EINDHOVEN UNIVERSITY OF TECHNOLOGYDEPARTMENT: ELECTRICAL ENGINEERINGgroup: Measurement and Control

CLASSICAL AND ADVANCED CONTROL INDISTRIBUTED CONTROL SYSTEMS:THE PRESENT AND THE FUTURE

by J. van den Dool

Report of the InternshipPerformed for Honeywell Amsterdam,Division Industrial Automationfrom September upto October 1993Honeywell Coordinator: J.C.J. van de WielUniversity Coordinator: prof. A.C. BackxDate: May 3, 1994

The Department Electrical Engineering of the Eindhoven Universitydoes not accept any responsibility for the contents of internship andgraduation reports

Summary

Historically, control research is being performed along two different lines: ServoControl and Process Control. Nowadays Servo Control covers academic research,Process Control covers the development and improvement of Distributed ControlSystems. Todays Process Industries are forced to improve their production methods. Apowerful method to their disposal is to improve the control algorithms in DCSscovering primary, supervisory and optimization control. Here, Control Theory canhelp. The past of both fields is described. The outlines of an ideal control system withsplit architecture are sketched. Throughout the report this "model" is looked upon asthe ideal structure for mc 3000 (Honeywell's DCS). After a detailed description ofIDC 3000, it is proven that Model Based Control can be implemented. Bottlenecks indeveloping IDe 3000 towards the ideal structure have a software character. Futureresearch will be directed on theory side to the solution of the Fundamental ControlProblem (a formulation for any control problem developed by Shell) and on DCSside to the software of the higher levels and the automation of (parts of) the controlsystem design in which Artificial Intelligence is going to playa crucial role. Researchschools can contribute to "system engineering" education and high risk but potentiallarge pay-off research.

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Contents

1 Introduction 7

2 History: Servo Control versus Process Control 82.0 Introduction2.1 Developments in Control Theory2.2 Developments in Process Control

3 General description of a Distributed Control System 133.1 What's in a name?3.2 The up-to-date Hierarchical Process Control Concept3.2.0 Introduction3.2.1 The Hierarchical Process Control Structure

4 The TDC 3000 system of Honeywell 194.0 Introduction4.1 Global description of IDC 30004.2 Currency of IDC 3000: Points4.3 Controller functions within IDC 30004.3.1 SISO control within the Advanced Process Manager4.3.2 Sequential Process Control within the Advanced Process Manager4.3.3 Controller functions performed by the Application Module4.3.3.1 Horizon Multivariable Predictive Control4.3.3.2 Real Time Statistical Process Quality Control4.3.3.3 Model Based Control versus Statistical Process Quality Control4.4 What levels in the hierarchy are being filled in recent projects ?5 Implementation of a Model Based Controller on TDC 3000 355.0 Introduction5.1 The Internal Model based Control form5.2 Internal Model based Control for a Distillation Column5.3 Implementation on IDC 30005.3.1 Requirements5.3.2 Results5.3.3 Conclusions

6 Software and Hardware 456.1 Developments in Software and Hardware6.1.1 Performance6.1.2 Programming Languages6.2 Hardware and Software requirements for the Hierarchical Control

System

7 Recent and future topics in Research 497.1 The Fundamental Control Problem

7.2 Areas of Research for the solution of the Fundamental Control Prob-lem

7.3 The Fundamental Control Problem versus the Hierarchical ControlStructure

7.4 Areas of Research for the Hierarchical Control Structure7.5 The role of Academia in Control Research

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9

Process Automation Projects

Conclusions

References

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1 Introduction

Historically, developments in "control" have taken place in two fields: Servo Controland Process Control. Servo Control evolved from the control of ships' rudders to thefield of academic control theory nowadays. Process Control has evolved in thechemical and physical process industries and has resulted in the development ofDistributed Control Systems of which IDC 3000 is an example.

Todays industries are confronted with a very dynamic and hardly predictablemarketplace which has to be serviced using the available plants and control systems:redesign is too expensive. In most process control systems safety and operabilitydemands are met and now production must be improved using academic controltheories or the ''wisdom'' of the other field. This asks for a study of a recent ProcessControl System (the IDC 3000 system of Honeywell) as to what extent classical andadvanced control concepts are, and could be implemented nowadays, and what therequirements are, to be ready for future developments.

In chapter 2 the developments in both fields are described. Chapter 3 gives theoutlines of a model of an ideal control system, prepared for the future. Throughoutthe report, this model will be treated as the ideal form of IDC 3000. In chapter 5 theimplementation on IDC 3000 of a compact Model Based Controller is treated and inchapter 6 software and hardware requirements for the ideal Control System will bedescribed after a description of the availabilities. Chapter 7 contains a reflection onthe fundamentals of every control problem and its relation to the ideal controlsystem. It can be seen as the presentation of the themes around which the discussionbetween the mentioned fields will continue. For both fields topics of recent andfuture research will be described. In chapter 8 problems and solutions are describedfor recent Process Automation Projects.

2 History: Servo Control versus Process Control

2.0 Introduction

Control systems have been developed along two different lines: servo mechanisms andprocess control. Both fields started research at the same time and evolved separatelyever since. Now the two fields turn out to be: Academic Control and Process Controlor "theory" and "practice". The distance is felt as a gap because there is so littlecommunication between people that deal with the same processes nowadays.

Servo Control systems [3] are required when the force needed to move thecontrol mechanism is too large for the human operator (ships' rudders, aircraftcontrol surfaces). The function of the control system is to follow the position set bythe operator suppressing disturbances which in general are low. The response must befast (seconds at most). Servo systems traditionally operate in a large dynamic range.In general they are built in large numbers for one particular application.

Process control systems [3] are applied in the chemical and physical processindustries. Applications of the systems are often unique. Their function is to maintainthe variable under control at a preset level in response to changes that occur in theprocess or are attributable to external causes. Process control systems generallyoperate in a limited dynamic range. The response of the plant will take at leastseconds, minutes commonly.

The persistence of the gap can be explained by the origin of the differentcontrol engineers.

The inventors of servo mechanisms were -at that time- mechanical engineers(later on a new species splitted off: electrical engineers). As said before the disturb-ances to these mechanisms were minimal. Smarter control strategies were developedbut there were always the assumptions of minimal disturbances and the application onlinear processes (aero space: avionica).

The process control engineers originated from chemistry. Their main activitywas and is inventing new products and designing the necessary production processes.Their top priorities being safety and continuous operation, they need(ed) reliable androbust controllers and not the ones that were optimal in a small range.

Application of Model Based Control forces both parties to cooperate. In themodem process control system both first principle models (rigourous, white) stem-ming from process engineers and black box models from people that apply ofacademic control are required.

Now the developments in both fiels will be described.

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2.1 Developments in control theory [3]

Classical Control was developed before and during the Second World War. Itconcerns PID controllers. The design for Single Input Single Output systems is basedon frequency domain descriptions. It is broadly applicated in industry (80%/90% ofall low level control loops). A general model suffices for tuning. Roughly tunedcontrollers are robust to changes in the dynamics of the process.

Development of Modem or Multi Input Multi Outout control started the late 50s. Inindustry MIMO control is called advanced control. Three of the most populartechniques are [9]:

Pole Placement Control: As a control-system-design tool abandoned becausethere is little insight in where to place poles.Decoupling: Earliest method for MIMO control. First suggested in 1949 ([9D],page 309). Decoupling converts the multivariable problem into independentSISO problems. The general philosophy now is to remove all 'interactions'whenever the performance is not deteriorated.Linear Quadratic Optimal Control: Suggested in late 50s by Kalman. Based onthe State Space Description of the processes to be controlled. Through theassumption that signals are stationary, controllers can be designed that mini-mize quadratic criteria. Signals that depend linearly on the states of the systemare fed back within the controller. When the states cannot be observedphysically, optimal (Kalman) filters can be used to derive the states frominputs and outputs to the process.

During the early 70s nothing was left of the enthusiasm of the 60s aboutthe optimal control theory. In the LQ Optimal Control scheme disturbancesare assumed to have infinite bandwidth (white noise) and to afflict the outputonly. LQC tries to control the process over infinite bandwidth. This requires anexact model of the process dynamics! To cope with model uncertainty (byphase and amplitude variations) feedback is used explicitly in classical control.But the problem of model uncertainty is not dealt with by state space methodswhich made them inadequate for practice.

During the 80s research was looking for a mathematical tractable relation between amodel uncertainty description and a measure of performance. This led to:

Robust Control (H_inf and H2) minimizes the weighted sensitivity operator (transferof output disturbance to the process outputs). The expected uncertainty is modeled inthe frequency domain and the control system is designed to minimize the worstperformance. Performance is specified by a bound on the sensitivity operator. H2minimizes the 2-norm or average value of the weighted sensitivity operator andH_inf minimizes the inf-norm or maximum value. The biggest problem is how to useRobust Control in practice. The weights determine how degrees of freedom will beused to match desired process response criteria. There is no systematic procedure toobtain the uncertainty description and the selection of weights is not obvious. For H2the weights can be obtained from well defined experiments. Case studies and experi-ments help here to gain insight.

