Developments in Monitoring and Control Of

7/30/2019 Developments in Monitoring and Control Of

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DEVELOPMENTS IN MONITORING AND CONTROL OF

FOOD PROCESSES

S. LINKO and P. LINKO

Laboratory of Bioprocess Engineering, Helsinki University of Technology, Espoo, Finland

Recent developments in advanced control techniques have opened up novel possibilitiesfor food process control. Food processes have been particularly difcult to automateand control owing to nonuniformity and variability in raw-materials, and lack of

sensors for real-time monitoring of key process variables and quality attributes. Model-basedcontrol, distributed control systems together with eld communication protocols, and othercomputer-aided advanced control strategies are already widely used in chemical processindustries, and have proven themselves in selected food processing applications. The benetsof advanced control techniques include reduced costs, increased quality, and improved safety.Fuzzy logic and neural networks provide convenient means for dealing with uncertainties and

highly non-linear events typical of biological processes. Nevertheless, replacing the humanexpert by computer-aided systems in bioengineering has been slower than in other processindustries, and there are few published landmark cases. This paper discusses the potential ofsuch novel tools in food process control.

Keywords: advanced control; distributed control; expert system; food process control; fuzzylogic; neural network

INTRODUCTION

In food process control, the main objectives are food safety,high quality, minimal processing, and high yield at minimal

costs1 , 2 . Moreover, there is an increasing public awarenessand a growing demand for improved standards. High-quality products require accurate and reliable instrumenta-tion, fault detection techniques, and on-line control ofprocess variables in order to meet both industry andgovernment specications

2. Intelligent computer systems

capable of modelling and real-time simulation of entire foodprocessing operations from production planning to processcontrol have been visualized as future goals. Food processesare, however, especially difcult to automate and controlowing to the variability in raw materials, and lack of meansfor real-time measuring and monitoring of key food process

variables and food quality attributes

1 4

. Control becomesparticularly difcult when there are interactions betweenmanipulated and controlled variables.

Advanced process control techniques have been widelyapplied in the chemical, petrochemical and forest-basedindustries, after the apparently rst computer-aided processcontrol system was installed in 1959 in an oil renery inPort Arthur, Texax5 , 6 . In general, computerized controlsystems in the food industry have been recently comprehen-sively discussed7 . There is no doubt that advanced,intelligent control techniques such as model-based, expert,neuro-fuzzy and hybrid control systems would offerparticular advantages also in food and allied processes1 .

Investments in automation, robotics, and advanced controltechniques are likely to result in marked savings in costs,increased productivity, improved and more consistentproduct quality, and increased safety

8. In a recent Australian

study, substantial benets were realized from proposedadvanced control systems9 . Of the seven industrial casestudies, the food industry was represented by a sugar

renery. Consistent product quality would benet from theadaptability of the control system to the changing environ-ment but such a level of integrationof control systems is stillrelatively uncommon in the food industry. Consequently,the interest in advanced control strategies in food processengineering to improve food manufacturing and quality hasbeen rapidly growing during the last few years1 , 2 , 4 ,1 0 1 2 .This paper discusses some recent developments in advancedcontrol techniques for food processing applications. Table 1describes some of the nomenclature used.

WHAT IS ADVANCED CONTROL?

Advanced control (and computing) can be dened inmany ways, depending on the point of view. For example,any control strategy other than PID control or one thatis implemented by using a computer has to be called`advanced1 2 . In this paper, advanced control is consideredmainly as intelligent, computer-assisted, model-based con-trol. In general, the introduction of advanced computing andcontrol techniques is expected to improve process prot-ability and business competitiveness. Furthermore,advanced control techniques aim to ensure more stableoperations which respond appropriately to the changingrequirements, while improved management information and

decision support systems through advanced control bringsadditional benets. Nevertheless, the driving force ininvesting in advanced control systems should not be justto install high-tech control but to increase prot. Pay-back

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times of less than one year have not been uncommon ininvestments to advanced control.

Advanced control techniques include a wide variety ofmethods from model-based predictive controllers to intel-ligent and `software sensors, neuro-fuzzy control andexpert systems. Many recent advances in process controlhave been mainly a result of inexpensive computingthrough microprocessors and intelligent sensor technolo-gies, resulting in improved operational efciency. Casestudies have shown that such benets can be achieved cost-effectively, especially where a modern control and monitor-ing infrastructure with programmable logic controllers(PLC), distributed control system (DSC), and supervisorycontrol and data acquisition (SCADA) system is already in

place. Moreover, new tools of articial intell igence suchas fuzzy logic, neural networks, and knowledge-based(expert) systems offer novel solutions to efcient andreliable real-time control. They provide a novel means forestimation and prediction of key process variables whichare conventionally not measurable on-line. Hybrid systemsare based on several techniques such as conventionalprogrammin g, expert knowledge, fuzzy logic, neuralnetwork, and so on.

Although advanced control can be taken to cover thewhole food manufacturing system including quality assur-ance, management i nformation systems, purchasing andsales and scheduling, this paper only deals with the

production process itself. A detailed review of the stateof the art and discussions on the theories behind thedifferent schemes of advanced process control is beyondthe scope of this paper.

