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Application of a fuzzy logiccontroller in temperaturecontrol of a pilot high-temperature short-time heatexchangerJ.S. Shieh, H .C. Chen and L.H. FerngThis paper is based on th e use of a personal comput er (PC-AT), ADDA(analogue to digitalldigital t o analogue card) and an electro-pneumatic trans-ducer t o control an aseptic processing high-temperature short-t ime (HTST)sys tem, w hich requires accurate tem perature cont rol. In this study fuzz yalgorithm s based on linguistic rules describing the operators control st rategy wereapplied t o temperature control. The controller, w ith suitable membershipfunctions, rules and defuzzification mechanism, w as a simple controller forregulation of the HTST temp erature and can be compared to the conventionalproportional-integral-derivat ive (PID) method w hich controlled temperaturew ithin +0.5C. Some result s were not bett er than obtained w ith a traditionalPID controller but in certain tem perature ranges it could cont rol t he sys temtemperature as well as the PID controller based on self-tuning of the rules andmembership functions for the sys tem.

Keywords: HTS T; fuzzy logic controller , PID controller

INTRODUCTION

Fuzzy set theory was first introduced by Zadeh (1965)and it has been used successfully in a numb er of controlapplications. The first application of fuzzy set theory tothe control of dynamic processes was reported byMamd ani and Assilian (1975). They described controlof a small laboratory scale model of a steam engine andboiler combination. Using a fuzzy logic controller toregulate the engine speed and boiler pressure, theyobtained acceptable control. Kickert and Lemke (1976)designed a fuzzy logic controller for a laboratory scalewarm water plant. The first expriment applied fuzzylogic to design a controller on an industry plant w asundertaken by Rutherford and Carter (1976 ). Thepurpose was to control the permeability at the Cleve-land sinter plant and they showed that the fuzzy logicFood Industry Research and Development Institute, P.O.Box 246. Hsinchu, 30099 , Taiwan, Republic of China

controller can control slightly better th an the PIcontroller. There followed several publications both onthe theory and application of fuzzy control. Tong(1976) applied fuzzy logic to a pressurized tank co ntain-ing liquid, although it was no better than performanceobtained by a controller designed using conventionaltechniques. Ostergaard (1976) applied fuzzy logicsuccessfully on a heat exchanger. Sheridan and Skjoth(1983) attempted to use fuzzy algorithms to mimic kilnoperators at the Durkee plant of the Oregon Portlandcement company.Ingredients of food are dependen t on place, seasonand climate. Food processes, therefore, often have ill-defined, time-varying and complicated systems, and itis suitable to control them using fuzzy set theory. Thefirs applications of fuzzy set theory to food control werereported by Eerikainen et a l . (1988) and A arts et al.(1990). Several authors have since conducted researchinto these fields, such as glutamic acid fermentation(Czogala and Rawlik, 19 89; Kishimoto, 1990), done-

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Fuzzy logic control of HTST heat exchanger: J.S. Shieh et al.Table 2 Lookup table

Producterror -2

Hot water error-1 0 1 2

-2 -2 -2 -2 -2 0-1 -2 -2 -2 -1 0

0 -2 -2 0 0 01 -2 0 2 I 12 0 2 2 2 2

Table 3 Quantized variableHE (C) PE (C) V (mA) Quantized level-5 -5 4 -2-2.5 -2 .5 8 -1

0 0 12 02.5 2.5 16 I5 5 20 2

When hot water error an d product error have beenobtained, one can decide which quantized levels belongto them. Th e values of mem bership functions can thenbe obtained from Table 1. Meanwhile, each rule has adifferent grade of contribution for these errors.Com bining all rules produces a lookup table(Table 2).In this investigation, the electro-pneumatic trans-ducer has a range from 4 to 20m A to control theregulating valve from closed to open. Therefore, onecan define the following range: H E from -5 to SC,PE from -5 to 5C and V from 4 to 20mA. Thevalues of quantized variable are shown in Tabfe 3. Thefirst and second co lumn represent the hot water andproduct temperature range in different q uantizedlevels. The third colum n is the valve range in differentquantized levels. When sensors detect the hot waterand product temperature, it can decide which quan-tized level belongs to this value. Then, the quantizedlevel of control input is obtained from the lookup table

Recycle process

(Table 2). Comparing the third and fourth columns inTable 3, it is easy to obtain the size of valve open . Usingthis method, the control valve can open to various sizesdepending on product and hot water error.

EXPERIMENTAL METHODA APV CREPAC O pilot-scale HTST system wasequipped with hot water heating and water coolingapparatu s. A two-stage plate heat exchanger composedof stainless 316 material was included in the system toheat and cool the product. Two T-type thermocouplesensors were installed at the site of product out and hotwater inpu t (Figure 2). High pressure steam froma boiler through the reducing valve decreased pressureto 4 bars. Then, using regulating valve controlled steaminto water to produce hot water which can heat productvia a plate heat exchanger. The regulating valve wasadjusted by electro-pneumatic converter w hich wascontrolled by an analogue to digital/digital to analogue(ADDA ) card. When product temperature was belowthe set point the computer sends a signal via theAD DA card to open the solid state relay (SSR) andcontrol prod uct returned to the original tank. O n theother hand , signals from thermocouples were so smallthat they need to be amplified to a suitable range via anamplifier card which connected to the ADD A card.The who le system of process control was thereforeaccomplished with an IBM PC-AT compatible com-puter w hich was linked with the HTST through anADD A and amplifier card (&we 2) .

