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Integration of fuzzy logic based control procedures in brewing
Food Control 13 (2002) 23±31
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Integration of fuzzy logic based control procedures in brewing B. O' Connor a, C. Riverol a,*, P. Kelleher a, N. Plant a, R. Bevan b, E. Hinchy b, J. D'Arcy b a
Advanced Process Technology Group (APT), Food Science Building, University College Cork, Cork, Ireland b Beamish and Crawford Brewery plc, South Main Street, Cork, Ireland Received 2 April 2001; received in revised form 29 June 2001; accepted 5 July 2001
Abstract The objective of this paper is to integrate fuzzy logic into the fermentation process in a brewery. The brewery involved is a local commercial brewery, Beamish and Crawford Brewery plc in Cork, Ireland. Our approach consists of developing a control system for a fermentation process using fuzzy logic in two stages. In the ®rst stage the software package fuzzyTech from Inform provided the fuzzy logic controller, and in the second stage InTouch from Wonderware provided the user front-end. The results of this new controller and the fault detection system show a clear alternative over the conventional controller (PID). Optimisation of the fermentation vessel is run simultaneously with this project. The main idea is based on examining the various input parameters that in¯uence the fermentation process. These parameters include temperature, pressure, fermentation duration and yeast count. Next the quality or output parameters are identi®ed. These include alcohol content, present gravity, pH, colours and bitters. A model based on fuzzy logic is developed to establish the inter-relationships between these factors and how they aect the output quality factors. The ultimate objective is to integrate this fuzzy logic model into the fermentation process control system. Ó 2001 Elsevier Science Ltd. All rights reserved.
1. Introduction In recent years, interest has been growing in the application of advanced process control strategies to improve food-manufacturing operations (Harley, 1995). The processing of food and beverages in industry is often complicated because of lack of linearity, interactions between ingredients and the gap of knowledge concerning their control and the critical lack of sensors. Classical automatic control seems to be dicult and many open loops are encountered in which the role of the human operator becomes more and more important. Product properties, which contribute to qualities and process productivity, depend mainly on the accuracy of this control reaction (Trystram, Perrot, & Guely, 1995). Hence, fuzzy logic control is attractive for food processing since it can use both process conditions and an expert's knowledge (Zhang, Litch®eld, & Bentsman, 1993). Thus in brewing new practical ways of accelerated fermentation and maturation are made possible.
*
Corresponding author. Tel.: +353-21-903645; fax: +353-21-903091. E-mail address: [email protected] (C. Riverol).
High level beer quality must always be the prime target of the brewer. Alterations, therefore of any fermentation parameter in order to achieve a shortening of production time can only be accepted without signi®cant change in beer quality. The wide knowledge of the different fermentation parameters and their eect on beer quality is a basic tool the brewer. The technological parameters the brewer can apply to in¯uence fermentation include: · Wort composition. · Aeration rate. · Pitching rate. · Yeast strain. · Fermentation temperature. · Pressure. Temperature control is a practical problem in production-scale brewery fermenters, when the vessels are very big and is not possible to immediately follow changes in the temperature set point pro®les. Since in practice the control strategies are very simple, considerable temperature ¯uctuations and delay times may be observed in big fermenters. Temperature is the variable by which the fermentation process in beer breweries is controlled. Hence, the temperature control is of considerable importance for
0956-7135/01/$ - see front matter Ó 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 6 - 7 1 3 5 ( 0 1 ) 0 0 0 6 7 - 6
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Nomenclature A; B; C matrices in space state Ap area Fd fault parameters Gf sensor parameters Kf actuator parameters r residual vector r1 reaction rate for yeast r2 reaction rate for wort any quality assurance of the beer fermentation process, in particular for a reliable prediction of the process behaviour, which becomes necessary if the length of the fermentation process and thus the tank logistics are to be controlled. Several cognitive approaches show great potential to control or optimise the fermentation stage in beer production. The reason for the usage of cognitive methods in brewing technology lies in the complexity of the matrix wort or beer and in the characteristics of interesting processes, which are mostly dynamic and nonlinear. Therefore, most of the processes and the matrix are, in eect, not describable with mathematical methods. However a lot of technological knowledge or large quantities of data exist which can be used for cognitive approaches e.g. based on fuzzy logic or arti®cial neural networks. Intelligent control systems will become necessary in the near future because of increased needs in production and maintenance of controlled processes and machines. Many of the current control systems are unable to deal with these demands. In control systems of the next decade the key features and terms will probably be adaptivity (to enable high utility and easy maintenance), rapid development (to reduce the developments costs), and predictive fault diagnosis systems together with versatile monitoring systems (Rauma, 1997). New technology used in industry tends to be a blend of PC, PLC and DCS. There exists a choice of mini-DCS, powerful PLC-type control, system PLCs with enhanced continuous control capability or soft control where everything is carried out on a PC. One thing is certain; the hardware platform is becoming less relevant and less recognisable as software is acknowledged as the key element of a controller. Almost all control is now soft. It is only the platform and operating systems that vary (Higginson, 1998). One driving force behind this new technology is the development of chips. These are doubling in size every 18 months to 2 years. With these chips, DCSs are being developed with PLC features and vice versa. These are useful in the food industry where a large variety of control is necessary (Williams, 1998). Fuzzy logic methods are also a way of the future, as it is easy to implement and has a wide range of potential
T Tj u U V Vp y q l
temperature jacket temperature input vector overall heat transfer coecient projection matrix volume measurement vector density growth rate coecient
applications in the food industry. Many software packages exist based on the principles of fuzzy logic and can be implemented with many of the previous hardware and software platforms. Some of the most common are: · fuzzyTECH. · CubiCalc. · TILShell. · FIDE. · RT/Fuzzy. · Fuzzy Knowledge Builder. · Fuzz-C. Fuzzy control techniques are applied successfully in many fermentation processes even though the idea of using fuzzy set theory to control biotechnology processes appears to be a contradiction (Gvazdaitis et al., 1994; Halme, 1988; Ming, 2000; Nyttle & Chidambaram, 1993; Postlethwaite, 1989; Simutis, Havlik, & Lubbert, 1993; Venkateswarlu & Gangich, 1995; Yuan, Miao, & Li, 1999; Zhang, Visala, Halme, & Linko, 1994). Fuzzy techniques make it possible to use observations and process knowledge in computer-based control systems. Fuzzy systems oer considerable potential for increasing automatic control in the food industry. They can be based entirely as fuzzy techniques or combined with conventional control techniques in hybrid systems (Mittal, 1997, Chapters 6 & 8). These advanced controls are better able to complement classical techniques for companies that continually seek to lower costs and shorter product development cycles (Bartos, 1997). Fuzzy logic control is attractive for the control of food processes because it can: · Handle multiple control objectives simultaneously. · Utilise fuzzy information in analysing complex processes. · Take historic process data into decision-making consideration. · Evoke skilful operator's experiences and perform approximate reasoning in process control. · Mathematically model process uncertainties. · Be economic to use as the development time is much shorter and easier with lower engineering eorts then traditional methods (Froese, 1993).
