0001 INSTRUMENTATION AND PROCESS CONTROL

InstrumentationR Oliveira, New University Lisbon, PortugalP Georgieva and S Feyo de Azevedo, University ofPorto, Portugal

Copyright # 2002, Elsevier Science Ltd. All Rights Reserved

doi:10.1006/rwds.2002.0401

Oliveira, RNew University LisbonDepartment of Chemical EngineeringLisbonPortugal

Georgieva, PFaculty of EngineeringUniversity of PortoDepartment of Chemical EngineeringRua dos Bragos4050-123 PortoPortugal

Feyo de Azevedo, SFaculty of EngineeringUniversity of PortoDepartment of Chemical EngineeringRua dos Bragos4050-123 PortoPortugal

Introduction

0002 Industrial evolution in the second half of the twen-tieth century was mostly in¯uenced by four types ofinterrelated factors ± progress in digital technology,advances in science, evolution of societal require-ments and demands and, particularly over the last 30years, evolution of business concepts.

0003 Developments in digital technology and in systemstheory led to major progress in sensor and infor-mation technology and a revolution in the avail-ability of distributed control systems and opensoftware applications. New concepts, particularlyknowledge-based measurement and advancedcontrol methodologies, are slowly but steadily beingbrought into the practice of process operation,performing online and in real time.

0004 Societal and economic factors have driven evol-ution in the same direction. The increasing concernfor health, safety and sustainability issues, marketquality requirements, economic pressure and the

evolution of company strategy from local to globalbusiness concepts ± the so-called knowledge econ-omy concepts ± all together re¯ected on plantinvestment decisions, favouring process automationfor cleaner and safer operation, higher product qual-ity and improved process ef®ciency and productivity.

0005Discussing plant automation from a technicalpoint of view means a discussion on instrumentation,control system con®gurations, data communicationsand theoretical control structures.

0006This article deals with instrumentation, addressingin particular both basic and advanced conceptsconcerning sensors and the issue of how to integratelocal hardware for automatic control, usually dis-persed throughout the plant.

Basics of Plant Automation

0007There are well-established methodological stepsfor the design and implementation of a control struc-ture: (1) designing and implementing an appropriate,¯exible, control con®guration; (2) performing ®rst-level data acquisition and process monitoring,including record keeping and ®rst-level alarms;(3) performing data interpretation (implementingsecond-level process monitoring); and (4) designingand implementing optimization and control solutions.

0008In plant-wide distributed control con®gurations, asillustrated in Figure 1, the backbone of transmissionis all digital. Communication protocols (some open,some proprietary) ensure data transmission for cen-tralized data interpretation, for monitoring and forplant scale optimization. Control at sector and unitlevel is usually performed locally. Local-level signaltransmission has for many years been analog only;initially, pneumatic signals and later electric (currentor voltage) signals. However, more and more,information also ¯ows digitally between (smart)sensors and controllers.

0009At a local level, process control system instru-mentation includes: (1) sensors for measurement ofprocess variables; (2) controllers, for implementinga proper (digital) control structure (e.g. PID, pre-dictive or adaptive controller, etc.); (3) ®nal controlelements for manipulation of process inputs; and(4) support devices such as general signal condi-tioners of both input and output signals, electric (V/Iand I/V) transducers, electric-to-pneumatic trans-ducers and hold elements.

INSTRUMENTATION AND PROCESS CONTROL/Instrumentation 1

0010 The range of instrumentation and control systemsis very large today. Nowadays industrial users areavailable through the internet information sites ofinstrumentation manufacturers with very detailedinformation on all types of instrumentation andcontrol systems con®gurations, including data com-munications, related to plant automation.

0011 A relevant management decision in the automationof older plants ± and most fall into this category ± ison how to step from existing local control solutionsto integrated distributed control. Investment ona complete new solution is high and the tendency isto try to adopt a solution that makes use of availableequipment. This is often hard to achieve and leads toa ®nal con®guration that mixes too many differentcontrol equipment suppliers, with the related costs ofoperation and maintenance.