PID, PLC, DCS AND SDS

Conventional PID (proportional-integral-derivative) con-trollers have been used for more than half a century

1 3. They

are affordable, robust, relatively easy to use, tune andmaintain, and generally commercially available. Essen-tially, a PID controller reads sensor information, and makesthe necessary changes to control-device actuators on thebasis of a built-in algorithm. Inexpensive, programmablelogic controllers (PLC) with built-in PID loop functionsprovide, in many cases, the basic units for process control.Furthermore, many controllers now offer the possibility oftuning on the basis of demand. Their main disadvantage isthat they perform well only with processes of linear, low-

order kinetics, while typical nonlinear bioprocesses changetheir behavi our whenever their process variables change.Nevertheless, PLCs have been credited with one ofautomations biggest advancements, and a PLC coupledwith smart transmitters and colour graphics user interfacerepresented a revolution in process control1 2 . The change-over from inexible, conventional hard-wired relays, timersand switches to software-based control has resulted inmarked savings both in space and costs, eliminated time-consuming relay mounting and wiring, and brought aboutthe possibility of quick and easy modications in the controlstrategy with changing production needs. Highlightingsuccessful examples from the food industry, PLC interfaced

to workstations have been used to control packaging lines ina bakery1 5 and to control the refrigeration system in a meatprocessing plant

1 6.

The interest in exible distributed control systems (DCS),

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Table 1. Nomenclature.

AI Articial intelligence. A eld of computer science aiming at the possibility that acomputer could behave `intelligently the way a human expert does.

ARMAX Auto-regressive moving-average with auxiliary input.CAM Computer-aided manufacturing.CBR Case-based reasoning. Programming in which knowledge is stored in the form of

experience or cases.CIM Computer-integrated manufacturing.DCS Distributed control system. A computerized controller that is based on several

coordinated processors.ES Expert system. A knowledge-basedcomputer system imitating the performance ofa human expert in problem solving.

FC Fuzzy control; FL, Fuzzy logic. The process of solving problems that deal withambiguous data using multivalued logic, expressing all things as a matter ofdegree through membership. functions that receive values between 0 and 1.

GMC Generic model control.GUI Graphical user interface.I/O Input/output.IPS Integrated production system.KBS Knowledge-based system.LAN Local area network.MIMO Multi-input multi-output.MPC Model-predictive control. A multivariable control strategy that depends on a

mathematical model to predict the future state of the process variables.NN Neural network(sometimes ANN for articial neural network).

PC Personal computer.PID-loop A linear single variable controller that generates its control efforts on the basis of a

proportional-integral-derivative algorithm.PLC Programmable logic controller. A controller typically programmed using Boolean

or ladder logic.SCADA Supervisory control and data acquisition system.SDS Smart distributed system. A modular integrated PC-based control system.


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consisting of a group of PLCs and an operator interface witha video display for viewing and manipulating of the processvariables distributed from the input/output electronics, hasrecently increased. With the DCS, modications in controlstrategy frequently require only software changes, resultingin marked savings both in costs and time. In chemical andwood pulping processes, feed forward control has alreadybecome routine, and the combined use of DCS and PLCs

with an open architecture is becoming more and morecommon. A recently described example of the installation ofa microprocessor-controlled DCS in a food plant is theGerman liquid sugar producer Amino GmbH1 7 . To stabilizethe process, feed forward control is commonly employed inDCSs to compensate for an upstream disturbance before itaffects a controlled. Conventional cascade control is usedwhen there is a need for one controller to adjust the set-pointof another to compensate for changes in a second variable.Where continuous feedback of nal product quality is nottechnically feasible, statistical process control (SPC) isrecommended to maintain product quality.

In food process control, control hardware is often

distributed over long distances and sometimes has tooperate in a hostile environment. There is a trend towardincreased levels of communication through multi-layernetworks to provide up-to-date information to the operatorand management in order to help ensure that the processruns efciently and reliably. There is no doubt that DCS-based computer-integrated manufacturing (CIM) withinformation transfer via local area networks (LAN) islikely to increase in popularity in the near future in t he foodindustry, resulting in major cost savings

1. A typical modern

installation would consist of a digital eldbus with manynodes from I/O devices to PLCs, to message displays, andPCs with colour graphics

1 8. Fieldbus is a generic term which

describes a new digital, two-directional communicationsnetwork used to link isolated eld devices such astransducers, sensors, controllers and actuators. Each elddevice has a low cost computing power, which enables theexecution of simple functions such as diagnostics andcontrol. With more and more open systems being installed,such advanced communication protocols are becomingincreasingly important. However, until recently, the lack ofeldbus standardization has somewhat limited the progress.

According to Caro and Morgan1 food processes wouldbenet from the eldbus, which enables the integration ofmodern smart sensors with inexpensive advanced control

systems. As an example, in a packaging line controlled byPLCs, the remote PLCs communicate with a master PLCthrough a two-way digital bus which also sends data to alocal display panel providing status information1 9 . Eachlines master PLC is connected to a higher level bus, whichhas a high capacity to transmit data from all differentproduction areas within the plant to the control centre. Atthe highest level, the eldbus is connected, for example, to aLAN such as Ethernet. An important advantage of eldbus-connected PLCs is the modular construction as new units ornodes can be easily added at will, and if one unit has to beshut down, others are not affected. Blevins and Kinney2 0

have discussed in detail the benets from a eldbus, using a

waste water remediation plant as an example. Honeywellrecently enhanced its smart distributed system (SDS) tomodular integrated PC control with an open network,bringing real-time communications to sensors and controls

in machine intensive plants typical of food and beverageproduction as well as packaging2 1 . This eliminates the needfor PLCs, with an increased exibility and functionality atdecreased costs

2 2. Smart distributed systems have been

since installed in canned and packaged foods in Canada, andin biscuit production by Nabisco.