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Adaptive controllers essentially are linear controllers that periodically tune theparameters to improve robustness. This requires a second feedback loop in whichinformation is gathered about the changing dynamic behaviour of the process.Examples are Self Tuning Control and Model Reference Adaptive Control. Majordrawbacks are: minimum phase behaviour of process required, large sensitivity tonoise, the parameters must be updated significantly slower than the slowest relevantprocess dynamics and expressing the stability of the system requires complex mathe-matics.

Continued research on optimal control led to the Model Based Control concept in the60s. To improve the control of a process, the controller needs to 'know' the processe.g. contain detailed knowledge about its dynamics including the process equipment.This requires an off-line identification procedure that delivers a model. MBC enablesmanipulation of system dynamics up to physical constraints, compensation for non-desired system properties, decoupling, smooth setpoint tracking and disturbancerejection. The dynamics of some processes are so complex that MBC is the only wayto control them.

When the model of a process has been obtained, still nothing has been saidabout the controller structure. The design of a specific controller uses the extensivedegrees of freedom for manipulation of process dynamics in a particular way. Thecontrol design depends on the desired process response. The translation of criteria forprocess response (rise time, overshoot, response time, ..) into useful control designcriteria is the biggest problem in this field. When using Robust Control for the designof the controller, this problem is incorporated by the selection of proper weightingfunctions.

Identifying a process within prescribed bounds is very expensive. Certainly notall processes apply for this treatment. Within the MBC class we distinguish LinearUnconstrained Control (for example Internal Model based Control in which aparametric State Space model resides) and Linear Model Predictive Control(Dynamic Matrix Control and Model Algorithmic Control which use non-parametricprocess description). In the second concept constraints are dealt with explicitly. Themethods use different process model representations.

Intelligent controllers cope with process non-linearities and complex information. Threesubclasses exist:

Fuzzy control: Uses "fuzzy sets" and "fuzzy" rational equations. Fuzzy set theoryallows to partially belong to a set. The membership of a set is expressed by anumber between 0 and 1. This facilitates the implementation of constructionslike "for many ..." or "the rudder doesn't function correctly."Expert systems: Expert systems are used to connect long rows of "if...then..else..." rules (forward chaining). Through internal feedback the system canlearn and achieve a higher speed by testing off-line what if-conditions wereexecuted (backward chaining). Expert systems are dedicated controllers thatare based primarily on human knowledge and experience.Neural Networks: Based on the principles of switching in our own neurons,many small processors are connected in parallel and in feedback. The outputsare functions of weighed sums of inputs. The weights can be adapted by learnprocesses.

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2.2 Developments in process control

Before reading the history of process control one should realize that the ranking ofpriorities of process industry significantly differ from advanced-control theorydevelopers:

1. Safety2. Keeping the plant in operation: shut downs are very precious because produc-

tion stops and the plant must be cleaned before restart3. Optimizing quality and efficiency

During [3] the first thirty years of this century the operator used simple measurementdevices (pressure and temperature gauges) to determine the adjustment of thepositions of the valves and meet the desired values. The plants were small andoperation always in steady state. Control mechanisms were mechanical.

The development of pneumatic transmission of measurement signals made itpossible to collect all indicators, recorders and other equipment into one centrallocation in the late 30s and 40s. The regulator mechanisms had been developed intopneumatic servo mechanisms that were located in the control room as well.

During and after the Second World War process plants grew larger andbecause only S1S0 controllers under direct supervision of the operators were used,control consoles grew proportionally. Now the disadvantages of centralization becameclear: (i) the quantity of information offered in the control room became too great forhuman comprehension and (ii) the increasing distance between controller and pointsof measurement and regulation introduced a significant delay into the control loops.

From the 60s the second problem could be solved by the development ofelectronic analog signal transmission. This development had long been delayed inmany industries by the danger of electrical energy in explosable atmospheres. So farthe development of control system design had been based on the universal S1S0controller rather than on the customer's wishes and although the theory behindfeedforward, interactive, cascade or ratio control existed, the implementation wasdelayed because this required hardwired or piped interconnections between theindividual controllers and the design of the control panel could only be altered whenthe plant was shut down. Replacement of pneumatic by electronic control of pro-cesses is still ongoing.

The appearance of digital computers (Direct Digital Control) in the 60s didn'tsolve any of the problems. The computers were large, expensive and not reliable.Application of a central computer was only justified if it replaced 100 S1S0 control-lers and at the same time this centralization in a medium which was not reliablecreated a dangerous situation. This forced the computers to play a supervisory role atfirst (setpoint optimization).

The development of the microprocessor and the integrated circuits made itpossible to distribute intelligence over a control system. During the first half of the70s Distributed Control Systems were developed. Through the User Station theoperator can have access to all control loops to facilitate coordination and steering.During the second half of the 70s it became possible and economic to use micropro-cessors in control systems. This led to increased computational power and higher

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robustness.The advantages of using computers within a distributed architecture:

The distributed architecture overcomes the "all or nothing" problem of controlsystem availabilityA reconfiguration of the interconnections between SISO controllers is possiblewhile the plant is operatingThrough the data processing capability of computers it is possible to displaylarge amounts of data in a way that the operator can make use of itIt reduces the amount of cabling required since digital signals can be multi-plexed and the analogidigital interface can be located near the points ofregulation and measurement.

With each new version of DCS software information about the process is added. Itsrepresentation to the operator asked for more graphic capabilities in the 80s. As anexample, SPQC is treated here.

During the second half of the 80s Statistical Process Quality Control [10] wasintroduced. SPQC uses statistical methods to improve process productitivity andproduct quality.

In rebuilding their (parts-)manufacturing industries after WW II, the Japaneseput an extreme priority on quality which led to the Japanese miracle. Because of itsdirect relation with "Quality Management", SPQC receives a lot of attention from themanagement of process industries. However it is not that natural to apply SPQC inprocess industries. Inspired by its parts-manufacturing background, SPQC starts withthe assumption that the process will remain on target unless an unexpected eventoccurs. A situation is "in statistical control" whenever the measurements are indepen-dently and normally distributed about the target with constant variance.

Engineering process control assumes that the process under study is alwaysbeing disturbed by disturbances whose sources may be known but cannot be elimin-ated and that the best one can expect for being "in a state of statistical control" is thatthe quality measurement behaves as a stationary process centered about the targetwith as small a variance as possible.

The great value of SPQC lies in the fact that operators can work with it in avery easy and pleasant way. It provides a means of listening to the process and thecontrol system to eliminate causes of problems. The introduction of gatewaysin the 80s linked ncss with PLCs, analyzers and other computers. This developmentmeant further integration of the control system.

In the 90s the control system is being further integrated; the system gets deeper andwider which facilitates management to pop up whatever information they want (moreor less detailed, recent or old, far away and close) and direct their orders world-wide."Open Use" and "Object Oriented" are key words of the 90s.

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3 General description of a Distributed Control System

3.1 What's in a name?

The word "distributed" [3] in Distributed Control Systems reflects on systems whichdepend on distributed signal processing of plant measurement and control signals. Inthis type of signal processing, measurement data are converted to their digital form,stored in a memory and multiplexed for serial telemetry over the data link. Nowadaysa lot of people use the word "distributed" to denote that intelligence is distributedover different levels within one control system: primary, supervisory and optimizationcontrol.

For the data link there are three configurations: star, ring and multidrop (seefigure 3.1). The configuration depends on the nature of the data transfers required bythe system architecture. The advantage of the ring configuration is that it toleratesone break.

To overcome the problem of distortion of digital signals and failure of I/Oequipment (AD interface and multiplexing) duplication or even triplication (nuclearplants) of these elements is required. Received and decoded signals are compared toidentify distortion.

From now on Distributed Control Systems will denote control systems thatmonitor and control a process in real time. It provides a user interface to the processand supplies real time control actions to the actuators.

3.2 The up-to-date hierarchical process control concept

3.2.0 Introduction

Originally DCSs were developed to keep processes in operation (second priority ofprocess industry, chapter 2.2), safety being guaranteed by "robust" mechanical processdesign and an independent, hardwired safeguarding system. Nowadays one is inte-rested in matching the third priority: optimization.

Todays industries are confronted with a full customer [4] market. This meansthat they have to deal with a very dynamic and hardly predictable marketplace, inother words produce relatively small quantities with low margins and in spite of thisremain competitive and profitable. Count to this the high social attention forenvironmental problems we arrive at the situation that industries need reliablemethods, techniques and tools to operate processes at maximum efficiency, with totalquality control and maximum flexibility.

Most industries cannot afford to redesign their plants. The mentioned goalsmust be achieved using existing plants and control system hardware.

Translated into control terminology industries need fully integrated optimalcontrol of all interacting unit processes such that decisions taken at management level

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(entered in management terminology) are executed by the control system in the mosteconomic way.

Because process operation has to be close to the process constraints thecontrol system has a hierarchical structure. Now, the up-to-date concept will bedescribed [4].