Measurement Instrumentation

0012 A key limitation to the application of process controlis the lack of appropriate sensors for many processvariables. This is so in dairy processes and thereforemeasuring techniques for online use in dairy industryare subject to considerable research and manufac-turing interest. Basic concepts and the main meas-urement techniques are reviewed in this section.

Basic Characteristics of Sensors

0013 A sensor is composed of a sensing element anda transmitter, as indicated schematically in Figure 2.The sensor output, whether analog or digital, shouldbe a standard signal suitable to be supplied toa controller.

0014 Most transmitters respond rapidly. When thesensor element response is also fast, then measure-ment dynamics can be neglected in view of modellingprocess dynamics. Such a case is indicated in Figure 3,which shows the time response of a pH electrode toa step change in pH from 4 to 7. The characteristic®rst-order time constant for the sensor dynamics (thetime required for the response change to reach 63%of the step change) is in the order of milliseconds(� � 570 ms). There are however cases where themeasurement dynamics, particularly time lag, maybe signi®cant and ignoring it can lead to controldif®culties.

0015 Sensors can exhibit linear or nonlinear behaviour.This is being related to and expressed by the relationbetween the variation of the property value and thatof the transmitted signal. Sources of nonlinearityusually lie on the sensing element. For a linear sensor,the gain for a given calibration is constant and equalto the ratio between the set span and the range of the

sensor output. Nowadays, with digital data acqui-sition, transducer nonlinearities are easily incorpor-ated in the data interpretation software and cause nopractical dif®culties.

Specialized Sensors/Measurement Systems

0016The most important properties of dairy processessubject to measurement that re¯ect both processoperation and product quality are classi®ed asobjective or subjective. Examples of the former arepH, temperature, ¯ow rates, pressure and level.Sensors for online measurement of such propertieshave been available for a long time. Properties suchas taste, ¯avour, colour and consistency are con-sidered to be subjective and dif®cult to measure.

0017Commercial sensors applied in the dairy industryare subject to several quality constraints, such assanitary, safety and environmental requirements.A trend in new sensor design technologies is theincreasing integration of the sensing elements intosilicon chip microcircuits. These new measurementdevices directly incorporate all circuitry needed toself-compensate for environmental changes and yieldan output that is suitably ampli®ed for transmissionto standard electronic controllers. These sensors offerthe advantage of small size, reduced price and prac-tically no mechanical parts to wear out.

0018Next, measurement techniques and instrumen-tation for the most important objective properties inthe dairy industry are considered.

pH 0019pH measurements are of paramount import-ance for quality control in milk fermentation andrelated processes. For example, inadequate pH canbe a cause of discoloration in cheese or fermentedmilks, excess free whey and excess or inadequatetartness in fermented products; pH changes arerelated to the viscoelastic properties of yoghurt andthey are also correlated to the physiological state ofbacteria in lactic acid fermentation. Also, the ®nalpH value is normally a feature of the ®nal product:fermentation converts lactose to lactic acid, causinga drop in pH to a value in the range of 4.25±4.5;rapid cooling at the correct level of lactic acid thenstops bacterial action.

0020pH electrodes can be in direct contact with food, ifthey meet sanitary requirements. In general terms,pH measurements, particularly in conjunction withelectrical conductivity measurements, constitute animportant means for continuous, real-time processmonitoring.

2 INSTRUMENTATION AND PROCESS CONTROL/Instrumentation

Temperature0021 In industrial applications the mainmeasuring devices for low temperatures (below250 �C) are either based on thermoelectric effects(thermocouples) or on resistance changes (e.g. plati-num resistance temperature devices (RTD) thermis-tors. Measurement characteristics, particularlysensitivity and the degree of (non) linearity, favourthe use of platinum RTD; the most widely used is theso-called PT100 device.

0022 Thermal processing is a key stage in most dairyprocess operations. As an example, during pasteur-ization of yoghurt the temperature is raised to 85±90 �C to destroy undesirable microorganisms anddenature the whey to improve viscosity and preventsyneresis. Then the product is cooled to 40±45 �C sothat the sterile mix is ready for inoculation. Also,freezing and refrigeration are used to prevent growthof unwanted microorganisms. In general, these op-erations and process units require a reliable andaccurate temperature-monitoring and controlsystem.