The brewing industry has been at the forefront inintroducing automation to the food and beverage produc-

tion. Digital eldbus technology was recently introduced atthe Bitburger Brewery in Bitburg, Germany2 2

, with reportsof up to 40% cost reductions in cabling, commissioning,andmaintenance compared to the conventional analoguetechnology. Another example is a Guinness brewerywhere a new control system of an Integrated ProductionSystem (IPS) has been adopted2 3 . The whole process iscontrolled from a control room on the brewing oor wheresilent touch screens show every detail of the brewingprocess. The information to the control room is received viaa digital eldbus multiplexer, which transfers signals fromthe PLCs at local plant control to the mimic display. Due tothe eldbus, up to 70% savings in the system installation

costs could be realized. Touch screen operator interfaces areemployed also, for example, at the Stroh Brewery Companyin St Paul, Minnesota2 4 . The familiar Windows platformallowed installation within minutes, and the Windows-based QuickDesigner allows the simulation of push buttons,selector switches, displays and so on, on the screen; theoperator can select, eg., the CIP (clean-in-place) cycle withthe touch screen. Another recent Windows-based controlsystem is Wonderware InTouch developed in cooperationwith Microsoft. InTouch has been applied, for example, bythe Helsinki University of Technology in cooperation withVaPo Ltd, Jyvaskyla, Finland, to control an immobilizedcell bioreactor system

2 5.

PC AND SCADA

Ten years ago it was hard to imagine a PC on the factoryoor. However, with the phenomenal developments both insoftware and hardware, many advanced control systemscan now be achieved with a PC2 6 . Although PLCs aregenerally better at data acquisition, PCs excel in data analysis.Relatively recently, however, PCs have been programmedto be able to execute PLC-type real-time ladder-logic controlcalled `soft PLCs

2 7. Visual programming, object-oriented

software, and client/server networks have helped to increase

the popularity of PCs. The invention of the modern, graphicaluser interface (GUI) has brought the plant automation systema giant step forward 2 8 , and the key importance of a user-friendly, colour-graphics operator-machine interface inadvanced computer-aided process control systems can notbe emphasized enough2 9 . PC-based software can now alsoprovide a colour-graphics user interface, supervisory controland data acquisition (SCADA), and trend analyses, and isexpected to have a marked impact on process control. Quiterecently, the introduction of the Microsoft Windows NToperating system has brought PCs into part of DCS2 8 ,3 0 .Windows NT has the same look and feel as consumer PCsand, consequently, requires less operator training. Todays

Windows NT provides for support and documentation tools,video conferencing on the plant oor, and multi-tasking, multiprocessing, and the determinism required forsophisticated real-time applications.

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When a new SCADA concept was introduced to theGuinness Park Royal plant, the most critical area was to getthe SCADA and the PLCs to cooperate3 1 as a loss of just onebatch would cost more than the plants entire automationsystem. Market cost savings have also been realized byCarlsberg-Tetleys Alloa brewery as a result of theintroduction of a new SCADA system

3 2. Other examples

from the beverage industry are the modern open SCADA

systems based on off-the-shelf components, and recentlyinstalled at HP Bulmer, which has 53% of UK cider market,annually producing 50 million gallons3 3 , and the PC-basedcontrol system in the RL Seale & Co rum distillery ofBarbados3 4 . Networking across the production areasprovide for process control, system diagnostics, and thedownloading and management of recipes.

A smart, PC-based SCADA system has been installed, forexample, at Birds Eye Wall, Europes largest ice creamfactory in Gloucester, UK, producing up to 60% of Europesice cream

3 5. By optimizing compressor control algorithms,

marked cost reductions could be achieved. This requiredhistoric trend analyses, and real-time data available at the

remote PLCs. The refrigeration plant is managed by morethan 100 PLCs, and the SCADA system reads the data overthe sites Ethernet network.

When Sanders, one of the European leaders in animalfood production,wanted to establish a new plant in Chateau-Gontier in France, it also wanted an automation systemwhich would assist the operators through a user-friendlyman-machine interface3 6 . Sanders eventually rejected aconventional DCS solution in favour of a PC-based system,owing to savings in costs. Genesis real-time, multitaskingcontrol software running on a PC was selected for all theautomation, and PID loop control is used for the accuratemixing of expensive raw materials for consistent qualityandreliability. In this case, no PLCs were employed.

Johnson3 7

has reviewed the current developments incommercially available distributed control tools. In manycases, Windows NT has been chosen as the operatingsystem, allowing the DCS to be integrated with mostcommercially-available information networks such as aeldbus digital communication protocol, and with standardWindows-based word processors and spreadsheets. Forexample, National Instruments (Austin, Texas) newBridgeVIEW designed to monitor and control one or moredistributed units was developed in G environment to run onWindows 95 or NT, and includes SCADA, alarm and event

logging, historical data collection, and device servers tocommunicate with PLCs, etc.3 8

. Likewise, Alfa Lavals(Lund, Sweden) PC-based SCADA system with an openarchitecture was developed for the 32 bit Windows NTplatform

3 9. The supplier support service for process control

and automation systems is becoming increasingly availableworld-wide through the Internet4 0 .