B. O' Connor et al. / Food Control 13 (2002) 23±31
2. Selection of software As previously mentioned, there are many dierent types of software packages which are based on the principles of fuzzy logic e.g. fuzzyTECH, CubiCalc, TILShell, FIDE, RT/Fuzzy, Fuzzy Knowledge Builder and Fuzz-C, to mention but a few. As well as these commercial packages there are many packages for research and teaching only. Many research institutes develop their own generic fuzzy software suitable for their own individual needs. The many software packages that exist vary greatly in their functionality, cost and compatibility with hardware and other software packages. The initial work with the brewery determined a broad outline of the project requirements and these were used to select a suitable software package. The package must complement the previous control system and communicate with all the present control systems in the brewery. This package must also be Microsoft Windows compatible. Each package was analysed under headings of functionality, support, expansion capability and value. Shipping costs also had to be included into the overall cost of the package. While each package has its own advantages and functions, the software Fuzzy Knowledge Builder, Fuzzy Judgement Maker and CubiCalc were deemed not suitable, as none of them would have the functionality to support a project to be implemented in industry. The three remaining possibilities were TILShell, FIDE and fuzzyTECH. The software used in this project is fuzzyTech (version 5.01) from Inform and InTouch (version 7.0) from Wonderware. This project used a personal computer (PC) Gateway 2000, G6-266 with 128 MB of main memory (RAM) and 6 GB of hard disk, running under MS Windows NT version 4.0. The fuzzyTECH (fuzzyTECH, 1997) provides code generators for C and assembly, as well as interfaces to industrial process control systems (InTouch, TheFIX, FactoryLink, WinCC, Foxboro-IAS, DIGIMATIK, Freelance2000, Contronic, Citect and LabVIEW). Dedicated fuzzyTECH's Runtime Modules are also available for Programmable Logic Controllers. 3. Case study 3.1. Fermentation control system In the brewery there are 6 fermenters of 1300 and 18 1000 HL fermenters. Each fermenter has a glycol jacket for cooling process that works automatically. The model was based on a beer product with a temperature setpoint of 16 °C and a PG endpoint of 1015. The model was called Ferment-Stop and created using the fuzzyTECH wizard. The model consists of two inputs, Temperature and PG, with the output being Process. These projects
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were developed further with many dierent adjustments. This is a very tedious procedure. After each adjustment is made, a back check involving a complete computation of a set of data must also be made to ensure that the decided position for term 1 (less Setpoint) with variable 1 (Temperature) is still allowable if term 2 (Cont O Cont) in variable 3 (Process) is adjusted and so on. Table 1 shows a summary of the parameters used. The rules were developed in the IF THEN format. Nine rules were automatically created by the wizard. Only six rules were necessary to cover each possible scenario of the fermentation process (see Table 2). The expected result is that the fermentation should continue until the desired PG endpoint is reached. The wizard automatically generated the number of rules, which corresponded to a permutation of all possible combinations of the input variables. This model generates 36 rules, 3 3 4, which is based on a permutation. This is calculated using the number of terms of each input variable multiplied by the number of output terms. These rules are generated automatically, so many of them are not possible when the constraints of the fermentation process are taken into account and many of them are also unnecessary. This project used generated data from both the PLC and fuzzyTECH and also process data from the Brewery. The data from the PLC demonstrates the ease with Table 1 Summary of variables and their parameters used in Ferment-Stop Variables
Terms
Ranges
Temperature
Less Setpoint Setpoint Greater Setpoint
0±16 °C 14±18 °C 16±32 °C
PG
Less Setpoint Approx Setpoint Setpoint
960±1010 985±1012 1010±1060
Process
StopCool ContOCool ContCool Continue
0±1.0 0.4±1.2 0.8±1.6 1.2±2.0
Table 2 Inference rules used in the fuzzy controller IF
1 2 3 4 5 6
PG
Temperature
Less Setpoint Less Setpoint Less Setpoint Less Setpoint Setpoint Setpoint
Less Setpoint Setpoint Greater Setpoint Setpoint Less Setpoint Setpoint
THEN Process StopCool Stopcool ContOFFCool ContCool Continue Continue
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which the model may be connected to a PLC, but the data is just randomly generated values. These are selected to remain within the designated ranges. The brewery data provided the best results, as it is real data. Using the data as inputs a decision on the fermentation process is computed and the results are displayed on the mimic panel. This particular fermentation was commenced on 25 November 1997 and completed on 1 December 1997. The information logged in the brewery for each individual fermentation process consists of the date, time, real-time temperature value, temperature setpoint, realtime PG value, and PG endpoint. The two new columns are information coming from the fuzzyTECH model, the Process value in numeric form and the corresponding linguistic variable. See Table 3 for initial 10 data inputs and their corresponding fuzzy model outputs. A fermentation process for a beer should be between 5 and 8 days for a stout or ale product and between 7 and 12 days for a lager product. The time duration of data set was approximately six and a half days. The endpoint for the brewery data is the point when the temperature setpoint was changed to 4 °C, as this is the ocial end of the fermentation stage. The endpoint for the model is considered the point when the PG value reaches the value 1015. This includes 1015.99 and usually anything less than 1016. The advantage of fuzzy is that it is not an exact cut-o point between 1015.99 and 1016. Sometimes the stop and cool value will not be computed until a lesser value is reached, such as 1015.33, depending on the other values of temperature and other in¯uences. This is the basic principle of fuzzy
logic. There are overlaps generated in the MBFs, which gives results other than hot/cold or on/o. This model can easily be modi®ed for dierent products or even dierent quality parameters, without having to devise new complicated algorithms. This is a clear advantage over conventional control systems. The linguistic variable remains the same until the variable changed to ContCool, which translates as ``continue fermentation process with cooling system on'' (see Table 4), as the temperature is at 16 °C and the PG value is at 1029. Even though the temperature is at its setpoint, the computed output decides that the cooling system is necessary. This might seem a contradiction but if the data is analysed it can be seen that the temperature stays within a range of 16.1 °C and 15.7 °C for the ContCool period. The theory behind this idea of using fuzzy systems to control a fermentation process seems contradictory, but it allows qualitative information to be used in controlling the process. The Fig. 1 shows as the data ¯ows along of the process in the fermentation room. 3.2. Fault detection In this section we outline the general procedure of fault diagnosis using residual generation, this method depends upon the quality of the model of the system. In this case, we need to create a model for design the fault detection system. Biochemical reactors are used to produce a large number of intermediate and ®nal products, including
Table 3 Sample of data Temperature (°C)
Set temperature (°C)
PG
PG Endpt
Process
Decision
16.1 16 15.9 15.9 15.9 15.7 15.7 15.9 15.6 15.7
16 16 16 16 16 16 16 16 16 16
1052.3 1052.3 1052.3 1052.3 1052.3 1052.3 1052.3 1052.3 1052.3 1052.3
1015 1015 1015 1015 1015 1015 1015 1015 1015 1015
1.12235 1.1247 1.1247 1.1247 1.1247 1.1247 1.1247 1.1247 1.1247 1.1247
ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool
Table 4 Extract of data set Temperature (°C)
Set temperature (°C)
PG
PG Endpt
Process
Decision
15.9 15.9 16 15.8
16 16 16 16
1031.5 1031.5 1029 1029
1015 1015 1015 1015
0.95645 0.95645 0.893 0.893
ContOFFCool ContOFFCool ContCool ContCool
B. O' Connor et al. / Food Control 13 (2002) 23±31
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fashion. Notice that l is the ®rst-order at low x2 and zero-order at high x2 . l x2 l max : 6 km x2 That is, when x2 is low l x2 l max km and when x2 is high l lmax : Since the reaction rate is
Fig. 1. Process control information architecture.