3.2.1 The hierarchical process control structure

In the hierarchical concept, control in the broadest contents is executed at differentlevels, with different priorities, at different sample frequencies. 9 Levels can bediscerned [1], [4]:

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Minicomput4'lr

Local op,natorintcrrfacQ and

store

Star links

Plantmicrocomputer

PlantmicrocomputQr

Plantmicrocomputer

tal

MinicomputerLocal operatorinterface and

store

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Plantmicrocomputer

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(b)

Plant(data

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Figure 3.1 Data link configuration [3J(a) star (b) ring (c) multidrop

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level 0 :level 1 :level 2 :level 3 :level 4 :

level 5 :

level 6 :

level 7 :

level 8 :

field instrumentation and AID interfacesmanual emergency systemSafe Guarding Systeminterlockingprimary process controlT < = 1 secondunit process control: second < = T < = 1 minuteconstraint handling and unit process optimization1 minute < = T < = 1 hourdynamic plant performance optimization1 hour < = T < = 1 dayproduction optimizationT > = 1 day

From level 4 upto and including level 8 sample times are derived by multiplying thesample time of the lower level typically 10 times. Average sample times are given.For some processes, unit process control has a sample time of 1 hour.

In figure 3.2 the 5 last levels are given together with their interconnections.

Oyru.m: PIlMpwiorn'llroc:4opciMnOOf'l

Primaryc:ontrol

Figwe 3.2

ProdJcaon Khedulinc and rnodoII bued dyn,,,,,c PI"t pwionnaflcaopOnwuaon

Hierarchical process control [4]

The field instrumentation and AID inteifaces level covers the sensors and actuators(valves) that are connected to the field and the AID and DIA converters thatinterface the process and the control system.

The manual emergency system is a system that can be started by pushing "the redbutton" and brings the process to a safe shut-down state. It is hard wired and fullyseparated from the control system.

The safe guarding system is a system that accomodates the so called safe guardinglogic and automatically leads the process to a safe state when certain conditions arenot met. Safe guarding systems are fail-safe micro computer based.

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Interlocking is the lowest level of the control system. To prevent the process of gettinginto an abnormal situation, it checks and, if necessary, corrects all I/O signals.

The primary process control level covers the control of primary process inputs (e.g.pressure, flow, speed, ..). In this level manual, feedforward and feedback control takesplace which is mainly executed by SISO PID control systems. Dependent on the appli-cation the tuning can be fIxed or automatic. The primary control in general results insmoothly responding linear or weakly non lineal process inputs (figure 3.3). At thislevel the operator is able to dictate setpoints to the PID controllers ("automatic"control) or to enter the controller outputs directly ("manual" control).

5 P --Jl,.,)PIDL...-1 ......_-41~~ IPV

Figure 3.3 Example ofprimary process control

When in normal operation, the setpoint inputs of the SISO PIDs are the manipulatedvariables for the next level: unit process control. This mode is called supervision.

First a general definition: the process outputs are the variables to be controlled(Controlled Variables) and the inputs for the controller. Another name is ProcessVariables. The manipulated variables (MV) are the inputs to the process being theoutputs of the controller.

The unit process control level covers the control of MIMO processes, in industry called"advanced" control. A unit process is a part of the plant consisting of several inputs(setpoints to the primary process control) that can be manipulated within a definedrange, a set of disturbances that cannot be manipulated and a set of process outputsthat have to be controlled according to given specifications. The manipulated andcontrolled variables have strong dynamic interactions which makes model basedcontrol the best way to control the MIMO process units.

As by the first level the transfers between manipulated and controlledvariables were made smoothly nonlinear and operation often takes place in specifIcpoints or ranges, the dynamic behaviour of the process unit can be approximated by aset of linear models.

The inputs of the unit process can be manipulated within bounds. To preventthe signals of overshooting the bounds, a constraint handler is introduced. Constraintsare variables or functions of variables to be kept within bounds. We distinguish twotypes: hard constraints do not allow violations, soft constraints temporarily do for thesatisfaction of other criteria. The constraint handler uses a process model to simulateprocess responses over some horizon and to detect whether constraints at inputs,outputs or states are violated. The constraint handler will adjust signals if necessary. It

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interfaces the unit process control level and the unit process optimization level.

The unit process optimization level guarantees optimal operation of the supervisedunit processes. This can also be denoted by "coordinated" control that can decide toshift the process from one point of operation to the other. In this level the overalldynamic performance optimization for the whole plant is combined with the operationof the unit processes up to hard constraints. Unit processes usually have moremanipulated variables than controlled variables. The corresponding degrees offreedom can be used for further unit process optimization. Options for using thesedegrees of freedom:

introduction of additional (soft) constraintsminimization of additional criteria (mostly related to operation economics)operation of input variables at preferred variables (ideal resting values)

The soft constraint values and ideal resting values depend upon instantaneousoperating conditions and have to be determined from plant wide optimization.

The dynamic plant performance optimization level covers the optimization of theoverall plant. This includes the determination of production schedules with thecorresponding unit process operation conditions. These conditions include the setpointvalues for the unit process control level, the soft constraints that are sent to theconstraint handler and the ideal resting values in the unit process optimization leveland additional criteria for the unit process control systems.

The production schedules determine when, what product, in what unit processwill be made. Production schedules are based upon characteristics of the productionfacilities and production planning information.

To determine the corresponding operating conditions of the process units,linear programming techniques based on steady state plant simulation are usednowadays. As stated before industries are confronted with a dynamic marketplace thatorders relatively small quantities of specific (narrow banded) quality. Production mustbe possible for different input-product-qualities (crudes for crude distiller). Differentqualities of input product mean different steady states which are not always reacheddue to changes in inputs and small amounts of ordered quantities. This is why theoperating conditions based on steady state plant simulation will not be optimal. Abetter optimization technique will be to use rigorous (white box) dynamic modelbased simulators for the unit processes that significantly contribute to the overallplant performance.

The production optimization level covers overall optimization of production. Optimalproduction planning is determined considering the products requested, requiredmaintenance etc. A target planning is obtained which is used to determine theproduction schedules. This activity is nowadays also denoted by Computer IntegratedManufacturing.

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4 The TDC 3000 system of Honeywell

4.0 Introduction

The IDC 3000 system [6] is a Distributed Control System with a multidrop data linkconfiguration [3]. TDC replaces Total Distributed Control which stresses the splitarchitecture of the controller functions. In figure 4.1 the IDC 3000 architecture isgiven.

A global description of TDC 3000 follows, TDC 3000 will be projected on thegeneral hierarchical control structure given in figure 3.2 and control functionality willbe treated.

4.1 Global description of mc 3000

Within the TDC 3000 system (figure 4.1) four levels can be discerned: the processcontrol networks level covers the primary process control and unit process control level(figure 3.2), the process supervisory level covers the unit process optimization level, theproduction level covers the dynamic plant performance optimization level and the plantmanagement level covers the production optimization level.

There are two types of process control networks: The Data Hiway which is an"old" system and will not be described here and the Universal Control Network whichwas introduced in 1988.

The Universal Control Network is a 5 Mbit/sec token ring network compatiblewith IEEE and ISO standards. This description of TDC 3000 focusses on the UCNand not on the Data Hiway because it is the most advanced control subsystem withinIDC 3000 and is applicated in most of todays process automation projects. The UCNhas three types of nodes: the Process Manager, the Advanced Process Manager andthe Logic Manager. Both types of PM's do data acquisition and have modulating ofsequential control as well as interlocking capabilities. The advanced type has largermemory capacity (table 5.1). The primary function of the LM is to provide rapidexecution of logic-type operations. It is composed of PLC's and an interface to theUCN.

The Local Control Network 5 Mbit/sec serial coax token ring. Its maximumlength is 300 m and 32 redundant devices can be connected. When a device possessesthe circulating token it is allowed to broadcast its information to all nodes on theUCN network.

The nodes at the upper side of the LCN perform the operator interface, savethe history and process supervision task. Via the Universal Station one can superviseany part of the system that is lower in hierarchy including the nodes on the LCN. TheHistory Module is a hard disk that contains the software of the system, the graphicobjects and the collected history. The Application Module can be used in combinationwith UCN nodes A)PM, LM) to extend the control capabilities.

To make the transition between transmission techniques and communication

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protocols, interfaces are required to connect the different networks. The HiwayGateway connects the Data Hiway to the LCN, the Network Interface Moduleconnects the UCN to the LeN and the Programmable Logic Controller Gatewayconnects the Safe Guarding System to the LCN. Several LCN's can be connected by aNetwork Gateway.

The Local Area Network stretches out plant-wide. Its interfaces to the LCN arethe PLant Network Modules. Its nodes execute the management information task.VAX. computers perform the dynamic plant performance optimization and by meansof other host computers (including a simple Personal Computer) the productionmanager can optimize the overall plant production (Computer Integrated Manufactur-ing).