ADVANCED MODEL-BASED CONTROL

PID-loops described above are the most common singlevariable or single-loop controllers. However, most foodrelated processes are multivariable, time-varying and non-

linear. Nonlinear processes are difcult to predict withconventional models designed for linear processes butproblems involving several process variables have beengenerally dealt with by multi-loop controllers running

several independent PID-loops concurrently. Few reportson advanced model-based food process control werepublished before the 1980s although the principles werealready reported in the 1960s, and in 1973 the Andersas(Paris, France) identication and command controller hasbeen claimed to have been the rst application to acommercial multivariable control problem. Variousmodel-based or model-predictive control (MPC) strategies

have more recently been applied, for example, to bakingovens, cooking extruders, grain dryers and nut roasters3, and

a number of commercial MPC packages are available7 , 4 1 .During the last decade, considerable interest has beendirected to adaptive linear controllers which, in principle,are based on an on-line estimation algorithm for automaticparameter adjustment when process conditions change. Thistype of controller is especially suitable for fermentationsand enzymatic processes, in which the biocatalyst activitymay vary unpredictably.

In model-based control, the behaviour of the process isrepresented by an analytical or empirical model and acontrol law is then established on the basis of the model.

The model is different for each application and is typicallygenerated off-line from experimental data. Detailed physi-cal principles that govern the process are not normallyrequired and the model should be accurate, simple, stable,and applicable for on-line control purposes, in order to beuseful in practice. In generic model control (GMC), theconstruction of a control algorithm capable of driving theprocess model along a reference trajectory eliminates theneed for tuning. The complexity of bioprocesses makes,however, modelling difcult and time-consuming, forexample, the modelling of the kinetic behaviour of anextrusion cooker4 2 . Recently, however, Elsey et al.4 3 havedemonstrated that the performance of MPC with an internalprocess model for optimizing its control action is farsuperior to that for PI control of a food extrusion cooker insingle-input-single-output control. Clearly, multivariatemodels may be employed to predict the quality of the endproduct and, with feedback via optimization, to give newset-points for process control. Although the general lack ofknowledge bases on food properties makes the prediction offood quality during processing difcult, applications arelikely to be on-line in the near future. That automatic controlof critical food quality variables is becoming a reality hasbeen demonstrated recently by Whaley

4 4who described in

detail a model-based control system for a cereal drying

plant.In modelling or system identication, empirical model-ling using time series enables the process output to berepresented by a linear, time-invariant, discrete-time modelwhich can be transformed into an ARMAX (Auto-Regressive Moving-Average with auXiliary inputs)format3 . Although an ARMAX model represents a linearrelationship between the inputs and outputs, in practice, thisis often sufcient even though the true relationship over therange of interest may be non-linear4 5 .

FUZZY KNOWLEDGE-BASED CONTROL

The most recent developments in control technology havefocused on user friendliness and on techniques in whichexact mathematical modelling of the process is not required.Only twenty years ago microprocessors and minicomputers

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began to take over control tasks and a decade laterdistributed control systems began to be commonplace, thelate 1990s are now witnessing fuzzy control (FC), neuralnetworks (NN), and various hybrid systems coming intoeveryday life. A fuzzy system can be constructed to mapinputs to outputs as any conventional computing system4 6 .According to Waller

4 7, fuzzy logic brings simplicity to the

system, when great accuracy is not really needed. It allows

the application of heuristics in the form of rules in thecontrol of complex problems, and neural networks bring theability to learn to the system. Furthermore, FC can deal wellwith nonlinear and task-oriented problems which has led tothe wide availabi lity of hybrid PID-fuzzy controllers. It is anenabling tool for automated supervisory control because itconverts human operators strategies to direct use4 8 . If sucha controller is used for set-point regulation, for example, intemperature control, fuzzy logic is usually employed tosupervise a conventional PID-controller4 9 . Although Kingand Mamdani had already described a simple fuzzyalgorithm to control the temperature of a stirred tank inthe late 1970s, the rst industrial application of fuzzy logic

was apparently a cement kiln controller developed inDenmark in the early 1980s. Other known early examplesare the fuzzy train control in Sendai, Japan developed byHitachi, and the control of the petrochemical plant Atlan-tecin Germany, using Informs fuzzyTech software. Accordingto Whitney5 0 , the fuzzy expert (more appropriately: knowl-edge-based) system Linkman has saved millions of dollarsannually in fuel costs alone for Blue Circle Ltd in the UK.Knowledge-base is the part of an ES that consists of factsand heuristics about the domain of interest.