r1 lx1
beverages. Biochemical reactor models are similar to chemical reactor models, since the same types of material balances are performed. In the simplest reactor, we consider two components: biomass and substrate. The biomass consists of cells (yeast) that consume the substrate (wort). In this module we assume that the reactor is perfectly mixed and that the volume is constant (Breusegem, Thibault, & Cheruy, 1991; Bequette, 1998). The dynamic model is developed by writing material balances on the biomass (yeast) and substrate (wort). Biomass grows by feeding on the substrate. The biomass balance in a batch reactor is written as dVp x1 Vp r1 : dt
1
The substrate material balance is dVp x2 Vp r2 : dt
2
The reaction rate (mass of cells generated/volume time) is normally written in the following form: r1 lx1 ;
3
where l is the growth rate coecient. We can think of l as being similar to a ®rst-order reaction rate constant; however it is not constant (it is a function of the substrate concentration). The unit of l is time 1 . There is a relationship between the rate of generation of biomass and the rate of consumption of substrate. De®ne Y as the yield, that is, the mass of cells produced per mass of substrate consumed: r1 Y : 4 r2 For a constant volume reactor, substituting all the equations yields: dx1 lx1 ; dt dx2 lx1 : dt Y
5
The growth rate coecient is usually not constant; the growth rate coecient often varies in a hyperbolic
7
this means that the description is similar to a secondorder reaction when x2 is low since l x2 x1 r1 max 8 km and to ®rst-order reaction when x2 is high since r1 lmax x1 : The energy balance in the tank is dT U Ap T Tj : dt Vp Cp q
9
10
A leakage ¯ow is modelled as an extra term however; the presence of noise makes it necessary to use ®ltering. Typically, the residual generation problem can be stated as follows. Consider a dynamic system with a known nominal mathematical model as, for example (space state) x_ t A x t B u t Ed t Kf t; y t C x t Fd t Gf t;
11
where x is the state vector, u the known input vector, y the vector of measured outputs and A; B; C known matrices of appropriate dimensions. The term Ed models the unknown inputs to the actuators and to the dynamic process; Kf actuator and component faults; Fd the unknown inputs to the sensors and Gf sensors faults. Notice that A; B and C are nominal matrices of the system. Since the faults that are principally re¯ected in changes of A; B; C as well as modelling errors, are considered by f and d associated with proper choices of E; F ; G; K. The initial model can be written as space state. The variety of fault modes that can occur may be classi®ed as follows: 1. Abrupt fault modes. 2. Incipient (slowly developing) faults, e.g. bias or drift. Typically, abrupt faults play a role in safety-relevant systems where hard-failures have to be detected early enough so that catastrophic consequences can be avoided; incipient faults are of major relevance in connection with maintenance problems where early detection of worn equipment is required. In this case, the faults are typically small and not as easy to detect, but the
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B. O' Connor et al. / Food Control 13 (2002) 23±31
detection time is of minor importance and may therefore be large. Given the actual input vector, u t, and the measurement vector, y t, suppose that a residual vector r t exists that carries information about a particular fault. Find an algorithm that generates r t when the fault has occurred, under the following conditions: · The mode (time evolution) of the fault is unknown. · The mathematical model of the nominal system is uncertain. · There is system noise and measurement noise. · The residual generation has to be performed within a speci®ed time. Of course, there are many modi®cations of the problem statement depending upon the given situation and the particular purpose of application. For the detectability and distinguish-ability of a fault the following conditions must hold: · Knowledge of the ``normal'' behaviour of the nominal model A; B; C. · De®nitiveness of the ``faulty'' behaviour. One of the methods of residual generation best known is the Parity space approach (Basseville & Nikiforov, 1993; Frank, 1990; Willsky, 1976). The idea is to check the parity (consistency) of the mathematical equations of the system by using the actual measurement (10). A fault is declared to have occurred once preassigned error bounds bi are surpassed. To outline the basic idea of the parity space methodology we consider ®rst the simpli®ed case of redundant measurements that may be obtained directly. Let them be modelled by the algebraic measurement equation y Cx Dy; where y is the measurement vector, C the measurement matrix, x the true measurement value and Dy the error vector Dyi > bi , de®nes a faulty operation indicated by the ith measured variable. For detection of Dy the vector y can be combined to a set of linearly independent parity equations given by p Vy:
12
where p is the parity vector. The projection matrix V is now determined such that VC 0; V tV I VV t I;
1
C C t C C t ;
r V t p:
15
This procedure was applied to beer type Lager only as the tank is not yet pressurised. To save space, we made a resume in three ®gures and two tables where the results of two weeks are shown. As a starting point, the three dierent leakages used in this article are shown in Tables 5 and 6 shows the detected leakages. These leakages were used for to test the controller and the fault diagnosis method. The residual vector without faults is assumed to be zero mean and with constant variance. When a failure occurs, the parity of the residual is modi®ed for a jump. The residual evaluation permits these changes to be detected. In the present case, the possible faults transform the residuals by a jump of mean, which is easily detected by us. As appropriate safety measures in the case of a malfunction of the controller is provided with supervision and evaluation algorithms to detect this malfunction. Fig. 2 shows the response of the system to a leakage type ``to the atmosphere''. This type of fault is not the most common in the brewery and if is not detected rapidly can result in serious damage in the production. The deviations of temperature are small. In the case of the temperature a maximum of 0.54 °C is obtained, and in the pressure 5.78 kPa; in both the deviations are less of 1% of the corresponding reference values. An example of the system responses to the faults in real time is shown in Figs. 2±4. These ®gures demonstrate that fault detection via state space approach is good because the technique assumes that the process model is quite good. Errors in the model cannot be interpreted as faults, thus yielding false alarms.