Level, density or interface level0023 Liquid height, den-sity or interface level between two liquids can bemeasured either by differential pressure (d/p cells)sensors or by measurement of buoyancy force ona displacer suspended in a liquid. An electronictransmitter converts the output of the sensingelement to an appropriate analog or digital outputsignal.

0024 Nowadays instrumentation companies offer thedairy industry a variety of special transmitters forlevel monitoring and control applications in inven-tory tanks and clean-in-place (CIP) vessels.

Pressure0025 In most process operations, particularlywhen thermal processing is required, pressure regu-lation is one of the essential control loops. Pressuresensors are thus among the most commonly usedonline sensors. D/p cell transmitters for either gaugeor absolute pressure are the solution which is gen-erally adopted.

Thermal conductivity0026 Thermal conductivity ex-presses the ability of a substance to conduct heat.The most common thermal conductivity probesconsist of an assembly of an electric heated wire anda temperature measurement system (based on ther-mocouples or thermistors) from where heat ¯uxes aremeasured and heat conduction is inferred.

0027 Thermal conductivity measurements ®nd wideapplication in dairy chemistry and biochemistry andin food process engineering. The measurement ofthermal conductivity by Line heat source probes may

be used for inline determination of the coagulationtime of milk for cheese curd and yoghurt productionand may be helpful for to automating these pro-cesses, aiming at maximizing the yield of cheese andyoghurt. Coagulation time may be detected by thesharp increase in the temperature difference betweenprobe temperature and initial milk temperature.

Electrical conductivity 0028Electrical conductivity (G)expresses the ability of a substance or media toconduct electricity. It is employed for quality controland further ®nds online use in identifying featurepoints of fermentation states. For example, electricalconductivity measurements allow the urease activityand the acidi®cation activity in lactic acid fermen-tations to be distinguished.

0029In a dairy plant where many fermentations areperformed, either simultaneously or sequentially, thereal-time prediction of fermentation completion timeis very important for scheduling raw material supplyand energy utilization. Data-driven models (e.g.neural network-based) relating characteristic prop-erties of the fermentation state (model outputs) topH and electrical conductivity (model inputs) can be®rstly identi®ed (trained) and subsequently used tomonitor the fermentation process and to predictfermentation times.

Viscosity 0030The main items of equipment for vis-cosity measurement are process viscometers. Shearviscosity for Newtonian ¯uids can be measured bya capillary ¯ow viscometer. Cone and plate visco-meters are suitable to determine the shear viscosity oftime-independent non-Newtonian ¯uids. From thechanges in viscosity, the stages of aggregation and gelformation can be described and calculated. The vis-cosity can ®nally be used as an objective measure forthe control of coagulation processes.

0031In industrial operations involving slurries, pulps,grease or other similar media, consistency, ratherthan viscosity, is measured by rotation and oscil-lation rheometers. These devices allow, for example,the continuous online monitoring of the transition ofmilk ¯uid into a viscoelastic gel structure.

NIR spectroscopy 0032Infrared and near-infrared (NIR)spectroscopy can be used to measure the levels ofwater, fat and protein in liquid milk and relatedproducts online (and ex situ). This is a commonlyused technique for quality control, but may also beused for online closed-loop control.

Optical density 0033Optical density (OD) is commonlyused to measure biomass concentration online and

INSTRUMENTATION AND PROCESS CONTROL/Instrumentation 3

ex situ. The measurement principle is based on theindividual or combined use of measurements oftransmission, re¯exion or scattering of light. Theinterpretation of the signal is complex, but it isnormally linearly correlated to biomass concen-tration in diluted solutions.

Final Control Elements

0034 Final control elements (FCE) are devices, driven bycontroller signals, used to manipulate process controlvariables. In most cases control actions consist ofadjusting ¯ow rates of process input or outputstreams (solid, liquid or gas) or cooling and heating¯uids. The most widely used FCE are ¯ow regulatorvalves.

0035 Designing a valve involves taking decisions onvalve size, choice of body material, choice of typeof valve (signal-to-close or signal-to-open) and choiceof ¯ow vs aperture characteristics (essentially linearor equal percentage valves).