4.2 Currency of TDC 3000: Points

The IDC 3000 operator console contains several User Stations (up to 10) with sharedperipherals. From the console three

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AM ,',= ApplicationModule , EOS '" Enhanced Operator Station lCNE' , '" Local'Coriit' PG ,'" Prqc~llsq~;AMC,':: '" Advanced Multifunction Controller' G~CL '" General Purpose Computer Interface, ll.MUX': '" Low,Lev ' ' PLC,'; = Proghu'r:i'BC",:"'" Basic Controller '",,~ HeM,;'", H1wax. qquplipg Module,,' I.M30i(:';", L(jgi~;;'PM30ci:.iproces~iBOS,; ',;= Basic Operator Station ,,"'HG : ",Hiy..ay Gateway 'MC', '

sections of a process, subdivided in unit processes, can be controlled: area, unit andpoint.

An area is a portion of a process controlled by one operator. Example:polymer area. A unit is a portion of an area represented by a number of points, a setof alarms and messages. The control responsibility of the operator for the given unitis limited to this set. Example: reactor unit. A point represents all of the informationabout a specific process related item and can be considered as a data record repre-senting a number of related I/O and parameters. It is the basic element to whichcontrol functions are applied. The main elements of a point are:

Point IDParametersFunctions

Point ID is the name of the point (tagname) throughwhich it can be called. In"Parameters" is declared what inputs and outputs are used and the values of tuningconstants. Example: PID constants. "Functions" defines the operation of the point.Example: PID. As an example possible points of the (A)PM are listed below.

Analog Input Points:Digital Input Points:Analog Output Point:Digital Output Points:Digital CompositePoints:

Totalizer Points:PID Control Points:

Logic Slot Points:

Process Module DataPoints:

Numerics, flags, timers

bring in continuous process databring in digital informationtypically used to drive control valvecontrol individual relais

combine related DI and DO into one point (e.g.on/off values)accumulates continuous flows over timeclosed loop control of process value, contain AIandAOinterlocking: part of the interface between processand control system, check whether outputs of thecontrol system or Process Variables are withinpredefined safety bounds.

user written algorithms by means of Control Lan-guages

Each point is associated with a slot of the memory in the control device A)PM,LM,-AM).

A SISO control point has two inputs: Process Variable (PV) which is theactual process output, SetPoint which is the desired value for the PV. It has oneOutPut to the final control element (control valve). A control point has three modesof operation: manual, automatic and cascade. When just has been switched to"manual", OP is held at current position and the operator is in direct control of OP.When in "automatic" the operator controls SP and the controller automatically adjustsOP to keep PV on SP. In "cascade" OP of one controller (master/primary) is SP inputto the following controller (slave/secondary). When switching from "manual" to

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"automatic", the process is not allowed to be disturbed by step changes in OP. Thisrequires bumpless transfer mechanisms (e.g. PV tracking).

Some points are more important to the operator than others. Within APMthree point forms are distinguished. The full point form is to be fully specified. Thecomponent point form only delivers information to the system. The untagged pointform has no name and is referenced by module (= node) number, slot number, andwhether its type is input or output point.

4.3 Controller functions within TDC 3000

In figure 4.2 the distribution of the IDC 3000 nodes over the levels from section 3.2.1is depicted.

We will focuss on control now rather than on handling abnormal situations andinterlocking. In the following the control possibilities will be treated for the AdvancedProcess Manager and Application Module. The LM will not be examined. It's one ofthe APM's peers on the DCN. It uses ladder logic to execute programs fast.

23

Control Hierarchy

e.g. PC ~ Production Optimization

Dynamic Plant PerformanceOptimization

Unit Process Optinlization andConstraint Handling

Primary Process Control

(A)PM Unit Process Control

AM

e.g. VAX

TDC 3000

LM Interlocking

PLCG Safe Guarding System

Figure 4.2 Relation between TDC 3000 and the general hierarchical process controlstructure

24

4.3.1 SISO control within the Advanced Process Manager

When a point requires standard SISO control within the APM, it is referred to as a"regulatory control point". An algorithm can be allocated through a special menu. Thepossible algorithms are treated below.

The Proportiona~ Integra~ Derivative (PID) controller is a SISO feedbackcontroller that reduces the error between Process Variable and SetPoint to zero. Itreceives PV and SP and outputs the Control Variable in percentages of the maximumvalues.

A PID point in the APM can be the master of another datapoint in the sameAPM or in another APM on the same DCN.A PID point in the APM can be the slave of another datapoint on the same DCN orin the Application Module (constraint handling) or in another computer through theComputer Gateway.

There are two possible forms: interactive and non-interactive form. In theinteractive form the P and I action are added and multiplicated with a special versionof the D action. In the noninteractive form all actions are added (also "digitalcomputer version"). The latter is used to emulate the old pneumatic PID form. Thevalues of the constants can be easily copied in the interactive form.

Both forms have 4 different equations, introduced by the different values theP,I and D act on.

D Ion PV-SP

AB

C

P, I, D act on PV-SPP,I on PV-SP, D on PVt-PVt_T

I on PV-SP, P,D on PVt-PVt_T

to reduce error to zero as quickly as possibleeliminates spikes in CV when quick changesin SPsmoothest and slowest response to changesin SP

In the A,B,C equations the P and I actions are added.

For the Interactive form:

Equation A - P, I, and 0 act on the error

CVs

= k x (1 + Tl * s * 1 + T2 * sT1 * s 1 + a * T2 ~ s ~ (PV? s - S ? ? s) )

Equation B - P and I act on error, 0 acts on PV

1 + Tl * scV s = K x (-----'--...;:.T1 * s

1 + T2 * s1 + a x T2 ~ ~ pVP s -s

1 + Tl x sT: s ... sPPsJ

Equation C - I acts on error, P and 0 act on PV

1CVs = K x (+ Tl x sTl * s

i + T21 T a

s 1~ ?V?s - --~- x SPPsJTl x 5

Equation 0 - Integral control, only

* PVPs - SPP s )s

25

For the Noninteractive fonn:

Equation A - P, I, and D act on the error

1 + T1 * 5CV5 = K '" [( T1 * 5 + 72 * 5) * (? VP 5 - SPP5) ]

Equation B - P and I act on error, D acts on PV

1 + T1 * 5CV5 = K '" [( + 'x2 * 5)71 '" 5

Equation C - I acts on error, P and 0 act on PV

1 + 71 '" s71 " s

* SPP s ) ]

1 + 71 '" sCVs = K '" [( 71 * 5

Equation 0 - Integral contrOl, only

1CVS = 71 * 5 * (PVP s -

+ 72 '" S)

SPP s )

1 ~V?s - --~-- '" SPPs]

T1 " 5

cv = Output of the PID algorithm, full value in percent

a = A constant equal to O.l. l/a is the high-frequency gain or rateamplitude.

K = Gain. See 8.13.1.3.7.

PVP = The process variable in percents = The Laplace operator

spp = The setpoint in percent

Tl = The int~gral time constant in minutes per repeat for interactiveform.

T2 = The derivative time constant in minutes.

26

There are four options for changing the gain (K). In linear gain the (constant) value isspecified by the user. Through gap gain modification K is decreased when the errorreaches a specific (small) value. In nonlinear gain modification K is proportional tothe square of the error. In external gain modification K can depend on an input valuefrom the process or a user written program.

The PID controller is completed by anti-windup handling (if windup then Iaction is stopped) and bumpless transfer mechanisms between "manual, automaticaland cascade".

The PID with FeedForward (PIDFF) controller is identical to the normal PIDcontroller but now the influence of an extra variable can be put in the output of thecontroller. The feedforward signal is added to or multiplicated with the output of anormal PID controller and accumulated. Before the FF signal is fed-in it must havebeen subjected to deadtime compensation or lead-lag compensation.

PID with external reset feedback (PIDERFB) accepts a reset feedback signalfrom another datapoint, typically a PV of slave-datapoint that receives SP of PID-ERFB. This technique prevents a wind-up.

PID Position Proportional Controller (PIDPOSPR) is a normal PID in cascadewith a PosProp controller. The OP of PID is the PV of PosProp which generates raiseand lower pulses.

Ratio Control (RATIOCTL) calculates the SP for a PID that must realize thedesired ratio of controlled and not controlled variable.

Ramp and Soak (RAMPSOAK) is used as a setpoint programmer for afollowing PID.

Auto Manual (A UTOMAN) is the slave of a cascade that may add to the OP ofthe master-controller a bias value that is provided by the operator.

Incremental Summer (INCRSUM) is the incremental sum of the weighedchanges in at most 4 variables.

Switch (SWITCH) chooses between the outputs of at most 4 controllers.The Override Selector (ORSEL) passes the input (1 out of at most 4) through

with the highest or lowest value. Through this it is possible to measure and control aspecific process variable and have another variable selected to constrain the con-trolled variable under a specific condition.

4.3.2 Sequential Process Control within the Advanced Process Manager

The point within the APM that executes a Sequential Process Control program is theProcess Module Data Point. These points allow to implement MIMO or unit processcontrol within the APM (see chapter 5). The language by which the SequentialProcess Control is implemented is Control Language/APM. Control Languageemploys a variety of general and process oriented statements with improves bothsecurity and throughput. The maximum number of statements is roughly 3*(1500Q-n)with n = number of memory units used by datapoints. The programs can be loadedeasily from the Universal Station. Through a PMDP sequential programs can bestarted, status can be observed and alarms can be sent to the system.