Fuzzy (continuous) logic was introduced by Lofti Zadehin 1965. The theory of fuzzy sets provides convenient meansto deal with uncertainties and to convert subjective expertknowledge into quantitative functions which can beprocessed by a computer. An advantage in the context offood processes is that no complex mathematical relation-ships are required in the construction of a fuzzy logiccontroller (FLC). The principles of fuzzy control have beendescribed in detail for example by Yamakawa

4 6. A FLC

manages a complex control surface by heuristics instead of amathematical model. Fuzzy models can be written in theform of easy to understand linguistic rules of the type Ìf...(antecedent), then... (consequence), similar to the rules inexpert systems (ES). A rule is a conditional statementrepresenting heuristic reasoning in which the rst part ìf...

establishes the conditions that must apply in the second partand `then... is to be acted upon. Together with thecorresponding membership functions that describe thedegree, within a scale of 0 to 1, with which an elementbelongs to a set, a group of rules constitutes a fuzzy model.In most commercial FLCs, relatively simple triangular orGaussian membership functions are used.

In Japan, `neuro-fuzzy has already become a `householdword. Most commercial fuzzy logic applications are relatedto control, and today fuzzy logic is used in cameras tocontrol automatic lens focusing; in washing machines toselect the cycle time; in cars to select the gear for automatictransmission or to control the automatic braking system; and

in videos, vacuum cleaners, lifts, etc. In 1991, Matsushitaalone sold over one billion US dollars worth of fuzzy logicbased products in Japan, and by 1996 Japanese `fuzzyexports reached about US $ 90 billion. The early food

process related applications of FLC have been described byLinko and others5 1 , and by Dohnal5 2 .

In the mid-80s, fuzzy modelling was suggested to controla at bread extrusion cooking process, which was probablythe rst fuzzy control application to a food process5 3 ,5 4 . Afuzzy expert system was then built for extruder control,based on the object oriented SmallTalk/V programmingenvironment implemented on a PC. This was expanded to a

fuzzy ìf..., then... rule-based start-up and shut-downcontroller which was tested on a production scale twin-screw extruder in cooperation with the Federal ResearchCentre in Detmold, Germany, using at bread production asthe model process5 5 . A convenient GUI with colourgraphics was programmed for easy operation and all keyprocess variables are shown in the form of dynamic gauges,in which the arrows change in colour from green (within set-points) through yellow and orange (within given tolerances)to red (outside tolerances, indicating alarm). The systemalso provides for the denition of fuzzy sets with triangularmembership functions for all factors and responses.

The ability to `remember historical data is invaluable

because important information can be provided by the timeand sequence of key events and the operator can ask foradditional information from the ES at any time. The fuzzycontrol system was also connected to a neural extrusionsimulation model and controller which, after start-up, hadsuperior performance when taught with values close to theoperating area5 3 .

Other recently published applications on fuzzy foodprocess control include a number of fermentations and foodprocesses

1 0 , 1 1 ,5 2such as Oishi and others

5 6who applied

fuzzy control theory to the regulation of the moromi (sakemash) temperature in the sake brewing process. Alfafara etal.

5 7developed a fuzzy controller for ethanol concentration

in yeast fermentation and employed it to maximize theproduction of glutathione. Examples of other potential foodprocess appli cations of FLC are the drying of sugar beetpulp5 8 , grain drying5 9 and aseptic processing6 0 .

Motorola has recently introduced a new 16-bit micro-controller architecture that supports FL programs

4 8. A

number of companies have currently plug-in fuzzy logicmodules in beta-site testing phase which run on SCADAloops of the plant, rather than on the local control loops.Both Yokogawa Corporation of America (Newnan, Geor-gia) and Omron Electronics have successfully combinedfuzzy logic with conventional PID control with the benets

of both systems

4 6

.

EXPERT SYSTEMS

An ES is à computer program which applies expertknowledge to solve complex problems, mimicking thereasoning skill of a human expert6 1 . It has also beenappropriately called àn i ntelligent automation environmentcomprised of conventional and heuristic methods to solve aparticular problem6 2 . An ES can either be advisory, with anability to `discuss with the user, or a stand-alone system,which can handle complex control tasks. Most expertsystems are so called if..., then... rule-based systems. ESs

can be very helpful in dangerous and unpleasant situations,relieve unskilled labourers from tiresome routines, and theycan be built for round-the-clock operation.

The expert system for advisory control of heat processing




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designed at the Campbell Soup Company is believed to bethe rst of its kind in the food industry. In this well-knownlandmark case, the idea was to collect and organize theexpert knowledge of the retiring retort operator AldoCimino into a rule-based ES called `Aldo on the disc6 3 .According to Whitney5 0 , the ES for advisory control systemfor hydrostatic sterilizers has been said to have paid offalready during the rst pilot trial. The 151 rule base

capturing the 30 years of experience was built on a PC usingthe Texas Instrument tool Personal Consultant , and it tooksix months to develop at a cost of about $65,000notincluding the time of the interviews given by Cimino. Inaddition to troubleshooting, the system also includes allstart-up and shut-down procedures for hydrostatic androtary sterilizers, and it has been successfully installed andoperated in several plants. This is a typical example inwhich the system was designed on the basis of availableexpert knowledge, in this case of a retiring expert in theeld. The ES has since been installed and successfullyoperated in several plants.