Table 5 Leakages Leakage
Possible consequence
To the atmosphere From ammoniac network No leakage
Loss of CO2 Over pressurised tank ±
13
i.e. the rows of V are orthogonal, V is a null-space of C. Hence p V Dy:
with each measurement. The q-dimensional residual vector, r y Cx, where x C t C 1 C t y is the least squares estimate of x, is related to the parity vector p as
14
This reveals that the parity equations contain only the errors due to the faults, independent of x, which is not directly measured. Moreover, in the parity space, the columns of V de®ne q distinct fault directions associated
Table 6 The detected leakages Conclusion
Real leakage
To the atmosphere No leakage To the atmosphere Ammoniac network
To the atmosphere No leakage To the atmosphere Ammoniac network
B. O' Connor et al. / Food Control 13 (2002) 23±31
Fig. 2. Response of the system when there exists a loss of CO2 .
Fig. 3. Response of the system as for temperature with a leakage type loss CO2 .
Fig. 4. Response of the system with respect to temperature with a leakage type ammoniac network.
Probably the most interesting fact is in Fig. 4 where a fault in the jacket can yield high oscillation in the system. In this case, osets of 2 °C were detected.
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However, the predictability of the extract degradation and its reproducibility depend on the accuracy of the temperature. The variations of 2 °C in the fault detector can be translated to the controller as ¯uctuations of 1 °C as the fuzzy controller guides the temperature within a given tolerance band that minimises the cooling under the condition that quality parameters stay within guaranteed intervals. Since it has been observed that the dynamic behaviour of the big fermentation tank is different at dierent temperature intervals, the parameters of the model have been separately identi®ed for intervals around the normal fermentation temperature. The two dierent temperature intervals are 16 °C > T > 10 °C and 10 °C > T > 4 °C. In the ®gures it can be seen that both temperature and pressure, go to steady-state after a loss of CO2 (open valve). In approximately 3 min mean the system reduces the disturbance. The system was tested during two weeks using all the types of leakages. The changes in the temperature into tank are few using the fuzzy controller as the controller acts over the ammoniac ¯ux taking the centre of gravity of the combined distribution of the temperature. The results clearly explain the improvements obtained with the fuzzy controller and the fault diagnosis method. The attenuation obtained was of 77% but in the classic system was 75.6%. In the fuzzy system the ¯occulation was complete and ®nishes the reducing of all diacetyls. In the classic method generally, the ¯occulation is complete and light butterscotch ¯avour is obtained when the system faults. 3.3. Process optimisation of fermentation For 10 fermentations a series of seven individual parameters were monitored. These included temperature, yeast count, present gravity, pH, bitters, colours and alcohol content, see Fig. 4. These parameters were divided into two distinct categories, i.e. input parameters and output parameters. Input parameters were de®ned as those whose pro®les are not aected by the other parameters and which may be directly controlled over the course of the fermentation. Output parameters then, are those in¯uenced by the input parameters. The development of the model focused on the eect of temperature on other measured parameters of the fermentation. The measured output parameters aected by temperature were alcohol content, bitters content, colours, present gravity, pH and yeast count. The model was used to create fuzzy outputs or predicted output values for each of these variables. Actual and predicted values were then compared for all trials. Good correlation was found between predicted and real data for most parameters measured. Best correlations were obtained for predicted and measured values for present gravity and pH. Poorer correlations were obtained for bitters and colours pro®les however maxi-
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B. O' Connor et al. / Food Control 13 (2002) 23±31
mum errors between these predicted and measured data sets were relatively small. Yeast cell count pro®le was one of the less successful parameters modelled in terms of correlation coecients however the general trend of the predicted model matched the real growth curves in most of the trials. In summary, the fuzzy logic model using temperature as the sole input was best at predicting wort present gravity and pH values. Figs. 5 and 6 illustrate comparisons of real and predicted values for present gravity and pH, respectively. This value provides the end point of the process and this data is used in the development of a predictive/decision model which will constantly monitor the fermentation process and provide automatic decisions on how the fermentation should proceed ± i.e. continue, stop or cool the fermentation process. This will result in
Fig. 5. Real and predicted wort present gravity values.
Fig. 6. Real and predicted wort pH values.
increased automation and consistency in the fermentation process. 4. Conclusions and future perspectives The objective of this project was to develop a control system, failure detection system and predictive/decision model (optimisation process) for a brewery using fuzzy logic, speci®cally in the fermentation area. The software package fuzzyTECH from Inform provided the fuzzy logic controller, while InTouch from Wonderware provided the user front-end. The implementation of the system will depend on the brewery and their willingness to invest in the project, to purchase the necessary hardware and software components to integrate the fuzzy logic model into their present control system, to either replace or augment the system. These results clearly demonstrate the bene®ts of using a fuzzy logic control system. The use of the fuzzy logic model provides many advantages over conventional control of a fermentation process in the brewery. This control should provide a good quality, consistent beer, produced economically, while minimising the level of undesired ¯avour components. It should also reduce beer and raw material losses and provide a more predictable fermentation vessel residence time to ensure the proper application of a realistic scheduling package to maximise production capacity. The disciplines of fuzzy logic have enormous bene®ts for the fermentation process in a brewery: · This model can control a complex non-linear process, which is otherwise dicult to control. · Reduce fermentation time and increase production. · Produce a more consistent quality beer. · It allows on line modi®cations, without dealing with algorithms. · It allows easy integration into many SCADA packages. · Allow for a better scheduling package to be implemented. Technology is evolving at a rapid rate in food processing. 50 years ago there were no computers in the industry. Now they are commonplace, from the personnel department to processing and warehouse departments. Each department has their own computerised system. Some of them link each department together. Advanced control systems are the way of the future and it is only a matter of time before fuzzy logic is used in all industries in the Western world just like in the Japanese industries. Fuzzy logic will be especially useful to the food industry as it can deal with non-linear processes and variations in raw materials that are widespread in the food industry.