A CLIAPM sequential program is able to read and write values from any nodeon the same VCN, to be subjected as a slave to a datapoint in the Application

27

Module, to use local variables, to start another sequential program and to communi-cate with the operator.

The PMDP's can be partitioned on a process unit base (figure 4.3).

Advanced Process Manager Module

(APMM)

t::::

I

"Process ModUle Data POints ~60, 2 3 4 5 ($eQuence Slots)-C ) < ~ Ie )

abnonnal condition handlers:

Hold:Shutdown:EmergencyShutdown:

Perform corrective action when abnormalconditions are encountered. Three types:

partial shutdown (lowest priority)systematic shutdown

complete and sudden shutdown (highest priority)

The normal sequence can be started by an operator at the US, a statement "Initiate"in another sequence, CL block in AM, user written program in VAX computer(Computing Module), abnormal condition handlers and by a predefined processcondition detected by the sequence program.

4.3.3 Control performed by the Application Module

The Application Module is one of the modules on the LCN. The AM can communi-cate with all modules attached to the LCN including other LeNs and the processcontrol networks (Hiway and UCN). It can provide directly control outputs toactuators in the field or to datapoints in the surrounding modules (including itself). Infigure 4.4 all functions are given.

: BUILT-IN !! ALG(JArrHLAS~

..

PROCESS DATABASE

Ig

Figure 4.4 Application Module Functions

The AM contains built-in algorithms that perform point processing and control. Theprocess database contains datapoints. The Internetwork Point Processor provides forclosed loop control across the Network Gateway (connection to other LCN). As inthe APM, points can be defined in the AM that execute CLIAM programs. Programscan be coded at the US or in "Workbook"-environment (runs at DOS machine) andafterwards compiled at the US to eLIAM object code. A user-written CL block canbe bound to one datapoint or written as a generic CL-block.

CLIAM programs have two modes: foreground and background. Backgroundprograms have lowest priority (executed when CPU time is available). Within theAM, CL can be extended with the possibility to retrieve history from the HistoryModule (at most 262 values collected in one call) and by adding a Math Librarywhich covers a set of math subroutines (manipulating matrices, CPU usage calcula-tions, ..) for those AMs with high performance processor and math coprocessor.

Apart from user-written and standard algorithms that adapt signals from

29

datapoints in various ways the AM contains the same built-in control algorithms as inthe APM.

Several standard software application packages are available that run in theAM:

Looptune II: optimally tunes PID control loopsHorizon MultivariablePredictive Control: MIMO Model Based C0ntrolReal Time SPQC-II uses statistical control for early detection of quality prob-

lems

Only HMPC and SPQC-II will be described here.

4.3.3.1 Horizon Multivariable Predictive Control

Horizon Multivariable Predictive Controller (HMPC) is a model based controlalgorithm that executes in the Application Module. HMPC is used in stead of simplePID control if the process has difficult dynamics (dead time, instabilities, high order)or as supervisory control/unit process control.

The model allows the controller to predict how the process will react c.q. whatthe PV will do in the future for a certain controller output now. The algorithmcalculates the OP now that will make the output of the model (= PV) equal to thesetpoint in future time. So HMPC has only one tuning constant: future time =Correction Horizon. For the tradeoff between response, effort and robustness seefigure 4.5.

Small HFast correction

Large ouptut change!>Sensitive to model errors

~ H --..~La!'.&e H

Slow correctionSmall ouptut changes

Tolerant of model errors

Figure 4.5 Tradeoff between response, effort and robustness

HMPC works well if the process can be approximated by a linear, time invariant (stepresponse is same when test is repeated at different times) model. Most processes arelinear over the normal operating range but slowly or infrequently changing dynamicscan be dealt with as well by reidentifying the model.

The output of the controller can be sent directly to the process or cascaded tothe SP of a slave controller. The process "seen" by the upstream controller includeseverything between the OP and PV, induding all downstream controllers.

If some of the process disturbances can be measured or calculated it isbeneficial to feed them forward. Feedforward makes the controller aware of the

30

effects of disturbances before they cause a deviation of the PV fed back to the con-troller and SP (see figure 4.6).

Figure 4.6

r - - - - - - - - - - - - ,

I

Process 2 II

HPC IFeedforward I

Actual

Process

+

HPC Process 1ControllerL

- - - - - - - - - - - -J

Control loop with one feedfolWard

Feedforward can also be used to implement non-interacting control by compensatingfor the interactions between two or more loops (see figure 4.7).

SP2PV2

r-------------------lPV1 I OPl I I I ISP1~ HPC I ProcesI511 J. IControUer 1 I I I I + '

HMPC consists of four software products: Data Collector, Identifier, HMPC controllerand Simulator.

The Data collector collects data for building the mode1(s). Once the variablesthat will be used are identified, the responses of the outputs are collected to changesin the inputs of the unit and stored in the AM data base. In the real world productionmay not be stopped so the collection must take place within the disturbance margins.Because the following step takes place in a PC, the data must be transferred from theAM.

The Model Identifier creates mathematical models for each Control Variableexpressed in Manipulated Variables. HMPC uses the Finite Impulse Response formbut parametric models like the Auto Regressive Moving Average form are alsosupported. Before the real identification takes place the data have to be prepared.During a data conditioning session, invalid data must be replaced by linear interpo-lations, the sample frequency must be adapted to the dynamics of the signal, the datamust be filtered. The Data Analysis determines whether there is enough informationto create a good model (through Signal to Noise ratio, Correlation-, Spectrum- andtransfer function analysis). In a MatLab environment the model is built off line. Ifavailable the user can also enter the transfer function matrix, a routine will transformit to parameters of the model.

Figwe 4.8

History Module

LeN Resident

PC Resident

I Identifier

HMPC Data Flow

32

Editor

The HMPC controller definition and fine tuning takes place in the Pc. It is aninteractive process that requires a simulator that is -of course- the model. In figure4.8 the HMPC data flow is depicted.

If the user is satisfied, the models and HMPC controller structure are trans-ferred to the AM to actually control the unit.

4.3.3.2 Real Time Statistical Process Quality Control II

Real Time SPQC is a software package that runs in the AM that helps to minimizethe process variation and maximize the product quality. SPQC could be seen as anintelligent observer of the process and is a means of extending the operator's view.When certain ranges are crossed the operator is notified and can take correctiveaction. SPQC has three main functions:

automatic data acquisition from any point in TDC 3000 or manually enteredcontrol charting makes distributions easy to comprehendstatistical alarms: point alarms, range limit alarms

Using these tools in a proper way helps the user to detect quality problems beforethey occur, to detect changes in raw materials and to determine which productionareas affect most the end product quality.

4.3.3.3 Model Based Control versus Statisical Process Quality Control

Well designed Model Based Control systems know and use the dynamics of theprocess to perform optimally control. During the identification procedure the mostimportant disturbances were involved in the control system design. The quality of theproduct in a steady state is being guaranteed when the circumstances are the same asduring the identification.

MBC can be combined succesfully with SPQC to processes that show fast andslow dynamics, the latter caused by slow disturbances. The MBC uses the fastdynamics and the SPQC filters out the slow disturbances.

For processes that are hard to identify and can be controlled only by roughlytuned PID controllers, SPQC can help. The assumption that the process behaviour isstatistical might be the best under certain conditions.

4.4 What levels in the hierarchy are being filled in recent projects ?In this section the 9 levels of section 3.2.1 and figure 4.2 are important.

Section 3.2 was based upon the up-to-date hierarchical process control concept.Up-to-date means optimal here. However, in recent process automation projects lessadvanced structures are used due to the different priorities of process industry statedin section 2.2.

The levels 0 up to 4 (field instrumentation up to primary process control) arefilled in for almost every project. Experience gained in former projects is represented

33

by standard solutions for standard problems being gathered in libraries.As the primary process control level, the unit process control level (5) is

responsible to the unit process in operation in a specific point. In most applications itdirectly receives supervision from the operator at the User Station instead of thehigher hierarchical level. For those applications the operator has a larger responsibil-ity, interfacing the higher optimization levels (of which some can be automated) andthe lower control and emergency levels.

The presence of an AM on the UCN (figure 4.2) does not necessarily meanthat the unit process optimization level (6) is automated. In a lot of applications it isonly an extension of the APM that executes special user written algorithms at thesame level of control as the APM. In practice level 6 is denoted by "co-ordinatedcontrol". It can provide two different types of coordination: (i) sequential processcontrol to move to another point of operation (in the unit process optimizationterminology this motion would be optimal) and (ii) all kinds of calculation programs.Examples: calculation of controller constants at certain time intervals (AdaptiveControl), "Bang-bang Control" [1].

As stated in section 3.2.1 still a lot of research has to be done to develop thedynamic plant performance optimization level (7): requires white box modelling andsimulation of the total plant in order to derive production schedules with operatingconditions.

The production optimization level (8) or Computer Integrated Manufacturingis connected directly to the management information systems (supplies, data).Applications are known. Its success as a means of directing orders is of coursedetermined by the quality of the lower levels.