0036 Valve sizing (computing the Cv parameter) and thechoice of valve ¯ow characteristics require con-sideration of hydrodynamic aspects, particularlypressure drops along the piping. The choice ofmaterial depends on the properties (corrosive, slurry,etc.) of the process ¯uid. For the dairy industry,stainless-steel valves are most common. The decisionon working with normally open or normally closedvalves derives directly from answering the safetyquestion of how the valve should stay in emergencedue to energy failure.

0037 Regulator valves are typically driven bya pneumatic signal (range 3±15 psig or 0.02±0.1 MPa). Signals are normally transmitted to the®nal control element (FCE) as analog current signals(4±20 mA), being converted locally by a current-to-pneumatic transducer. Alternatively, a step motor,driven by a digital signal from the controller, canactuate valves.

0038 On-off valves normally use electrical or pneumaticactuators. They are mainly used for sequentialcontrol and during start-up and shutdown proce-dures. Other ®nal control elements, namely dis-placement devices and pumps, are more and moredigitally actuated by step motors.

Digital Control Equipment

0039 Digital control instrumentation represent the newstandard and indeed they have introduced a majorchange of mind set with respect to control systemstructures and solutions, with new procedures con-cerning communications and calibration routines.

They offer the computing power required toimplement advanced model-based monitoring andcontrol algorithms (software sensors and predictivecontrol) at local level and in real time. They enablethe implementation of plant-wide monitoring, opti-mization and control solutions through the availabledistributed control architecture.

Distributed Control Systems

0040A distributed control system (Figure 3) is simply anarrangement whereby control devices and computerprocessing power are distributed through a networkinstead of being centralized.

0041The main devices in a distributed control systemare essentially microprocessor-based subsystemssuch as programmable logic controllers (PLC), smartsensors, supervisory and engineering stations andother input±output (I/O) devices (e.g. FieldPoints,device integrators, etc.). All devices in the networkmust be integrated with proper hardware and soft-ware for communications.

0042PLC are today's industrial standard for localdigital control. They are reliable special-purposecomputers for control in the industrial environment,consisting of a set of I/O modules and a program-mable central processing unit. They can performanalog-to-digital (AD) and digital-to-analog (DA)conversion and have special-purpose digital I/O ports(PLCs were originally mainly designed for eventcontrol). PLC normally use proprietary program-ming languages.

0043Smart sensors are devices that through their digitalsystem can be connected to the network, communi-cating bidirectionally with the other devices. In par-ticular, they accept remote commands for remotecalibration.

0044General-purpose computers (PC, workstations)may be interconnected in the control system networkto carry out inferential measurement procedures,high-level data analysis, supervisory duties or moresophisticated dedicated control tasks.

0045FieldPoints are modular I/O devices that connecta bank of analog and/or digital I/O modules to anindustrial network, being able to perform AD/DAsignal conversion.

0046The key feature of a distributed control system isthus that the measurement and control tasks aredistributed out into the ®eld. Integrated hierarchicalcontrol con®gurations may be built where high-leveltasks, like process supervision and optimization, arefully integrated with the low-level data acquisitionand local control tasks.

4 INSTRUMENTATION AND PROCESS CONTROL/Instrumentation

Communication Standards

0047 Industrial communications refer to the networkinghardware and software, together with the respectivecommunications protocol.

0048 A number of industrial network standards, de-signed to meet different application requirements,are available today: Ethernet, DeviceNet, Foun-dation Fieldbus, PROFIBUS and controller areanetwork (CAN). Details of these industrial networkstandards can be found in Table 1.

0049 Some specify low-level sensor±controller±actuatorcommunication protocols (like CAN and Device-Net), whereas others are specially oriented for dis-tributed control systems in the process industries(Fieldbus and PROFIBUS). They differ on commu-nications bus speci®cations, on velocity of datatransfer, on communications protocol used and onthe communications model. For distributed controlsystems in large factories, Fieldbus and PROFIBUSare the two most important standards.