B. O' Connor et al. / Food Control 13 (2002) 23±31
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Ming, Y. (2000). The design of prediction controller based on fuzzy logic in fermentation process. Chinese Journal of Scienti®c Instrument, 21(6), 527±631. Mittal, G. (1997). Computerized control systems in the food industry (pp.179±205 and 235±276). New York: Marcel Dekker. Nyttle, V., & Chidambaram, M. (1993). Fuzzy logic control of a fedbatch fermentor. Bioprocess Engineering, 9, 115±118. Postlethwaite, B. (1989). A fuzzy estimator for fed-batch fermentation. Chemical Engineering Research and Development, 6, 267±272. Rauma, T. (1997). An adaptive control using diagnosis information. In Proceedings of TOOLMET97 ± tool environments and development methods for intelligent systems (pp. 116±121). Finland. Simutis, R., Havlik, I., & Lubbert, A. (1993). Fuzzy-aided neural network for real time state estimation and process prediction in the alcohol formation step of production-scale beer brewing. Journal of Biotechnology, 27, 203±215. Trystram, G., Perrot, N., & Guely, F. (1995). Applications of fuzzy logic for the control of food processes. In Proceedings of the FPAC IV conference (pp. 504±512). Chicago, IL, November. Venkateswarlu, H., & Gangich, K. (1995). Fuzzy modeling and control of batch beer fermentation. Chemical Engineering Communication, 138, 89±111. Williams, A. (1998). Process control ± the way ahead. Food Processing Supplement, 67(5), S4±S5. Willsky, A. (1976). A survey of design methods for failure detection in dynamic systems. Automatica, 12, 1976. Yuan, Y., Miao, Z., & Li, S. (1999). Fuzzy neural network controller in yeast fed-batch fermentation. Chemical Engineering Communication, 177, 167±172. Zhang, Q., Litch®eld, B., & Bentsman, J. (1993). Fuzzy logic control for food processes. AIChE-Symposium Series, 89(297), 90±97. Zhang, X., Visala, A., Halme, A., & Linko, P. (1994). Functional state modelling approach for bioprocesses: Local models for aerobic yeast growth process. Journal of Process Control, 4(3), 560±565.
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Integration of fuzzy logic based control procedures in brewing B. O' Connor a, C. Riverol a,*, P. Kelleher a, N. Plant a, R. Bevan b, E. Hinchy b, J. D'Arcy b a
Advanced Process Technology Group (APT), Food Science Building, University College Cork, Cork, Ireland b Beamish and Crawford Brewery plc, South Main Street, Cork, Ireland Received 2 April 2001; received in revised form 29 June 2001; accepted 5 July 2001
Abstract The objective of this paper is to integrate fuzzy logic into the fermentation process in a brewery. The brewery involved is a local commercial brewery, Beamish and Crawford Brewery plc in Cork, Ireland. Our approach consists of developing a control system for a fermentation process using fuzzy logic in two stages. In the ®rst stage the software package fuzzyTech from Inform provided the fuzzy logic controller, and in the second stage InTouch from Wonderware provided the user front-end. The results of this new controller and the fault detection system show a clear alternative over the conventional controller (PID). Optimisation of the fermentation vessel is run simultaneously with this project. The main idea is based on examining the various input parameters that in¯uence the fermentation process. These parameters include temperature, pressure, fermentation duration and yeast count. Next the quality or output parameters are identi®ed. These include alcohol content, present gravity, pH, colours and bitters. A model based on fuzzy logic is developed to establish the inter-relationships between these factors and how they aect the output quality factors. The ultimate objective is to integrate this fuzzy logic model into the fermentation process control system. Ó 2001 Elsevier Science Ltd. All rights reserved.
1. Introduction In recent years, interest has been growing in the application of advanced process control strategies to improve food-manufacturing operations (Harley, 1995). The processing of food and beverages in industry is often complicated because of lack of linearity, interactions between ingredients and the gap of knowledge concerning their control and the critical lack of sensors. Classical automatic control seems to be dicult and many open loops are encountered in which the role of the human operator becomes more and more important. Product properties, which contribute to qualities and process productivity, depend mainly on the accuracy of this control reaction (Trystram, Perrot, & Guely, 1995). Hence, fuzzy logic control is attractive for food processing since it can use both process conditions and an expert's knowledge (Zhang, Litch®eld, & Bentsman, 1993). Thus in brewing new practical ways of accelerated fermentation and maturation are made possible.
*
Corresponding author. Tel.: +353-21-903645; fax: +353-21-903091. E-mail address: [email protected] (C. Riverol).
High level beer quality must always be the prime target of the brewer. Alterations, therefore of any fermentation parameter in order to achieve a shortening of production time can only be accepted without signi®cant change in beer quality. The wide knowledge of the different fermentation parameters and their eect on beer quality is a basic tool the brewer. The technological parameters the brewer can apply to in¯uence fermentation include: · Wort composition. · Aeration rate. · Pitching rate. · Yeast strain. · Fermentation temperature. · Pressure. Temperature control is a practical problem in production-scale brewery fermenters, when the vessels are very big and is not possible to immediately follow changes in the temperature set point pro®les. Since in practice the control strategies are very simple, considerable temperature ¯uctuations and delay times may be observed in big fermenters. Temperature is the variable by which the fermentation process in beer breweries is controlled. Hence, the temperature control is of considerable importance for
0956-7135/01/$ - see front matter Ó 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 6 - 7 1 3 5 ( 0 1 ) 0 0 0 6 7 - 6
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Nomenclature A; B; C matrices in space state Ap area Fd fault parameters Gf sensor parameters Kf actuator parameters r residual vector r1 reaction rate for yeast r2 reaction rate for wort any quality assurance of the beer fermentation process, in particular for a reliable prediction of the process behaviour, which becomes necessary if the length of the fermentation process and thus the tank logistics are to be controlled. Several cognitive approaches show great potential to control or optimise the fermentation stage in beer production. The reason for the usage of cognitive methods in brewing technology lies in the complexity of the matrix wort or beer and in the characteristics of interesting processes, which are mostly dynamic and nonlinear. Therefore, most of the processes and the matrix are, in eect, not describable with mathematical methods. However a lot of technological knowledge or large quantities of data exist which can be used for cognitive approaches e.g. based on fuzzy logic or arti®cial neural networks. Intelligent control systems will become necessary in the near future because of increased needs in production and maintenance of controlled processes and machines. Many of the current control systems are unable to deal with these demands. In control systems of the next decade the key features and terms will probably be adaptivity (to enable high utility and easy maintenance), rapid development (to reduce the developments costs), and predictive fault diagnosis systems together with versatile monitoring systems (Rauma, 1997). New technology used in industry tends to be a blend of PC, PLC and DCS. There exists a choice of mini-DCS, powerful PLC-type control, system PLCs with enhanced continuous control capability or soft control where everything is carried out on a PC. One thing is certain; the hardware platform is becoming less relevant and less recognisable as software is acknowledged as the key element of a controller. Almost all control is now soft. It is only the platform and operating systems that vary (Higginson, 1998). One driving force behind this new technology is the development of chips. These are doubling in size every 18 months to 2 years. With these chips, DCSs are being developed with PLC features and vice versa. These are useful in the food industry where a large variety of control is necessary (Williams, 1998). Fuzzy logic methods are also a way of the future, as it is easy to implement and has a wide range of potential
T Tj u U V Vp y q l
temperature jacket temperature input vector overall heat transfer coecient projection matrix volume measurement vector density growth rate coecient
applications in the food industry. Many software packages exist based on the principles of fuzzy logic and can be implemented with many of the previous hardware and software platforms. Some of the most common are: · fuzzyTECH. · CubiCalc. · TILShell. · FIDE. · RT/Fuzzy. · Fuzzy Knowledge Builder. · Fuzz-C. Fuzzy control techniques are applied successfully in many fermentation processes even though the idea of using fuzzy set theory to control biotechnology processes appears to be a contradiction (Gvazdaitis et al., 1994; Halme, 1988; Ming, 2000; Nyttle & Chidambaram, 1993; Postlethwaite, 1989; Simutis, Havlik, & Lubbert, 1993; Venkateswarlu & Gangich, 1995; Yuan, Miao, & Li, 1999; Zhang, Visala, Halme, & Linko, 1994). Fuzzy techniques make it possible to use observations and process knowledge in computer-based control systems. Fuzzy systems oer considerable potential for increasing automatic control in the food industry. They can be based entirely as fuzzy techniques or combined with conventional control techniques in hybrid systems (Mittal, 1997, Chapters 6 & 8). These advanced controls are better able to complement classical techniques for companies that continually seek to lower costs and shorter product development cycles (Bartos, 1997). Fuzzy logic control is attractive for the control of food processes because it can: · Handle multiple control objectives simultaneously. · Utilise fuzzy information in analysing complex processes. · Take historic process data into decision-making consideration. · Evoke skilful operator's experiences and perform approximate reasoning in process control. · Mathematically model process uncertainties. · Be economic to use as the development time is much shorter and easier with lower engineering eorts then traditional methods (Froese, 1993).