34

5 Implementation of a Model Based Controller onTDC 3000

5.0 Introduction

In 1992 Honeywell implemented a Model Based Controller in the IDC 3000 system[11]. It was a pilot project: one wanted to know what possible problems could occurduring the project (planning, effort, responsibility), to what extend IDC 3000 isprepared for the implementation of a Model Based Controller (level of standardsoftware and hardware, operator interface: turning the controller on and of) and howmuch system capacity the controller would take (memory units, processor time).

5.1 The Internal Model based Control form

The Model Based Controller developed by IPCOS (SETPOINT IPCOS now) has theInternal Model based Control form [4] of which the most general scheme is depictedin figure 5.1.

Setpoi~

Incernal modelhued control

syn.emProcessoutpuu

Figure 5.1

--------------------------------------------------.--.----_._-----------~

Intemal Model based Control scheme [4]

The philosophy behind the scheme is that as much information as possible is fedforward and only unmeasurable disturbancesare fed back. In practice only forminimizing the error (difference between process and controller output) feedback isnecessary.

The scheme contains several model based controllers. The feedback controllercan be designed with H inf, the feedforward setpoint compensator is designed toprevent that operating co-nstraints are violated by changes in the operating point ofthe process. In "Model" the process transfers are modelled, in the feedforwarddisturbance compensator disturbance transfers of measured disturbances to theprocess outputs are modelled. The latter compensates measured disturbances bysubtracting the modelled output disturbances from the error signal and adding them

35

HIHo

Top product

Reflux levelcontroller

....

. .

Bottom product

Reflux tank

Condensor

r- - - - - ~~~IIIII r --I II I

III

~~~~l!~t ~

~?~~o_~J2l~~ty J

Reflux setpoint--------,

II

MIMOcontroller

Reflux

Feedflow

Preheater

Reboiler steamflowsetpoint

r-----------,I

r--;---~t II I

I----------------------------------

Stearn

Feed

Cooling water..

~~'~~t\.)

c,t:;'....

-.;::::l::l....c:::l('lcE'"-.......

:::ll::l..

~w ~0'\

("')C:::l....~::::::

to the model output. The constraint handler uses a process model to simulate processresponses over some horizon and to detect whether constraints at inputs, outputs orstates are violated. If so it will adjust signals.

5.2 Internal Model based Control for a Distillation Column

In figure 5.2 the distillation process and the controller are depicted. Of the primaryprocess control level only the PID controllers that receive setpoint values of theMIMO controller are shown.

Inputs of the controller are:top puritybottom purityfeedflowOutputs of the controller:setpoint for the reboiler steamflowsetpoint for the reflux flowThe controller architecture is not listed here because it is confidential informa-

tion. The general structure of the controller is given in figure 5.1. The controllercontains only two models. Apart from the process (including the primary controllevel) transfer model, the feedflow is the input of the feedforward disturbance model.The feedforward setpoint compensator is not used and the feedback controller is aProportional action only. A limiter limits the control signals to the process and thecontroller uses de-filtering. Defiltering is applicated when both the filtered and theun-filtered version of a signal should influence the steer signal. The models are StateSpace versions.

5.3 Implementation on TDC 3000

5.3.1 Requirements

The MIMO controller acts as a unit process controller. As depicted in figure 4.2 theunit process control level is covered by the (A)PM. The IMC controller was compact(State Space) enough to implement it in the PM (Process Module Data Point). Theimplementaton of an explicit constraint handler was not part of the project. If it hadbeen or will become, it ought to be implemented on the AM (unit optimization level)where there is enough space even to deal with a number of complex soft constraints.

The goal of the project was not to prove that the controller performed well(control the process) but that it could be implemented easily on IDC 3000 (A)PM.This is the reason why the models used in the controller could do as the processsimulator during the final tests. This raises one withdrawal: The MIMO controller isbased on models of the primary process control level + the distillation process. If themodels used in the MIMO controller function as simulators, the controller should beattached directly to the simulator. In this case the operator can only enter the desiredtop and bottom qualities and simulate the feedflow through a setpoint value. Toincrease the operator's power, the same PIDs as in the primary process level and

37

wco

TDC-3000Implementation .blockschemeIPCOS IMC

Operatorsettings

field inputs

digital)

>... ".3'-' eywell Amsterdam..... 11-92

digital

CL(JLOCK Col_Mirna

~rr :;0,;-

Program structure

Sequence Col_Mimo(PM; Point Col_Mimo)

External definitions and references

Local Variable definitions

Local Constant definitions

Phase Init

Define variables with a constant value (singles and arrays)

Phase Control

Step Read Val

Read Quality PV'sRead Quality SP'sRead Feedflow PVRead Reflux and Reboiler PV'sCalculate Fs filter

Step Proc mod

Multiply Am_Xmk = PJn * XmkMultiply Bm_Xmk = 8m * XmkAdd XJm< = Am_Xmk + Bm_Xmk

Multiply Cm_Xmk = em * XmkMultiply Om Umk = Om * UmkAdd Ymk = Cm Xmk + Om Umk

- -

Step Dist_mod

Multiply Ad_Xdk = Ad * XdkMultiply Bd Udk = Bd * UdkAdd Xdk = Ad Xdk + Bd Udk

- -

Multiply Cd_Xdk = Cd * XdkMultiply od Udk = Dd * UdkAdd Ydk = Cd_Xdk + od_Udk

Calculate Fr filter

Calculate Reflux and Reboiler SP's

Goto Phase Control

End Mimo Col

Figure 5.5 Sequential Program Structure for MIMO Process Module Data Point

40

cI) ,~i lin q Ld :3 t e r'..._-- - ---II

-1., _.. , , ' ,h. ~ l' 1'.J ,.,

, -__.._..--.--:-,,---..1:30.110 I(F(:"~'.. :.1 I

. -- I .111'''''''''''''''''''')I .Refluxtank

~~~~~~~~r I~l....lIlol4--fo!-r-r-rt ======== I;';' r-(O} ~ ~ ~ ~ ~ Cp b ~ ,i

I L.U.__ __ _Jp. _ S..I T - ,- I" "., .. j I I 0. fl I.U 1..1 ..'; 1.1 I ..J I. '.,I I 'L Ir-lo........~ -+_. .J .-..--- ---- --.-.--..-.- ------( iJ ", 1 f F"1 ';1 ~ ~~,--F.~_e_.b_l_1 1_'....1_e_r'__...... jIalll- 'I,~_._,,'_.__.-l._ . .~,~

Condensate Bottom product

i-_.~~

1~.00r-----i .'-.

i 'F'i, "0""ToO-

I PCO': :'I I 1,1 CiIMC DISfILLHTION CJLUMN

three delays were interconnected between the controller and simulator. In thisconfiguration, disturbances can be introduced by changing the tuning constants of thePID controllers. The limiting of the setpoints requires some logic as well.

It was only practical to implement the simulator in the same PM. Eventuallythe required system configuration was:

1 x User Station1 x Local Control Network1 x Network Interface Module1 x Universal Control Network1 x Process Manager, release 3001 x alarm printer

The resulting architecture does not contain a unit optimization level to provide theMIMO controller with setpoints for top and bottom quality. Instead they are entereddirectly by the operator from the US using the setpoints of the interconnected PIDs.This also holds for the simulated reflux- and reboiler valves. When the PID control-lers are in automatic mode, the operator can enter setpoint values for the PIDcontrollers during the "start-up" of the process. After this, the controllers are switchedto cascade mode (normal operation). In figure 5.3 the IDC 3000 implementationblockscheme for the IPCOS IMC is shown. In figure 5.4 the resulting loop diagramfor the PM 300 is depicted.The Sequential Process Control structure of the controller in a PMDP is given infigure 5.5. In "Step Proc_mod" the state and output vectors of the process model areupdated, in "Step Dist_mod" the state and output vectors of the disturbance modelare updated. This updating requires matrix multiplication for which no standard built-in algorithm is available right now in the PM (in the AM it is). In this case all matrix-multiplications are written out in full CL which makes this implementation veryspecific. In figure 5.6 the operator's view on the controlled process is shown.

5.3.2 Results

In table 5.1 the available and required performance for the implementation of theMIMO controller in the (A)PM 300 is given.

42

Table 5.1 Available and required performance of the (A)PM 300 for implementationof the MIMO controller

# Memory Units # Processing units # VariablesSystem capacity (available)

APM300 10000 1600 2048+80*nPM 300 3200 1600 2048 +8O*nControl Scheme (required)

-Basic Control 70 70 at 1 s scan-MIMO Control 100 Max. 150 at 10 s scan 300

Aver. 15 at 10 s scan-SP limits processing(estimation) 35 Max. 20 at 10 s scan 25

Aver. 2 at 10 s scanTotal amountrequired 205 for APM 2.1 % Max. 240 15% 325 < 16 %

205 for PM 6.5 % Aver. 90 5.6 %

The required amount of CPU is expressed in so called processing units. The scanningfrequency of the MIMO controller can be specified and is typically 10 times slowerthan the scanning of the connected primary controllers. The memory size is denotedby the number of Memory Units. The number of Variables depends on the number ofProcess Module Data Points (n). Each configured PMDP has 80 local variables.