While ESs such as the one described above are used

specically to solve only problems within a limited domainof expertise, ES shells or tools are generic and can beapplied to construct ESs for a wide variety of applications.Unfortunately, few affordable shells have real-time cap-ability in speed and functions, and a user-friendly GUI.Nexpert, ART and KEE have built-in real-time capabilityand, for example, G2 and MUSE have been designedespecially for real-time process monitoring and control.Recently, a number of KBS tools have also been combinedwith a graphic user interface, object-oriented programming(OOP), fuzzy-, model- and case-based-reasoning (CBR),learning ability provided by neural networks such as theNeurOn-Line algorithm in combination with G2, multi-media, and hypertext, etc. G2 has been said to be the mostpowerful of the commercially available process controlknowledge-based system (KBS) tools

6 4, and Gensym alone

has installed more than 1000 systems worldwide for processcontrol

6 5. For example, the ISI Agroindustrial Sugar

Company in Padua, Italy, has been investigating the useof G2-based expert systems in real-time control since19896 6 .

While rule-based systems have an excellent capability toexplain their reasoning, and some even have the ability tocheck for the consistency of their rules and to modify ruleson the basis of observed facts, neural networks are good at

learning. So called expert networks are hybrid systemswhich combine ESs and neural networks with theadvantages of both6 7 .

Today more than 60% of ES appli cations have beendeveloped for PC. They can run conventional PC software,be networked into a LAN, and in many cases replace PLCs.One of the rst prototype ESs built for fermentationprocesses was the real-time fuzzy ES for glucoamylaseproduction control6 8 , built using the same object-orientedSmalltalk/V-based (Digitalk, Inc, Los Angeles, CA) fuzzyES tool originally developed for extrusion cooking controland implemented in a PC6 9 . The hierarchic, rule- and frame-based system was built to operate on-line, in real-time, with

a capability of fault prediction, diagnosis and correction.The system has been subsequently further developed,and as the rst example cases an on-line diagnosis andcontrol system LAexpert for lactic acid production

7 0and

supervisory control system for bakers yeast production7 1

were built in Smalltalk/V-Macintosh environment. Aknowledge-network was constructed to represent facts andtheir relations. Also Kosola and Linko

7 2developed

intelligent, real-time neural and neuro-fuzzy state estima-tion, prediction, and control tools for bakers yeastfermentation. Visual C++ for Windows was employed inthe programming of direct neural control systems and

MATLAB

T M

was used for building a neuro-fuzzy hybridsystem, both implemented in a PC.Other types are also slowly coming to the food industry.

For example, Joshua Tetley has recently been reported tohave reduced refrigeration costs by an impressive 30% bythe introduction of fault diagnosis ESs at its Leedsbrewery7 3 . An EUREKA scheme based ALSACA (automa-tion for large-scale assembly) project contains an automaticadvisory error recovery system, which uses CBR to detectand diagnose errors within a plant7 4 . The system collects datafrom PLCs and compares that information with a database ofknown faults and their remedies.

NEURAL CONTROL

Neural network models do not require any a prioriknowledge on relationships of the process variables inquestion, and offer a simple and straightforward approach toidentication problems. The expert knowledge and facts inneural networks is obtained by iterative training on the basisof prior examples. A neural network can also be retrainedon- or off-line whenever new information becomesavailable.

An example of industrial-scale applications is the recentneuro-fuzzy hybrid system to control of the tank level atIdemitsu Chiba renery in Japan. Neural networks handlehighly non-linear problems and uncertainties typical of foodprocesses, and are characterized by an ability to learn frompast input/output vector pairs through iterative training.Consequently, neural network programming has becomeone of the biggest research areas of articial intelligence,although food process applications have only recently beenstudied and little published information is available5 7 .

It is often not realized that the basic concept of neuralnetworks was already presented in the 1940s in a search forways to simulate the function of the brain, much before JohnMcCarthy introduced the articial intelligence paradigm in1956. Thus, relatively old concepts are dealt with here. Why

is it then that practical applications of neural networks hadto wait for so long? Werbos presented the backpropagationlearning principle in his PhD thesis in 1974, but it was notuntil Rumelhart and coworkers made backpropagationwidely popular through a landmark paper that appeared inNature in 1986. This resulted in an explosion of research inneural network appli cations, but bioprocess applicationresearch only began to emerge in the 1990s.

Several Japanese companies have applied neural networktechnology to control kerosene fan heaters commonly usedin Japan to heat individual rooms. A more complex exampleis the automation of certain operator control tasks inlithographic offset colour printing by Rockwell Graphic

Systems. According to Hoskins and Himmelblau7 5 , feedfor-ward, backpropagation neural networks are well suited tosolving problems typically encountered in the chemicalprocess industry, and this is likely to also apply to

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bioindustries. Miller and others7 6

have discussed theprinciples of various neural control strategies in detail.Linko and others7 7 were the rst to use a neural networkwith output feedback and time delays for the control ofspecic mechanical energy (SME) on the basis of screwspeed in at bread production by a twin-screw extrusioncooker.

However, as a food extruder is basically a MIMO (multi-

input multi-output) system, a MIMO neural networksimulator and controller was subsequently integrated withthe fuzzy extruder expert system to form a hybrid controller,which was found to be superior after reaching a steady stateoperation7 8 . Dynamic changes in torque, SME and pressurewere modelled and controlled using two independently-taught feed-forward articial neural networks. Choutrouet al.