RESULTS AND DISCUSSIONIn general, the high-temperature short-time (HTST)system is defined at a sterilizing temperature from 95 to

Product in

Ai r

Steam

Figure 2 Scheme of control system for HTST

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Regulatingvalve Reducing valve

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120C and the holding time is >5 s. Regarding an ultra-high temperature (UHT) system, the sterilized temper-ature is defined as >135 C and the holding time isabout 3-5 s. The holding time is dependen t on the flowrate of product and length of the holding tube. Fromthe microbiological point of view, a suitable holdingtime can be chosen as regards some bacteria. Thissytem can change the pump rate and adjust the holdingtime from 20 -30s. It is thus able to sterilize high-acidfood such as juice an d cannot be used to sterilize low-acid food such as milk except using UHT. Different set-point values were therefore chosen betw een 95 to120C in order to simulate the HTST system. Accord-ing to FDA regulations, product temperature through aholding tube should be larger than the set point value,which is the sterilized temperature, and the tempera-ture variation at the holding tube should be

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methods from Table 4 , it has similar results for theother methods. In general, the tuning method proposedby Rovira e t a l . (1969) is better than the other method s.From Figure 3a, it can be seen that the producttemperature is stable but the controller output issaturated quickly, as in on/off control. It is thereforeeasy to dama ge the regulating valve and is not suitableto control the system.

Figure 4 show s temperature control of HTST atdifferent set-point values using the method of Rovira etal. The P , I an d D values were 80.8, 99.6s and 0.22s.The different set-point values were 1 20, 105 and 95C.From Figure 4, it can be.seen that the method of Rovirae t a l . can control HTST temperature within kO.5C.

125r ____

251

0 100 200 300 400 500 600 700a Time (s)125

L-1) I I 100 200 300 400 5 00 600 700b Time (s)125 r

0 100 200 300 400 500 600 700C Time (s)Figure 5 Temperature control of HTST at different set-point valuesusing a fuzzy logic controller. Set-point: (a) 110C; (b) 100C;95C

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Fuzzy logic control of HTST heat exchanger: J.S. Shieh et al.

Figure 3 and 4 show the experimental results ofHTST using the conventional PID method whichbelong to a SISO (single input/single output) system. Inthe HTST system, the feedback point can affect theresults. The ho lding time of this system is so long (i.e.25s) that the feedback point m ust be the hot water toprevent temperature cycling due to phase lag. Usinghot water as the feedback point, it can be seen that ifthe hot water temperature can be kept stable, theproduct temperature will be more stable as productpasses through the holding tube to exchange heat fromthe hot water. Althoug h this procedure has someadvantag es, the main difficulty is determining thetemperature difference of hot water and productproduct after the holding tube. A ccording to FDAregulation, product tem perature is very sensitive tofood safety and quality after the holding tube. For thisreason we attempted to use the fuzzy logic controller tosolve this problem.By using fuzzy set theory, the two input variableswere chosen to be the errors of hot water and producttemperature while the regulating valve was the systemoutput. The shape of memb ership function w as chosento be trapezoidal. Six rules were taken from experienceand the defuzzification was chosen to be the centre ofthe area. Figure 5 show s temperature control of HTSTat different set-point values using the fuzzy logiccontroller. The product temp erature was stable belowthe set-point when the set-point was high (Figure AZ).When set-point w as low, the product temperature wasstable above the set-point (Figure 5~). In Figure 5b theproduct temperature was just stable at the set point andcan be compared to the conventional PID methodwhich can control HTST temperature within f0.5 C.This fuzzy logic controller is therefore designed tocontrol H TST temperature at 100C . At other tem-peratures rules and memb ership functions should beadjusted to obtain a lookup table such as Table 2 and tocontrol the HTST system m ore accurately.

CONCLUSIONSThe results of temperature control of HTST w ithvarious conventional PID methods has been provedvery successful. Using the method of Rovira e t a l . givesbetter results than the other m ethods and it can controlHTST temperature within ?0.5 C. However, itcannot use product temperature as a feedback pointwhich could be a disadvan tage regarding food safetyand quality. A fuzzy logic controller was introduced tothis system in order to solve this problem. The resultswith the fuzzy logic controller are not very successfulover some range s, b ut, it offers a good approach tosolve this problem of controlling the system morecarefully and accurately. Muc h research will have to becarried out especially in the area of self-tuning rulesand selection of memb ership function g ain. Regardin gself-tuning rules, there are many publications (Procyk,1977; Daley, 1984; Moo re,l991) investigating this areaof self-organizing fuzzy logic control (SOFLC ). It canprovide an adaptive rule-learning capability to comple-ment a fuzzy logic control strategy. The selection of thegains in mem bership functions of fuzzy logic control isnot wholly subjective and several authors (Daley, 1986;1987; Linkens and Abbod, 1992) have conducted

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research relating this to fuzzy logic control. Com bingSOFLC and selection gain in membership functions willpermit more accurate control of HTST tem perature.

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