5.3.3 Conclusions

In table 5.1 the most important constraints are the number of variables and themaximum number of processing units required. These values determine the numberof control loops that can be implemented in the (A)PM. As to this number there isno difference between APM and PM. In this implementation 6 MIMO loops includingadditional requirements could be implemented but in practice one per (A)PM woulddo.

The number of variables can be decreased and robustness can be improved bydeclaring the parameters that do not change (contents of State Space matrices, scalingfactors) to be constants. This would mean a variable reduction of at least 70 %.Drawbacks of this operation would be that constants could not be changed andvisualized during runtime, the use of generic subroutines is impossible (manipulationsindex impossible) and declaration in both controller and simulator is necessary. Theoperation would make it possible to implement 8 MIMO controllers in one (A)PM.Because only one process-unit MIMO controller is required in the APM, its complex-ity can be increased and a constraint handler could be added.

If the PID controller is out of cascade mode, the MIMO controller sequence"holds" while the process simulator continues. Of course, this causes a large errorbetween MIMO controller output and process simulator output and consequently,when switching back in cascade mode, it takes a long time to get back the processsimulator PVs according the SP values.

Mter all we may conclude that the (A)PM allows MIMO controller implemen-tation without any problems as far as (A)PM performance is concerned (takes only a

43

few percents of total). The MIMO controller perfectly copes with the primarycontrollers in the (A)PM. In particular as far as bumpless transfer is concerned afterswitching back to cascade (MIMO) control. The operator need not worry and canswitch to cascade without disturbing the process. The MIMO operator interfacebehaves like a PID operator interface and needs the same approach. Once the MIMOcontroller design is available it is only a matter of man-weeks to get it implementedin (A)PM, tested and documented. If an existing plant has been equipped with a DCSlike IDC 3000 then there is actually no problem to move from primary control toMIMO control. The only real task is to get the right MIMO controller designed.Companies like Setpoint IPCOS can provide this kind of service. No particularmanagerial problems (planning, financial, responsibility of human resources) wereencountered during the MIMO pilot project.

44

6 Software and Hardware

6.1 Developments in Software and Hardware

The level of technology is determined by the level of hardware and software separate-ly and their integration. The technology is up-to-date if the software uses the capabil-ities of the up-to-date hardware to the full extent.

There are four criteria for technology [7]:

1 performance2 pnce3 reliability4 programmability and maintenance

The first three are hardware criteria, the fourth is a software criterion.Nowadays the price of the hardware has become a minor issue. The price of a

process automation project is mainly determined by the hours that the highly skilledproject team spent.

Reliability is guaranteed by the use of high quality, proven elements. Someelements are duplicated or even triplicated and as ultimate rescue, there is anemergency handling system.

In section 6.1.1 performance will be dealt with more explicitly.

6.1.1 Performance

The performance of a hardware configuration (CPU, ALU, memory, bus) depends onits accuracy, memory, speed and database access.

The accuracy is determined by the number of bits in the "currency" of theCentral Processing Unit and bus. This currency is called "word". Typical lengths: 8 bits(= 1 byte), 16 bits or 24 bits.

The memory is characterized by its size expressed in the number of words thatcan be contained.

The speed of the CPU depends on its clock, the number of bits in a word andthe way operations are implemented.

The frequency of the clock determines the duration of one cycle. All arithmeticoperations executed by the CPU take a number of cycles. Typical values; logicoperation: 1..2 cycles; addition/subtraction: 2 cycles; multiplication: 4..8 cycles (meanvalue: 5); Division: 8..16 cycles (mean value: 10). The number of cycles that anoperation takes is determined by the way the processor is programmed.

The speed of processors has increased over time through the availability ofhardware allowing higher clock frequencies, smarter programming and increasedlengths of words. A cascade architecture of more than 1 servers, one (coprocessor) ofthem receiving the overflow of others, further increases speed. In table 6.1 the INTEL

45

family is given with clock speeds.

Table 6.1 CPU clock frequencies [12]

CPO (INTEL)808880286808680286803868048680586

Clock Frequencies (MHz)4.776/881016/2025 / 33 / 50 / 6633 / 50 / 66 / 100

Whilst the individual processors are being optimized, the speed of the overall system(CPU + ALU + memory + I/O) is determined by a bottleneck like the data-baseaccess. Physical constraints are envisaged that makes research looking for lessconstrained alternatives.

6.1.2 Programming Languages

The software part of a specific application consists of algorithms that are imple-mented in a specific programming language. Such an implementation is called aprogram. Apart from the quality of the algorithm the language [12] is an importantfactor in the quality of the program.

In hardware newly developed generations of computers replace old ones. Incomputer languages, developments are being divided in generations as well but thereis a difference. As for the Hierarchical Control Structure, newly developed levels orgenerations are added to existing ones, making a higher level of abstraction possible.Programming languages can be divided into 5 generations:

The first generation is machine language. Instructions are series of bits that areentered by the programmer.

The second generation is assembler language. The programmer enters mne-monic abbreviations of the instructions. There is still a 1 to 1 relationship betweenthe program and the instructions for the computer. The assembler translates thesource code to the object code (machine language). A program in assembler languageis performed fast, efficiently using the processor. Assembler languages are dedicatedto a specific processor using its strengths. A higher language (> 2rd generation) cannever reach this.

Procedural languages belong to the third generation. Each command meansseveral instructions for the computer. Procedural reflects on the fact that the programmust contain data and procedures to provide action in all states of the application. Acompiler translates the program to the object code which is saved and executed. If thetranslation and execution are performed for each line separately, it is called aninterpreter. Procedural languages offer machine independency. Different languageswere developed for different applications. COBOL for business and administration,

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FORTRAN and MATLAB for science, Turbo Pascal for education, BASIC for PCenvironments, C for packages on PCs being powerful, flexible and fast.

Languages of the fourth generation want the user to specify what has to bedone rather than how in order to decrease the step between problem definition andprogram. Examples for PC environments: Oracle, dBASE.

The fifth generation programming languages have powerful commands and datastructures that can contain knowledge in order to solve problems. Usp and Prolog arelanguages in which knowledge can be structured in different ways. In Expert-System-Shells the structures in which the knowledge must be captured together with themethods to reason are fixed.

6.2 Hardware and Software requirements for the Hierarchical Control System

In this section hardware and software requirements will be treated for the levels ofthe Hierarchical Control System of figure 3.2.

If the controller in the primary process control level is a SISO PID then outputprocessing (given the noninteractive scheme, equation A, section 4.3.1 and the meanvalues for operation cycles in section 6.1.1) based on mean values costs 41 cycles.Choose one cycle to take 1 micro s (equals 1 MHz clock frequency) then approxi-mately 250 PVs can be sampled 100 times per second. Because a normal value for therequired sample frequency in chemical processes is 1 Hz, one should think that thereis a large overhead of speed which can be used by scanning a lot of PVs. However,the collection of PVs (AD/DA) is not the only function of the processors of the(A)PM: signal pre-processing, alarm functions, PV checking, ranging, etc. To guaran-tee the necessary level of reliability, at least 50% of the CPU capacity must be usedfor diagnostics. As was shown in figure 4.2, the unit process control functions(sequential process control, see chapter 5) are performed by the same (A)PM CPUsas well. Conclusion: the available CPU capacity is the bottle neck. The number ofvariables or local memory at disposal suffices in (A)PM.

The CPU capacity can be increased through more economic use of theapplicated ones (software problem) or by replacing them by more recent andadvanced CPUs. In the latter case a lot of software has to be rewritten.

The identification and controller design part of the HMPC package is only partiallyLeN resident (figure 4.8). The moment that an on-line identification (optimization ofmodel parameters of which the structure was determined off-line) and tuning ofcontroller parameters can be performed within the control system (all parts are LCNavailable) and the whole package can be engineered quickly, demands a minimum ofexpertise, can be applicable to a range of processes and is fully supported by itssupplier, then MIMO control will have become an accepted block-structured control-ler within TOC 3000 like the PID controller nowadays. The technology to incorporatethe package is already available yet not in IDC 3000.

The algorithms within the HMPC package were based on technology from thelate seventies / early eighties that has been improved by now. Now it is possible toproduce more compact models, decouple the control and optimization problem, andallow for robust control. Conclusion: the software can be improved as well.

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The HMPC documentation described that the non-parametric description ofthe process was saved in the AM. However, it did not treat where the controlleractions were calculated. Here it is assumed that the HMPC controller fully resides inthe AM.

For the levels unit process optimization upto production optimization, still a lot ofsoftware has to be written. For the calculations in these levels samples are taken of atleast one second. The hardware and availability criteria of these levels are not asstringent as for the primary process control level. In these levels VAX computers andPCs are typically used. Hardware and software breakthroughs can be applicatedrelatively quickly. The quality of these levels is determined by software rather thanhardware. Appropriate algorithms are developed "bottom up" by process controlscientists and "top down" by management scientists.

