

until a predetermined final density of massecuite is reached.

Typical diagramsofconductivity and level in the pan reshown

in

Fig.

2.

Characteristic values of seed, feed and brix-up conduc-

tivity are shown there. During the feed stage the conductivity

oscillates between two different values as the feed is interrupted.

The larger the amplitude of the oscillation, he better is the mixing

of the massecuite and thereby the small crystals are dissolved (but

the large ones last). The conductivity based control has several

drawbacks. It depends on the impurities and content of non-sugars

that are affected by the soil in which the beet was grown and

condition under which the beet is maintained and stored. They

vary from region to region and from year to year. The presence of

sugar crystals in the solution and its mixing affects the electrical

path length and thus the conductivity. The process can be satisfac-

torily controlled only under the condition that all conductivity

parameters are well adjusted reflecting the actual relation between

supersaturation and conductivity. They are: seed conductivity,

conductivity limits which create the envelope of the conductivity

oscillations n the feed stage and final brix-up conductivity. Much

skill and experience are required to set these parameters, which is

why the quality of control varies from shift to shift according to

the ability of the operator to adjust the parameters.

Expert Control

Expert systems show promise in solving control problems for

complex systems. As the complexity of technological processes

increases, the problem of their control becomes more critical. In

this work we focused our attention on a technological process

whose behavior is very difficult to define due to the number of

conductivity

pol boil down

F- l

loo

seed

feed

br ix up

- _

- _

_

..

_

I

I I I I I

1 2

3

4

5

time [hours]

o v

1

2 4 5

time [hours]

Fig.

2.

Typical diagrams

o

conduct ivi ty and level in the

crystallization pan.

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rule plane

I

Fig. 3. Expert controller structure.

nonlinearities and parameters that are impossible or difficult to

estimate. In this case, classical feedback control cannot be used.

One of the reasons is the difficulty in expressing the desired

behavior of the process in terms of conventional control theory

and classical design specifications. Crystallization in a vacuum

pan is a process characterized by the above mentioned condi-

tions, so that expert control presents itself as a viable alternative

to classical control methodologies.

In general the expert control of technological processes oper-

ates according to rules that are based on:

thorough knowledge about the construction and instrumen-

tation of the controlled system, and

intuitive knowledge based on the operators experience

(rules of thumb).

Both thorough knowledge and intuit ion can be expressed by

semantic rules as well as by mathematical formulas, which allow

us to express almost any desired fact about the properties of the

controlled system and its behavior. This information is concen-

trated in a knowledge-base, which typically consists of two parts

(Fig. 3). One part of the knowledge-base is a semantic net; it

consists of the statements and rules (rule plane).

A

pr ior i

probability values are assigned to all statements, indicating how

likely it is that the fact is true. Every rule links two statements

together, an evidence to a hypothesis. The strength of the link is

given by two conditional probabilities, determining the power of

the rule. All probability values are specified by the expert during

the knowledge-base construction and reflect the uncertainty of

the system. The deterministic data are related by mathematical

formulas or other algorithms, creating a parallel plane of the

expert system (data plane). Some facts in the rule plane can

trigger computation of relevant algorithms and update the nu-

merical values in the data plane, finally resulting in the value of

the controller output. Some data, on the other hand, influence the

probability values and logical conditions in the semantic net.

Creation of the knowledge-base structure for expert control

can

be

divided into three stages.

In the first stage, input data is examined from different points

of view. The starting evidences are the input data of actual values

of several-but not necessarily all-process variables. This

knowledge is obtained by means of direct or indirect measurement

orAspecially in the food industry-by means of subjective

human observation such as vision, smell or taste. The intermediate

hypotheses, hat are to

be

proven at this stage, concern properties

of the input variables. According to the input data, the a priori

probabilities of these hypotheses are modified during the expert

system run to reflect the actual stage of the system. Computation

routines may be activated at some nodes of the semantic net to

evaluate formulas expressing deterministicrelation among data.

The expert control strategy is derived from the fact that the

control rule can be expressed for some surrounding of its present

state, however complicated the system behavior may be. The

state space of the controlled process can be divided into smaller

parts or domains. The selection of the domains and their borders

is done so the control rule can be expressed by a simple formula.

The domains can overlap and the borders between the domains

can be fuzzy.

The second stage

of

the control starts with the proven hy-

potheses from the first stage. Its task is to find out the domain of

the present state of the controlled system. All final hypotheses of

the second stage have the same form: The present state of the

system is in domain D: The differences in the probabilities

encountered in several succeeding runs will show the tendency

of the controlled system behavior.

The last stage of the expert control must find out how to

influence the system in order to follow the desired trajectory in

the state space considering all criteria and limitations. It has been

assumed it was possible to determine the control rule in every

domain Di of the state space. Thus, the control rule is known for

each final hypothesis.

Now a decision is made as to which control rule will be

applied. There are several possible strategies to determine the

value of the manipulated variable of the controlled process. The

simplest possible strategy is to apply the value Ui that is associated

with the most probable final hypothesis. However, this strategy

neglects the influence of the other final hypotheses with smaller

but still comparable probability. It can lead to bang-bang control

as a small change in input data can cause other final hypotheses

to win and hence cause quite a different output to be applied.

Another strategy is based on a weighted average. In this case all

final hypotheses contribute to the value of manipulated variable

according to their final probabilities. The control is smoother in

this case. Yet other strategies use more sophisticated formulas to

determine the output value. The relation between the final hy-

pothesis and the output value can be expressed by means of a

linear discrete model with multiple inputs:

I

inference knowledge

engine

final

conduct iv i ty j

[

Fig 4 . Exper t control ler fo r tuning the parameters of the

conductivity control.

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IEE E Control Systems


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expert systems working with uncertainties the knowledge base

priori probability and every rule must possess a l ikelihood ratio

that expresses its strength. Usually there is no reason to prefer

any intermediate hypothesis in advance, which is why the apriori

probabilities of all nodes were set to 0.5. A much more difficult

problem is to set the likelihood ratios of the rules. For L=l the

rule has no effect; if L>1 the rule supports the hypothesis

categorically; if L


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