0050 Ethernet is the most widely used local areanetwork (LAN) technology. An Ethernet LAN mayuse coaxial cable, special grades of twisted pairwiring, or ®breoptic cable. `Bus' and `Star' wiringcon®gurations are supported. Ethernet devicescompete for access to the network using a protocolcalled carrier sense multiple access with collisiondetection (CSMA/CD). Ethernet conforms to theIEEE 802.3 speci®cations and runs commonly underthe high-level TCP-IP protocol (although manyothers are possible). The Fieldbus standard hasadopted a second alternative for the physical layerthat is based on Ethernet, thus providing a solutionfor factory-to-factory communication.

Basics of Analog-to-Digital andDigital-to-Analog Signal Conversion

0051 All digital control systems contain a data acquisitioninterface that performs AD/DA conversion. Suchtasks are commonly performed by standard compu-ters with AD and DA cards, by FieldPoint modulesor by PLC.

0052 It is worth examining basic aspects of the dataacquisition and control problem, namely the ¯ow ofinformation from the `process property' to the`binary word in the computer' and the feedback fromthe `binary decision variable' to ®nal control element.

Data acquisition0053 The data acquisition chain is re-presented schematically in Figure 2. The design stageof the data acquisition system should start witha qualitative analysis, addressing the following mainconsiderations:

� The sensor should be chosen with the objective ofmaximizing sensitivity for the desired measure-ment interval. This means that calibration shouldbe such that the sensor measurement span shouldmatch the measurement interval and be mappedinto the full range of the output signal.� Industrial analog-to-digital conversions (ADCs)

are nowadays standard. A 12-bit AD converter isgenerally suf®cient. The input range is nota problem, assuming that the required signalconditioners are available.� Signal conditioning should be such that the sensor

output signal V0 is transduced into a signal V2 thatexhibits the same range as that of the ADC. Thissituation maximizes the overall resolution of theacquisition chain.

Still at the design stage, the quantitative analysismust be performed in steps from the process to thecomputer:

� The sensor normally delivers an analog electricalsignal (V0), which should be a known function ofthe process property P. Assuming, for simplicity,a linear relationship, Eqn [1] holds:

V0 � k0P� z0 �1�� This signal will generally undergo some form of

conditioning (transduction, ampli®cation, attenua-tion, etc.), after the sensor and before the ADC,depending mainly on aspects related to compat-ibility and range of transmission signals (V1 andV2). These types of transformations can usuallybe adequately expressed by linear relationships ofthe form:

V1 � k1V0 � z1 �2�

V2 � k2V1 � z2 �3�

� Considering an N-bit ADC, with an input range[Vmin, Vmax], the digital word D, corresponding toV2, is given by:

D � INTV2 ÿ Vmin

Vmax ÿ Vmin2N

� ��4�

The corresponding discretization error is given by�0:5�Vmax ÿ Vmin�=2N. For a 12-bit converter thiserror is well inside industrial requirements, gen-erally lower than all other errors in the chain.� For the choice of the sampling time (t) in the

implementation, a simple rule of thumb is oftenused in industrial applications, adopting

INSTRUMENTATION AND PROCESS CONTROL/Instrumentation 5

a sampling time value of about one-tenth of theprocess characteristic time constant.

The implementation stage of local signal acquisitioncorresponds to effectively programming the multi-plexing, ADC, data reading (binary value D) anddata decoding (getting the property P from the binaryvalue D).

� For each scanning (multiplexing and data reading)of input channels, performed at every time intervalt, programming of data decoding is performed bysuccessively computing the values from the binaryword D to the property P, by solving Eqns [4], [3],[2] and [1] with respect to V2, V1, V0 and P,respectively.

V2 � Vmin � �D� 0:5�Vmax ÿ Vmin

2N�5�

V1 � V2 ÿ z2� �=k2 �6�V0 � V1 ÿ z1� �=k1 �7�P � V0 ÿ z0� �=k0 �8�

� At this stage, process values are available in thedata acquisition application for all types of desiredactions, namely data interpretation, data plotting,book-keeping, analysis of alarms and relatedactions, computing of control actions and outputof control commands.