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7 Recent and future topics in Research

7.1 The Fundamental Control Problem [8]

As we have seen before a DCS with split architecture automates the four technologiesthat are integrated in the decision making process: (i) measurement (ii) control (SISOand MIMO layer) (iii) optimization (iv) logistics (scheduling and allocation of rawmaterials). Such a control system extracts the most profit out of existing processes (noredesign needed) while responding to market changes.

For still a lot of processes all over the world integrated control systems have tobe designed (15.000 to 20.000). Since each petro-chemical process is unique, costscannot be lowered by manufacturing at large scale. So design and maintenance costsmust be minimized. This requires a unified approach to every specific controlproblem which starts from setting the specific problem into the framework of astandard problem formulation: The Fundamental Control Problem. In this section adesign procedure to solve the FCP will be treated. The value of the FCP lies in thefact that it limits the solution space to the solution(s) of the real control issue. Thesteps in the off- or on-line design procedure will make clear in what directions futureresearch should take place.

The advantages of a unified approach to control is that design and mainten-ance costs are decreased and that the focus of research is redirected from theapplication of intuitive process models (training and experience) and the use of ad-hoc solutions by non-expert designers to the fundamentals of the control problemitself.

There are two major disadvantages to the unified approach: (i) The unifiedapproach forces one to model a process completely which is of course much moredifficult than modelling parts of the process driven by the conscientiously built uplibrary with standard solutions for standard problems. (ii) The treated unifiedapproach does not centre the real issue. It implicitly states that the quality of goodcontrol is something that a process or model admits whilst the key issue that mainlybothers academics is what control objective a certain model together with its uncer-tainty description admit.Before the design procedure will be treated the Fundamental Control Problem isstated: On-line update the manipulated variables to satisfy multiple changingperformance criteria based on a process representation which includes a descriptionof the uncertainties. The practical performance criteria are:

economic:

safety andenvironmental:

equipment:product quality:

maintain PVs at targets dictated by the optimization phaseor dynamic minimal cost function

some PVs must not violate specific bounds for personal orequipment safety or environmental regulationsphysical limitations of equipmentconsumer's specifications

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humanpreferences: operator will not tolerate certain jaggedness or oscilla-

tions, preferred modes of operation

These criteria have to be translated to mathematical expressions, to be divided in:

objectives: functions of variables to be optimizedconstraints: functions of variables to be kept within bounds, two types:

hard constraints: no violations allowedsoft constraints: violations allowed temporarily for the satisfaction

of other criteria

The translation to objectives and constraints involves compromises and assumptions.Artificial Intelligence facilitates the formulation of the practical criteria and thetranslation to mathematical expressions for non-expert designers.

Mathematical models for petro-chemical processes are hard to formulate andoften contain uncertainty. Since uncertainties impose restrictions on the satisfaction ofthe performance criteria, apart from the process model an explicit uncertaintydescription must be available to perform the control system design.

In figure 7.1 the Fundamental Control Problem is given.

Objectives IControl the Top Compo- min (Yi* - yjF, i = 1,2, '" nobjsit ion at Its Target m

Inequality Constraints IYilow ,; Yi ,; Yihigh

The Reactor Temp. Must mjlD~mi s mjhighNot Exceed High Limit ILimj I .. max move

Equality Constraints IThe Air to the Regen- Yi = Yi

, erator Must Be on mj =mj* IITarget at All Times

Performance

Criteria

Process

Representation

Model Equations Im

The Overhead Temp. Yi (k) = L: aijl' Lim; (k-f)Drops Fast When I' =, + di (k)Steam is Reduced

>Uncertainty Desuiption I

a;;, ' a;j' ,a;j,;" ~IFeed Composition CanChange as Much as 20% Iddk) I ,; djin a Couple of Hours

AssumptionsQualitative --- --- QuantitativeCompromise

Figure Zl The Fundamental Control Problem [8]

50

Objectives I Objectives I Single Lumped Multiobjeetive Quadratic Quadratic Setpoint Tracking Setpoint Tracking.

~ Input Penalties ~

Objectives I Lumped Multiobjective Quadratic Setpoint Tracking.

Input Penalties ~

Objectives I Lumped Multiobjective Quadratic Setpoint Tracking.

Input Penalties i""':":""""I FiJCed

Objectives I Lumped Multiobjective Quadratic Setpoint Tracking,lnput

~~~~~~~i~t~oft r:C:;"""ha'--n-oii:-no-l

Changing

Inequality Constraints

Equality Constraints

Linear MIMO

linear MIMO

,.-------------.., r-------...,IEquality ConstraintsIIIII

~ ~ '-- LIC:.;.;h::.;.an;;;l.qi'c.;.nq....,

~I~;q~;ji~y-C~;;str~i~t~ - ~,,,IIIl.. -'

~E~~;ji~y-C~;;str~i~t~- - ~ ~E-q~;iity-C~;;s~r~i~t~ - - ~ ~E-q~;ii~y-C~;;str~i~t~ - - ~I , , II, I , III I , III II III I , II, II ".. ..J l.. ..... ...

... ~ '- "" L. ..J

r-------------, r-------------.., r-------------.,I Inequality Constraints I 'Inequality Constraints I 'Inequality Constraints, , I , ,I , , , II I I I', I I II, I I I II , I II

Model Equations I 2nd Order Linear.5150

Model Equations I Model Eq uations I Linear linear MIMO MIMO

~ IChanQinq

Model Equations I Linear MIMO

Model Equations I linear MIMO

r-------------, r-------------""\ r-------------.,IUncertainty Description I ,uncertainty Description I 'Uncertainty Description II I I II I,. Implicit It. Implicit I I- Implicit II. On-line Oetuning I I_ On-Line Oet:uninq I Ie On-Line Oetuning II , I 11 I, , I II I, I I II IL. ..J l.. ..1 ..J

ssv

Uncertainty Description

Explicit.. Structured Parameten and

Oi1turbances

~Un~;'~l;.tY-D-;~riitk>~~, ,,. Implicit II. On-Line Oetuning II ,I I~I I~ ~~~l.. -,

STR5150 P)[)Figwe Z2 Control Methodologies in light of FCP [8J

In figure 7.2, 5 control methodologies are reviewed in the light of the FCP. Theelements that are not dealt with at all or implicitly only (comprising the elements inboxes that are not dashed) are shown in dashed boxes.

In figure 7.2 (a) the model uncertainty can be dealt with by on-line detuningmaking the setpoint tracking worse.

Structured Singular Value Synthesis (SSV) is a H inf technique. QDMC is amodel based multivariable control technique that minimizes a quadratic criterion online. On-line modification of all elements of the control structure is possible andconstraints can be added. QDMC belongs to the class of Model Predictive Control-lers.

In 1988 the MPC was improved to include an uncertainty description whichprovides a controller that deals with all elements of the FCP explicitly.

Now the complete design procedure will be described under the assumption thatthere is enough computing capability and logical processing available to perform itoff-line which is by no means restrictive in the light of advances in the area of SuperComputing.

1 Provide the most accurate model and uncertainty description available for theprocess. Requires a model building facility that combines white and black boxmodelling techniques. This will demand advanced graphics facilities andArtificial Intelligence technology superimposed on chemical engineeringtechnology.

2 Formulate the pratical performance criteria. Requires an advanced graphics

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terminal interfacing an Expert System that contains Artificial Intelligence. TheAI links the graphics to practical criteria.

3 Translate the practical criteria to objectives and constraints (mathematicalcriteria). Performed by an Expert System.

4 Solve the Fundamental Control Problem. If this optimization problem has nosolution then go back to Step 1 or 2 (or 3) to improve the process representa-tion or relax the criteria. If the problem has a solution there is still no guaran-tee that it can function on-line. If the hardware facilitates, the design iscomplete. If not then proceed with Step 5.

5 Specify the controller equations (= expressions for the MVs). Pick a controlscheme that fits the available hardware. The equations that govern the chosencontrol system are added as constraints to the FCP.

6 Solve the FCP with the controller equations substituted for the MVs. Now thetuning parameters in the controller equations have become the independentvariables. If there is a solution the design is complete. If no solution then relaxcriteria (Step 2 or 3) or choose different controller (Step 5).

In section 7.2 specific research for the solution of the FCP will be treated. In section7.3 the place of the FCP in the design of Hierarchical Control Systems will beoutlined and in section 7.4 specific research for HCS will be described.

7.2 Areas of research for the solution of the Fundamental Control Problem [8]

Time domain optimization under uncertainties: The presence of time-domain inequalityconstraints in the FCP forces the optimization to be performed in the time domain.Techniques must be found that solve the optimal control problem in the face ofuncertainties in the time domain. (See figure 7.2: QDMC deals with model uncer-tainties implicitly and SSV does not deal with constraints). Existing techniques (11-optimal controller, newly developed) do not perform well. This, together with the factthat models are not accurate enough makes the constraint handling still takes place innonlinear logic elements with exactly specified priorities.

Multi-objective optimization techniques: All control methodologies of figure 7.2 handlethe satisfaction of multiple objectives by w