7 9emphasized the importance of a careful selection of

the design parameters to achieve a good performance of anadaptive neural controller. They employed a control schemebased on a plant inverse neural model to control a simple,experimental stirred tank bioreactor. Bakers yeast produc-tion represents another type of application of neurocon-

trol7 1 , 7 2

. Recently, new hybrid software tools, which useexisting PLC systems to predict and optimize productquality rather than focusing on the production process itself,have become commercially available

8 0.

Neural networks exhibit a great potential as `softwaresensors for on-line, real-time state estimation and predictionin complex process control applications1 0 ,8 1 ,8 2 . Zhuet al.8 3

used backpropagation neural networks in state variableprediction during the start-up phase of a chemostat ethanolproduction process. A successful simultaneous estimation ofconsumed sugar and total produced lysine using data fromindustrial-scale fed-batch fermentations has been recentlydemonstrated

8 4. Other typical food processing related

neural network applications have involved beer brewing8 5 ,yogurt production

8 6, enzyme production

8 7and drying of

cooked rice8 8

. Quite recently, Linko et al.8 9

reported on theapplication of a neural network in the estimation offermentation time in the production of b-galactocidase by

Streptococcus salivarius subsp thermophilus 11F whilePatnaik9 0 applied a dynamic recurrent neural network tocontrol the fed-batch b-galactosidase fermentation byrecombinant Eschericia coli which is sensitive to the

inux, noise and process faults. Still another recent exampleis the application of a recurrent neural network by Teissieret al.9 1 to monitor and control yeast production in a winebase medium using an open-loop control strategy.

WHAT CAN WE EXPECT OF THE FUTURE?

Improved food process control is needed to ensure a safe,

nutritious, and affordable food supply for future genera-tions. This was clearly recognized at the 1993 EFFoST(European Federation of Food Science and Technology)Conference on food control in Porto, Portugal. In theforeword, the great potential of advanced, computer-aidedfood process control systems has been presented but, as wehave seen, the complex nature of food processing makesreal-time process control a difcult and challenging task.Selected examples of recent developments in food processcontrol are given in Table 2, some of which are discussed ina greater detail in the foreword.

Although conventional feedback control is accessible andeconomic today, and statistical process control can be

employed as an excellent tool in total quality management(TQM), novel on-line sensors1 , 9 2 , 9 3 for key processvariables and quality attributes are needed for efcientcontrol. Robotics is likely to play an increasing role in foodprocess automation and control in the near future, with amajor German meat processor Waltner FleischwarenfabrikGmbH becoming a good example of the advantages gainedfrom the use of custom-programmed robots in pork chopprocessing8 .

Advanced control strategies are based on a process modelto improve the performance of the control system, andcontinued research for more reliable models is still needed.Advanced controllers can cope with nonlinearities, uncer-tainties, variable time delays, changes in process variables,and unmeasureable outputs. Statistical process control(SPC) and statistical process monitoring (SPM) employstatistical means as a part of the TQM in order to assure thatthe process is under control and the products are producedwithin dened specications. Although statistical processcontrol is not a new concept to improve productivity andproduct quality, increasing attention to SPC has been givenin overall process improvement and control, and its



Table 2. Examples of advanced food process control applications.

A packaging line controlled by PLCs with a two-way digital bus. 19An integrated production system (IPS) for control at a new Guinness brewery. 23A windows-based system with touch screens at the Stroh Brewery Company

in St. Paul Minnesota. 24A new SCADA concept at the Guinness Park Royal plant, in which

the most critical area was to get the SCADA and the PLCs to cooperate 31A new SCADA system at Carlsberg-Tetleys Alloa brewery 32A smart, PC-based SCADA system at Birds Eye Wall, Europes largest

ice cream factory in Gloucester, UK 33A new SCADA system based on the Wonderwares InTouch software 35A new PC-based control system at Sanders animal feed plant 36Model-based control at a cereal drying plant 44Fuzzy, expert and/or neurocontrol of a food extruder 54, 55, 78, 79Fuzzy control of grain drying 59Supervisory expert control of thermal processing 63

Fuzzy expert control of lactic acid and bakers yeast fermentation 70 72Fault diagnosis expert system, at Joshua Tetleys leeds brewery 73Neural estimation of consumed sugar and total produced lysine 82Neural control of fermentation processes 79, 8184, 8687


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principles are described elsewhere9 4 9 7

. A number of integrated CAM packages provide for SPC1 2 and, as anapplication example, Tanet al.9 8 have recently applied SPCcoupled with machine vision for automated real-timecontrol of an extruded food product.

Statistical control was monitored by conventional She-whart charts

9 9, and a PI algorithm was employed to

minimize the product size variation caused by moisture

variation. Negiz et al.

1 0 0

applied SPM to HTST dairypasteurization for monitoring of product lethality andprocess sensor reliability. Unlike the chemical industry,process optimization methods have still been little used inthe development of model-based predictive controllers forfood processing to maximize quality and minimize costs.Following the introduction of PLCs with built-in PID loopfunctions to replace electromechanical relays in the mid-1970s, the rapid developments in personal computers andrelated software can be said to have resulted in a secondrevolution in process control. The PC has nally arrived tothe plant oor1 , although doubts and criticism have alsobeen expressed1 0 1 , 1 0 2 . Modern computer control systems

can easily incorporate both on- and off-line data into theirknowledge pool. The capabilities of Pentium processors andthe recent introduction of the Windows NT operatingsystem has allowed the DCSs to be integrated with mostcommercially-available information networks such as theeldbus digital communication protocol, and with standardWindows-based word processors and spreadsheets. WhenPCs become more robust and crash proof, and links to theInternet become increasingly important, the choice for PCsin process control is likely to increase

1 0 3. The latest

software developments also enable marked improvementsin the SCADA concept1 0 4 , 1 0 5 and easy-to-understandgraphical user interfaces make the modern control systemssimple to operate.