Control action0054 It is outside the scope of this articleto analyse control algorithms. Assuming that acontrol decision has been taken, given by a binaryword C, such a command is transmitted from thecontrol system to the ®nal control element throughan elementary chain including the digital-to-analogconversion (DAC) (with a hold element), and, inthe more general case where the ®nal control elementis a valve, a signal conditioner, a power bufferampli®er and a current-to-pneumatic signal trans-ducer (Figure 2). For design purposes, assuming thatthe DAC is set for a voltage output, Eqn [9] re-presents the DA conversion, where N is the numberof conversion bits of the DAC , Vref is the referencevoltage (corresponding to an output interval of0ÿVmax, with Vmax�Vref):

V3 � Vref � C=2N �9�

The industrial standard for DA converters is nor-mally of 12 bit. Most common ranges of outputsignals are 0±10 V or 4±20 mA.

0055Finally, referring back to signal conditioning, caremust be taken to ensure that wiring is correct, thatinstruments and source grounds are of good equiv-alent level (a comment that also applies to wiring inAD lines) and that appropriate buffer ampli®ers areused to protect control devices from high currents.

Advanced Topics

0056Two topics closely related to the state of the art inboth information technology and systems theory arenow discussed: how to make measurable what is notso; and how to integrate information and managelarge-scale systems.

Software Sensors

0057In many cases, key process variables and character-istic parameters, namely kinetic and transport para-meters, are not available directly online and in realtime, either because they are really not measurableor simply because measurements may be expensiveand/or unreliable.

0058Software sensors are algorithms for the onlinecomputation of those state variables and parametersthat are not measurable in real time, from moreeasily accessible (accurate and inexpensive) relatedmeasurements. The concept is closely related to thoseof inferential measuring and of state observers,widely discussed in the specialized systems engin-eering literature. The design and implementation ofsoftware sensors provide in some cases a suitableanswer to cope with the lack of instrumental sensors.It may require the computational power ofa dedicated computer in the distributed controlnetwork, a requirement that nowadays represents notechnical or economical problem.

0059A software sensor relies on a process (subsystem)model that establishes the relationship betweenmeasured and estimated properties. Hence, the keyfor success (or failure) of a software sensor is theavailability of knowledge/information about therelationship between measured and unmeasuredproperties, i.e. the accuracy and robustness of theunderlying model. With respect to and in the contextof dairy industries, the kinetics in fermentation tanksare the most dif®cult part of the process to model.Traditionally, designing adaptive observation/esti-mation algorithms, of which the most frequentlyreported technique is the extended Kalman ®lter,circumvents the problem. This type of method re-quires a number of simplifying assumptions that arenot always acceptable. More recently, knowledge-based software sensors have been reported. They relyon arti®cial intelligence (AI) models like arti®cial

6 INSTRUMENTATION AND PROCESS CONTROL/Instrumentation

neural networks (ANN) and fuzzy or hybrid neuro-fuzzy models, combined with mechanistic expres-sions of process behaviour. Though models reportedin the literature are excellent in their ability to predictbased on minimum information, the requirements ofboth know-how and computational power forimplementation are still substantial. This hinders itsindustrial application on a wide scale, in the shortterm.

Measurement of Subjective Properties

0060 Properties such as taste, ¯avour, colour and consist-ency are subjective properties that are extremelyimportant for process operation in food industries.At present operators use their human senses, i.e.smell/aroma, feel, taste, as a gauge for the accept-ability of products. Measurement techniques forsubjective properties are reported to be under inten-sive investigations. The electronic nose concept ismaybe the best known example. The electronic nosemimics the human nose to detect speci®c aromasand smell. This technology is commercially avail-able today. Image analysis is now a well-developedtechnique. Properties such as material visual aspectcan be inferred from these techniques, providingnew information for further and new automaticprocessing.

Factory-to-Factory Automation

0061 As a ®nal point, with the internet, it has become clearthat the information ¯ow scheme of Figure 1 in anautomated factory may move down the hierarchy. Asthis technology becomes reliable and secure, we mayspeak of a factory-to-factory (worldwide) automatedinformation ¯ow and distributed processing. Whilstthis does not represent a state-of-routine automationsolution, it represents a real management tool in thecontext of prevailing business concepts.