B. O' Connor et al. / Food Control 13 (2002) 23±31
2. Selection of software As previously mentioned, there are many dierent types of software packages which are based on the principles of fuzzy logic e.g. fuzzyTECH, CubiCalc, TILShell, FIDE, RT/Fuzzy, Fuzzy Knowledge Builder and Fuzz-C, to mention but a few. As well as these commercial packages there are many packages for research and teaching only. Many research institutes develop their own generic fuzzy software suitable for their own individual needs. The many software packages that exist vary greatly in their functionality, cost and compatibility with hardware and other software packages. The initial work with the brewery determined a broad outline of the project requirements and these were used to select a suitable software package. The package must complement the previous control system and communicate with all the present control systems in the brewery. This package must also be Microsoft Windows compatible. Each package was analysed under headings of functionality, support, expansion capability and value. Shipping costs also had to be included into the overall cost of the package. While each package has its own advantages and functions, the software Fuzzy Knowledge Builder, Fuzzy Judgement Maker and CubiCalc were deemed not suitable, as none of them would have the functionality to support a project to be implemented in industry. The three remaining possibilities were TILShell, FIDE and fuzzyTECH. The software used in this project is fuzzyTech (version 5.01) from Inform and InTouch (version 7.0) from Wonderware. This project used a personal computer (PC) Gateway 2000, G6-266 with 128 MB of main memory (RAM) and 6 GB of hard disk, running under MS Windows NT version 4.0. The fuzzyTECH (fuzzyTECH, 1997) provides code generators for C and assembly, as well as interfaces to industrial process control systems (InTouch, TheFIX, FactoryLink, WinCC, Foxboro-IAS, DIGIMATIK, Freelance2000, Contronic, Citect and LabVIEW). Dedicated fuzzyTECH's Runtime Modules are also available for Programmable Logic Controllers. 3. Case study 3.1. Fermentation control system In the brewery there are 6 fermenters of 1300 and 18 1000 HL fermenters. Each fermenter has a glycol jacket for cooling process that works automatically. The model was based on a beer product with a temperature setpoint of 16 °C and a PG endpoint of 1015. The model was called Ferment-Stop and created using the fuzzyTECH wizard. The model consists of two inputs, Temperature and PG, with the output being Process. These projects
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were developed further with many dierent adjustments. This is a very tedious procedure. After each adjustment is made, a back check involving a complete computation of a set of data must also be made to ensure that the decided position for term 1 (less Setpoint) with variable 1 (Temperature) is still allowable if term 2 (Cont O Cont) in variable 3 (Process) is adjusted and so on. Table 1 shows a summary of the parameters used. The rules were developed in the IF THEN format. Nine rules were automatically created by the wizard. Only six rules were necessary to cover each possible scenario of the fermentation process (see Table 2). The expected result is that the fermentation should continue until the desired PG endpoint is reached. The wizard automatically generated the number of rules, which corresponded to a permutation of all possible combinations of the input variables. This model generates 36 rules, 3 3 4, which is based on a permutation. This is calculated using the number of terms of each input variable multiplied by the number of output terms. These rules are generated automatically, so many of them are not possible when the constraints of the fermentation process are taken into account and many of them are also unnecessary. This project used generated data from both the PLC and fuzzyTECH and also process data from the Brewery. The data from the PLC demonstrates the ease with Table 1 Summary of variables and their parameters used in Ferment-Stop Variables
Terms
Ranges
Temperature
Less Setpoint Setpoint Greater Setpoint
0±16 °C 14±18 °C 16±32 °C
PG
Less Setpoint Approx Setpoint Setpoint
960±1010 985±1012 1010±1060
Process
StopCool ContOCool ContCool Continue
0±1.0 0.4±1.2 0.8±1.6 1.2±2.0
Table 2 Inference rules used in the fuzzy controller IF
1 2 3 4 5 6
PG
Temperature
Less Setpoint Less Setpoint Less Setpoint Less Setpoint Setpoint Setpoint
Less Setpoint Setpoint Greater Setpoint Setpoint Less Setpoint Setpoint
THEN Process StopCool Stopcool ContOFFCool ContCool Continue Continue
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which the model may be connected to a PLC, but the data is just randomly generated values. These are selected to remain within the designated ranges. The brewery data provided the best results, as it is real data. Using the data as inputs a decision on the fermentation process is computed and the results are displayed on the mimic panel. This particular fermentation was commenced on 25 November 1997 and completed on 1 December 1997. The information logged in the brewery for each individual fermentation process consists of the date, time, real-time temperature value, temperature setpoint, realtime PG value, and PG endpoint. The two new columns are information coming from the fuzzyTECH model, the Process value in numeric form and the corresponding linguistic variable. See Table 3 for initial 10 data inputs and their corresponding fuzzy model outputs. A fermentation process for a beer should be between 5 and 8 days for a stout or ale product and between 7 and 12 days for a lager product. The time duration of data set was approximately six and a half days. The endpoint for the brewery data is the point when the temperature setpoint was changed to 4 °C, as this is the ocial end of the fermentation stage. The endpoint for the model is considered the point when the PG value reaches the value 1015. This includes 1015.99 and usually anything less than 1016. The advantage of fuzzy is that it is not an exact cut-o point between 1015.99 and 1016. Sometimes the stop and cool value will not be computed until a lesser value is reached, such as 1015.33, depending on the other values of temperature and other in¯uences. This is the basic principle of fuzzy
logic. There are overlaps generated in the MBFs, which gives results other than hot/cold or on/o. This model can easily be modi®ed for dierent products or even dierent quality parameters, without having to devise new complicated algorithms. This is a clear advantage over conventional control systems. The linguistic variable remains the same until the variable changed to ContCool, which translates as ``continue fermentation process with cooling system on'' (see Table 4), as the temperature is at 16 °C and the PG value is at 1029. Even though the temperature is at its setpoint, the computed output decides that the cooling system is necessary. This might seem a contradiction but if the data is analysed it can be seen that the temperature stays within a range of 16.1 °C and 15.7 °C for the ContCool period. The theory behind this idea of using fuzzy systems to control a fermentation process seems contradictory, but it allows qualitative information to be used in controlling the process. The Fig. 1 shows as the data ¯ows along of the process in the fermentation room. 3.2. Fault detection In this section we outline the general procedure of fault diagnosis using residual generation, this method depends upon the quality of the model of the system. In this case, we need to create a model for design the fault detection system. Biochemical reactors are used to produce a large number of intermediate and ®nal products, including