As the supplier support services become increasinglyavailable worldwide through the Internet, the systemmaintenance will no longer be a major problem. A digitaleldbus enables the integration of modern smart sensorswith inexpensive advanced control systems. A smart sensorincludes microprocessor software to overcome problemscaused by non-linearities. In many cases a smart sensor cancalibrate itself, and act as a fault detector

1. The eldbus

technology promises to improve quality, reduce costs andboost efciency. Following the European eldbus standardEuroNorm EN 50170, an international standard IEC may be

nally ratied by the end of 1998, but much of the benetsof a standard have been claimed lost during the 13 years ofnegotiations1 0 6 . The advantages of the use of eldbustechnology in the food industry have been recentlydiscussed, using the baking industry as an example, andemphasizing the dramatic reduction of complexity and costsof control system design, installation, maintenance andextension

1 0 7.

The principle of independent àgents and àppletscapable of communicating with each other could bevisualized as a future solution to the communicationproblems1 0 8 . With the platform-independent object-orien-tated Java developed by Sun Microsystems (Menlo Park,

California), monitoring, reports, trends, alarm lists andstatistical process control could become available for anyauthorized individual anywhere in the world. The JavaAutomation Application Programming Interface (API) has

been announced recently, and is expected to enable real-time monitoring and control through the Internet world-wide1 0 9 , 1 1 0 . With the 32-bit Java one writes àpplets to runon a Web browser, instead of writing applications in alanguage for a given platform. Integration of SCADA withthe Internet already benets Cargills oil blending plant inMerseyside

1 0 3.

In July 1997, a new organization OACG (Open

Architecture Control Group) was established, focusing oncreating and implementing a standard control engineinterface using Java, and a standard I/O medium to connectthe control engines with UDP/IP protocol running onEthernet1 1 0 . Another recent development is a novel object-oriented WEB monitoring and control system by TrihedralEngineering Ltd (Bedford, Nova Scotia, Canada) which wasimplemented at the Cavendish Farms new potato productsplant in Summerside (Prince Edwards Island, Canada), witha daily throughput of one million pounds of products1 1 1 .

The 1993 expert report on research needs into the 21stcentury of the IFT research committee identied articialintelligence and vision systems for sensing food safety and

quality as important research subjects1 1 2

. Although muchprogress has been made since then the report is for the mostpart valid. In other elds, fuzzy control and neuro-fuzzyhybrid systems are already increasingly appearing ineveryday li fe in camcorders, car transmissions, vacuumcleaners, washing machines, etc. Neural networks can alsobe employed as software sensors and predictive control forrapid inference of difcult-to-measure process output(s)from easily-measurable variables1 1 3 . Neural networks areuseful in process identication when the structural relation-ship between the inputs and outputs is not known, butsufcient data is available for the learning procedure. Willisand others

1 1 4have shown the superiority of neuro-control

over conventional PI control. According to Collins1 1 5 ,perhaps the biggest criticism against neural networks is theirinability to determine how they have arrived at a particularconclusion. Lack of information on successful production-scale applications is a further problem

1 1 6.

In monitoring and control, the real-time capability ofmodern expert systems is clearly invaluable1 1 7 . The benetsof knowledge-based systems i n industrial applications havealready been clearly demonstrated, although for proprietaryreasons little published information is available. Accordingto Chiu

1 1 8, the main driving force for applying advanced,

control systems is savings in time and money rather than an

improved controller performance. He sites as a typicalexample the integration of autotuning into todays PIDcontrollers, often with payback after a single use. There isno doubt, however, that in a short time articial intelligencetechniques integrated into food process control systems willbe seen, primarily to complement, rather than replace,conventional control techniques as well as to improvehuman friendliness. The operator is vital to the success ofany novel control application. Affordable standard tools ofadvanced process control are now available even for thesmall and medium-sized enterprises. There are no longertechnical reasons why food processing could not be as wellcontrolled as any other process, but a change in attitudes and

education may be needed. Furthermore, there are only a fewpublished landmark cases and references which do notparticularly encourage the conservative food industry toexperiment with the novel control techniques. It took an

134 LINKO and LINKO



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ACKNOWLEDGEMENTS

The authors are grateful to th e Academy of Finland for nancial support.

ADDRESS

Correspondence concerning this paper should be addressed to Dr S.Linko, Helsinki University of Technology, Laboratory of Bioprocess

Engineering, PO Box 6100, FIN-02015 HUT, Finland. Fax: +3589 462373. Email: susan.linko@hut..

The manuscript was received 6 November 1997 and accepted forpublication after revision 16 July 1998

This paper is an extended and updated version of an invited plenarylecture presented by Dr S. Linko at the 7th International Congress o nEngineering and Food (ICEF 7), April 1317, 1997, Brighton, UK.


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