Conclusions

0062 Basic measurement concepts and control instrumen-tation applied in the dairy industry have been re-viewed in this article. The intention was to providean insight to the main principles and characteristicsof classical and more advanced sensor devices andcontrol equipment.

0063 The measurement mechanisms considered rangefrom the commercially available sensors for import-ant objective properties of dairy processes to moresophisticated model-based software sensors and newtechnological solutions for monitoring subjectiveproperties in a variety of food industries.

0064The discussion of digital control system instru-mentation is also focused on core items such as ®nalcontrol elements, distributed control systems withrespective communications standards and signalconversion required for data acquisition.

0065Though the topics considered are far from beinga complete overview of all research and industrialdevelopments in this area, the article providesstructured information on the main instrumentationaspects routinely implemented or potentially appli-cable in the dairy industry.

See also: Hazard Analysis and Critical ControlPoints (HACCP): Processing Plants (0196).Instrumentation and Process Control: ProcessControl (0713). Process and Plant Design (0714).Flow Equipment: Valves (0717).

Further Reading

Bentley JP (1995) Principles of Measurement Systems, 3rdedn. New York: Longman House.

Campbell J (1984) The RS-232 Solution. Alameda, CA:Sybex.

Carr-Brion K (1986) Moisture Sensors in Process Control.New York: Elsevier Applied Science.

Cascetta F and Vigo P (1988) Flowmeters: A Compre-hensive Survey and Guide to Selection. North Carolina:Instrument Society of America.

Cubberly WH (1988) Comprehensive Dictionary ofInstrumentation and Control ± Reference Guides forInstrumentation and Control. North Carolina: Instru-ment Society of America.

Doz Y, Santos J and Williamson P (2001) From Global toMetanational ± How Companies Win in the KnowledgeEconomy. Harvard Business School.

Feyo de Azevedo S, ChoraÄo J, GoncËalves MJ and Bento L(1993, 1994) On-line monitoring of white sugar crystal-lization through software sensors, Part I. InternationalSugar Journal 95: 483±488. Part II. International SugarJournal 96: 18±26.

Feyo de Azevedo S, Oliveira R and Sonnleitner B (2001)New methodologies for multiphase bioreactors 3: dataacquisition, modelling and control. In: Cabral JM, MotaM and Tramper J (eds). Multiphase Bioreactor Design,pp. 53±83, New York: Taylor & Francis.

Fraser RE (2001) Process Measurement and Control:Introduction to Sensors, Communication, Adjustmentand Control. New Jersey: Prentice-Hall.

Johnson CD (1997) Process Control InstrumentationTechnology. London: Prentice Hall.

Krohn DA (1988) Fiber Optic Sensors ± Fundamentals andApplications. North Carolina: Instrument Society ofAmerica.

Liptak BG and Venczel K (eds) (1982) InstrumentEngineers, Handbook: Process Measurement. Randor,USA: Chilton Book Company.

INSTRUMENTATION AND PROCESS CONTROL/Instrumentation 7

Morris AS (1996) The Essence of Measurement. London:Prentice Hall.

Oliveira R, Ferreira EC and Feyo de Azevedo S (2002)Stability, dynamics of convergence and tuning ofobserver-based kinetics estimators. Journal of ProcessControl 12: 311±323.

Twork J and Yacynych AM (eds) (1990) Sensors inBioprocess Control. New York: Marcel Dekker.