Table 3 Sample of data Temperature (°C)
Set temperature (°C)
PG
PG Endpt
Process
Decision
16.1 16 15.9 15.9 15.9 15.7 15.7 15.9 15.6 15.7
16 16 16 16 16 16 16 16 16 16
1052.3 1052.3 1052.3 1052.3 1052.3 1052.3 1052.3 1052.3 1052.3 1052.3
1015 1015 1015 1015 1015 1015 1015 1015 1015 1015
1.12235 1.1247 1.1247 1.1247 1.1247 1.1247 1.1247 1.1247 1.1247 1.1247
ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool ContOFFCool
Table 4 Extract of data set Temperature (°C)
Set temperature (°C)
PG
PG Endpt
Process
Decision
15.9 15.9 16 15.8
16 16 16 16
1031.5 1031.5 1029 1029
1015 1015 1015 1015
0.95645 0.95645 0.893 0.893
ContOFFCool ContOFFCool ContCool ContCool
B. O' Connor et al. / Food Control 13 (2002) 23±31
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fashion. Notice that l is the ®rst-order at low x2 and zero-order at high x2 . l x2 l max : 6 km x2 That is, when x2 is low l x2 l max km and when x2 is high l lmax : Since the reaction rate is
Fig. 1. Process control information architecture.
r1 lx1
beverages. Biochemical reactor models are similar to chemical reactor models, since the same types of material balances are performed. In the simplest reactor, we consider two components: biomass and substrate. The biomass consists of cells (yeast) that consume the substrate (wort). In this module we assume that the reactor is perfectly mixed and that the volume is constant (Breusegem, Thibault, & Cheruy, 1991; Bequette, 1998). The dynamic model is developed by writing material balances on the biomass (yeast) and substrate (wort). Biomass grows by feeding on the substrate. The biomass balance in a batch reactor is written as dVp x1 Vp r1 : dt
1
The substrate material balance is dVp x2 Vp r2 : dt
2
The reaction rate (mass of cells generated/volume time) is normally written in the following form: r1 lx1 ;
3
where l is the growth rate coecient. We can think of l as being similar to a ®rst-order reaction rate constant; however it is not constant (it is a function of the substrate concentration). The unit of l is time 1 . There is a relationship between the rate of generation of biomass and the rate of consumption of substrate. De®ne Y as the yield, that is, the mass of cells produced per mass of substrate consumed: r1 Y : 4 r2 For a constant volume reactor, substituting all the equations yields: dx1 lx1 ; dt dx2 lx1 : dt Y
5
The growth rate coecient is usually not constant; the growth rate coecient often varies in a hyperbolic
7
this means that the description is similar to a secondorder reaction when x2 is low since l x2 x1 r1 max 8 km and to ®rst-order reaction when x2 is high since r1 lmax x1 : The energy balance in the tank is dT U Ap T Tj : dt Vp Cp q
9
10
A leakage ¯ow is modelled as an extra term however; the presence of noise makes it necessary to use ®ltering. Typically, the residual generation problem can be stated as follows. Consider a dynamic system with a known nominal mathematical model as, for example (space state) x_ t A x t B u t Ed t Kf t; y t C x t Fd t Gf t;
11
where x is the state vector, u the known input vector, y the vector of measured outputs and A; B; C known matrices of appropriate dimensions. The term Ed models the unknown inputs to the actuators and to the dynamic process; Kf actuator and component faults; Fd the unknown inputs to the sensors and Gf sensors faults. Notice that A; B and C are nominal matrices of the system. Since the faults that are principally re¯ected in changes of A; B; C as well as modelling errors, are considered by f and d associated with proper choices of E; F ; G; K. The initial model can be written as space state. The variety of fault modes that can occur may be classi®ed as follows: 1. Abrupt fault modes. 2. Incipient (slowly developing) faults, e.g. bias or drift. Typically, abrupt faults play a role in safety-relevant systems where hard-failures have to be detected early enough so that catastrophic consequences can be avoided; incipient faults are of major relevance in connection with maintenance problems where early detection of worn equipment is required. In this case, the faults are typically small and not as easy to detect, but the
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detection time is of minor importance and may therefore be large. Given the actual input vector, u t, and the measurement vector, y t, suppose that a residual vector r t exists that carries information about a particular fault. Find an algorithm that generates r t when the fault has occurred, under the following conditions: · The mode (time evolution) of the fault is unknown. · The mathematical model of the nominal system is uncertain. · There is system noise and measurement noise. · The residual generation has to be performed within a speci®ed time. Of course, there are many modi®cations of the problem statement depending upon the given situation and the particular purpose of application. For the detectability and distinguish-ability of a fault the following conditions must hold: · Knowledge of the ``normal'' behaviour of the nominal model A; B; C. · De®nitiveness of the ``faulty'' behaviour. One of the methods of residual generation best known is the Parity space approach (Basseville & Nikiforov, 1993; Frank, 1990; Willsky, 1976). The idea is to check the parity (consistency) of the mathematical equations of the system by using the actual measurement (10). A fault is declared to have occurred once preassigned error bounds bi are surpassed. To outline the basic idea of the parity space methodology we consider ®rst the simpli®ed case of redundant measurements that may be obtained directly. Let them be modelled by the algebraic measurement equation y Cx Dy; where y is the measurement vector, C the measurement matrix, x the true measurement value and Dy the error vector Dyi > bi , de®nes a faulty operation indicated by the ith measured variable. For detection of Dy the vector y can be combined to a set of linearly independent parity equations given by p Vy:
12
where p is the parity vector. The projection matrix V is now determined such that VC 0; V tV I VV t I;
1
C C t C C t ;
r V t p:
15
This procedure was applied to beer type Lager only as the tank is not yet pressurised. To save space, we made a resume in three ®gures and two tables where the results of two weeks are shown. As a starting point, the three dierent leakages used in this article are shown in Tables 5 and 6 shows the detected leakages. These leakages were used for to test the controller and the fault diagnosis method. The residual vector without faults is assumed to be zero mean and with constant variance. When a failure occurs, the parity of the residual is modi®ed for a jump. The residual evaluation permits these changes to be detected. In the present case, the possible faults transform the residuals by a jump of mean, which is easily detected by us. As appropriate safety measures in the case of a malfunction of the controller is provided with supervision and evaluation algorithms to detect this malfunction. Fig. 2 shows the response of the system to a leakage type ``to the atmosphere''. This type of fault is not the most common in the brewery and if is not detected rapidly can result in serious damage in the production. The deviations of temperature are small. In the case of the temperature a maximum of 0.54 °C is obtained, and in the pressure 5.78 kPa; in both the deviations are less of 1% of the corresponding reference values. An example of the system responses to the faults in real time is shown in Figs. 2±4. These ®gures demonstrate that fault detection via state space approach is good because the technique assumes that the process model is quite good. Errors in the model cannot be interpreted as faults, thus yielding false alarms.