I f x > 34Y = a* b /(g+1 0 0 0)-d /f

E lseif x < 100 Y = 0 R eturn

E nd ifD o I= 1 to N

G (i) =h(I ,8) E nd d o

I f x > 34Y = a * b /(g+1 0 0 0)-d /f

E lseif x < 100 Y = 0 R eturn

E nd ifD o I= 1 to N

G (i) =h(I ,8) E nd d o

I f x > 34Y = a* b /(g+1 0 0 0)-d /f

E lseif x < 100 Y = 0 R eturn

E nd ifD o I= 1 to N

G (i) =h(I ,8) E n d d o

I f x > 34Y = a* b /(g+1 0 0 0)-d /f

E lseif x < 100 Y = 0 R eturn

E nd ifD o I= 1 to N

G (i) =h(I ,8) E n d d o

If x>34Y = a* b/(g+1000 )-

d /f E lseif x<100

Y = 0 R eturn

E nd if

I f x > 34Y = a* b /(g+1 0 00 )-d /f

E lseif x < 100 Y = 0 R eturn

E n d ifD o I= 1 to N

G (i) =h( I ,8) En d d o

If x>34Y = a*b /(g+1000 )-

d /f E lseif x<100

Y = 0 R eturn

E nd if

I f x > 34Y = a* b /(g+10 0 0)-d /f

E lseif x < 100 Y = 0 R eturn

E nd ifD o I= 1 to N

G (i) =h(I ,8) E n d d o

I f x > 34Y = a * b /(g+10 00 )-d /f

E lseif x < 100 Y = 0 R eturn

E nd ifD o I= 1 to N

G (i) =h(I ,8) E nd d o

Local unit 1 Local unit N

Network

Server

PL

PID

FP

PLPID

FP

F0001 Figure 1 State-of-the-art instrumentation for process control: devices, signal transmission and information ¯ow. Continuous line,

network line (e.g. ®eldbus protocol/devices); dashed line, analog line (0±10 V, 4±20 mA) or digital serial line (RS232, RS485).

8 INSTRUMENTATION AND PROCESS CONTROL/Instrumentation

Sensingelement

TransmitterSignal

conditioningSignal

conditioningMultiplexing

and ADC

Sensor

Wiring

V0

V5 V4 V3

V1 V2

D

Physicalphenomena (P):temperature,pressure, flow,density, etc.

Digital controlsystem

DAC andhold

SignalconditioningI/P

Final controlelement

Reference

F0002 Figure 2 Schematic representation of stages in a digital control system. ADC, analog-to-digital conversion; DAC, digital-to-analog

conversion.

0 500Time (ms)

e(V

)

ef = 2.45 V

∆e = 0.63∆ef

N = 0.12 V

e0 = 1.48 V

τ = 570 ms

1000 1500 2000 2500 3000 3500 40001.0

1.3

1.5

1.8

2.0

2.3

2.5

F0003 Figure 3 Time response of a pH electrode to a step change in pH (4±7).

INSTRUMENTATION AND PROCESS CONTROL/Instrumentation 9

T0001 Table 1 Industrial communications standards

Standard Description General features Application areas

DeviceNet Low-level network designedto connect industrial devices

(sensors, actuators) to higher-

level devices (controllers)

Powered bus consisting of twoseparate twisted-pair cables

Built on CAN protocol

Producer±consumer model fordata transfer

Mainly manufacturingindustries

Foundation

Fieldbus

Digital network standard designed

specially for distributed process

control; expected to substitute forthe 4±20 mA analog standard

H1-powered 31.25 kb sÿ1 bus

(standards ISA S50.02-1992;

IEC 61158-2) or high-speed10/100 Mb sÿ1 Ethernet (HSE)

Mainly process industries

Fieldbus (communication)

protocol (IEC 1158-2)

PROFIBUSDP/FMS/PA

Family of communication standards.Leading open Fieldbus system in

Europe; PA is mainly used in the

process industries

DP and FMS: RS485 serial linewith baud rates up to 12 Mb sÿ1

PA: Fieldbus standard (IEC 1158-2)

Manufacturing and processautomation

Ethernet EtherNet is an industrial standard thatde®nes only the physical layer. Some

industrial standards are built on top

of Ethernet

� Coax cable with BNC connectorsor telephone wiring with RJ45

connectors or ®bre-optic cable

(10±1000 Mb sÿ 1)(standard IEEE 802.3)

Mainly in LANs for PC-to-PCcommunication.

� Ethernet does not de®ne itself as the

communication protocol. It runs

commonly under the TCP-IP protocolCAN Designed originally for in-vehicle

automotive communications

CANbus (serial bus) with CANbus

communication protocol

Also for process industries

CAN, controller area network;LAN, local area network.

10 INSTRUMENTATION AND PROCESS CONTROL/Instrumentation