Table 5 Leakages Leakage
Possible consequence
To the atmosphere From ammoniac network No leakage
Loss of CO2 Over pressurised tank ±
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i.e. the rows of V are orthogonal, V is a null-space of C. Hence p V Dy:
with each measurement. The q-dimensional residual vector, r y Cx, where x C t C 1 C t y is the least squares estimate of x, is related to the parity vector p as
14
This reveals that the parity equations contain only the errors due to the faults, independent of x, which is not directly measured. Moreover, in the parity space, the columns of V de®ne q distinct fault directions associated
Table 6 The detected leakages Conclusion
Real leakage
To the atmosphere No leakage To the atmosphere Ammoniac network
To the atmosphere No leakage To the atmosphere Ammoniac network
B. O' Connor et al. / Food Control 13 (2002) 23±31
Fig. 2. Response of the system when there exists a loss of CO2 .
Fig. 3. Response of the system as for temperature with a leakage type loss CO2 .
Fig. 4. Response of the system with respect to temperature with a leakage type ammoniac network.
Probably the most interesting fact is in Fig. 4 where a fault in the jacket can yield high oscillation in the system. In this case, osets of 2 °C were detected.
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However, the predictability of the extract degradation and its reproducibility depend on the accuracy of the temperature. The variations of 2 °C in the fault detector can be translated to the controller as ¯uctuations of 1 °C as the fuzzy controller guides the temperature within a given tolerance band that minimises the cooling under the condition that quality parameters stay within guaranteed intervals. Since it has been observed that the dynamic behaviour of the big fermentation tank is different at dierent temperature intervals, the parameters of the model have been separately identi®ed for intervals around the normal fermentation temperature. The two dierent temperature intervals are 16 °C > T > 10 °C and 10 °C > T > 4 °C. In the ®gures it can be seen that both temperature and pressure, go to steady-state after a loss of CO2 (open valve). In approximately 3 min mean the system reduces the disturbance. The system was tested during two weeks using all the types of leakages. The changes in the temperature into tank are few using the fuzzy controller as the controller acts over the ammoniac ¯ux taking the centre of gravity of the combined distribution of the temperature. The results clearly explain the improvements obtained with the fuzzy controller and the fault diagnosis method. The attenuation obtained was of 77% but in the classic system was 75.6%. In the fuzzy system the ¯occulation was complete and ®nishes the reducing of all diacetyls. In the classic method generally, the ¯occulation is complete and light butterscotch ¯avour is obtained when the system faults. 3.3. Process optimisation of fermentation For 10 fermentations a series of seven individual parameters were monitored. These included temperature, yeast count, present gravity, pH, bitters, colours and alcohol content, see Fig. 4. These parameters were divided into two distinct categories, i.e. input parameters and output parameters. Input parameters were de®ned as those whose pro®les are not aected by the other parameters and which may be directly controlled over the course of the fermentation. Output parameters then, are those in¯uenced by the input parameters. The development of the model focused on the eect of temperature on other measured parameters of the fermentation. The measured output parameters aected by temperature were alcohol content, bitters content, colours, present gravity, pH and yeast count. The model was used to create fuzzy outputs or predicted output values for each of these variables. Actual and predicted values were then compared for all trials. Good correlation was found between predicted and real data for most parameters measured. Best correlations were obtained for predicted and measured values for present gravity and pH. Poorer correlations were obtained for bitters and colours pro®les however maxi-
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B. O' Connor et al. / Food Control 13 (2002) 23±31
mum errors between these predicted and measured data sets were relatively small. Yeast cell count pro®le was one of the less successful parameters modelled in terms of correlation coecients however the general trend of the predicted model matched the real growth curves in most of the trials. In summary, the fuzzy logic model using temperature as the sole input was best at predicting wort present gravity and pH values. Figs. 5 and 6 illustrate comparisons of real and predicted values for present gravity and pH, respectively. This value provides the end point of the process and this data is used in the development of a predictive/decision model which will constantly monitor the fermentation process and provide automatic decisions on how the fermentation should proceed ± i.e. continue, stop or cool the fermentation process. This will result in
Fig. 5. Real and predicted wort present gravity values.
Fig. 6. Real and predicted wort pH values.
increased automation and consistency in the fermentation process. 4. Conclusions and future perspectives The objective of this project was to develop a control system, failure detection system and predictive/decision model (optimisation process) for a brewery using fuzzy logic, speci®cally in the fermentation area. The software package fuzzyTECH from Inform provided the fuzzy logic controller, while InTouch from Wonderware provided the user front-end. The implementation of the system will depend on the brewery and their willingness to invest in the project, to purchase the necessary hardware and software components to integrate the fuzzy logic model into their present control system, to either replace or augment the system. These results clearly demonstrate the bene®ts of using a fuzzy logic control system. The use of the fuzzy logic model provides many advantages over conventional control of a fermentation process in the brewery. This control should provide a good quality, consistent beer, produced economically, while minimising the level of undesired ¯avour components. It should also reduce beer and raw material losses and provide a more predictable fermentation vessel residence time to ensure the proper application of a realistic scheduling package to maximise production capacity. The disciplines of fuzzy logic have enormous bene®ts for the fermentation process in a brewery: · This model can control a complex non-linear process, which is otherwise dicult to control. · Reduce fermentation time and increase production. · Produce a more consistent quality beer. · It allows on line modi®cations, without dealing with algorithms. · It allows easy integration into many SCADA packages. · Allow for a better scheduling package to be implemented. Technology is evolving at a rapid rate in food processing. 50 years ago there were no computers in the industry. Now they are commonplace, from the personnel department to processing and warehouse departments. Each department has their own computerised system. Some of them link each department together. Advanced control systems are the way of the future and it is only a matter of time before fuzzy logic is used in all industries in the Western world just like in the Japanese industries. Fuzzy logic will be especially useful to the food industry as it can deal with non-linear processes and variations in raw materials that are widespread in the food industry.
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