Teaching Process Dynamics and Control Using An

Industrial-Scale Real-time Computing Environment

D. E. Rivera∗, K. S. Jun, V. E. Sater and M. K. Shetty

Department of Chemical, Bio and Materials Engineering

and

Control Systems Engineering Laboratory

Computer-Integrated Manufacturing Systems Research Center

Arizona State University, Tempe, Arizona 85287-6006

To Appear in Computer Applications in Engineering Education

Special Issue on Computer-Aided Chemical Engineering Education

Abstract

This paper describes how an industrial-scale real-time computing platform, namely the

Honeywell TDC3000, has been incorporated into the undergraduate process control course

at Arizona State University. The mixing tank dynamics and gas oil furnace control exper-

iments described in this paper are among eleven laboratory exercises developed for this

course which provide students with unique insights on life cycle issues in control, real-time

system architecture and information needs, and the benefits of “off-line” computer-aided

design tools.

Keywords: process control education, distributed control systems, system identifica-

tion, computer-aided control system design

∗to whom all correspondence should be addressed

1

Introduction

The Control Systems Engineering Laboratory at Arizona State University began in Septem-

ber, 1990 as the result of a major collaboration between Honeywell Industrial Automation

and Control, Digital Equipment Corp., and Arizona State University. Honeywell IAC do-

nated a $1.2 million, state-of-the-art TDC 3000 Plant Information and Control System,

with DEC providing on consignment a Local Area Network of VAX 3500 and DECStation

5000 workstations. The result is a unique real-time computing environment which allows

students in the laboratory to stage a wide variety of industrially-meaningful scenarios

spanning the areas of batch and continuous process control.

The goal of CSEL over the past five years has been to take advantage of these out-

standing facilities to develop a visible research program as well as augment the educational

experience of our undergraduates. Since the inauguration of the laboratory, eleven out of

the twelve experiments in CHE 461 have been developed or modified to run on the plat-

form. Dynamic modeling and control of both pilot-scale equipment (a shell-and-tube heat

exchanger, a salt-water mixing tank, and a pH reactor) and simulated plants (a two-

component mixing reactor and a heavy gasoil furnace) are featured in these experiments.

In addition to providing an excellent environment for implementing classical control con-

cepts, these labs expose students to issues in control system design and implementation

which are rarely seen in an undergraduate program. Specifically, these labs provide stu-

dents with an understanding of life cycle issues in control, an awareness of computer system

architecture and information integration needs, and an appreciation for the usefulness of

computer-aided design and analysis tools.

In this paper, we describe how the TDC3000 and other computing systems in our

laboratory have been integrated into ASU’s undergraduate control course by profiling two

specific experiments: a pilot-scale salt-water mixing tank (Lab E: Mixing Tank Dynamics

and Sensor Calibration) and a simulated heavy gas oil furnace (Labs D, J1, and J2: Furnace

System Identification and Control). While the Department of Chemical Engineering at

Arizona State University is not unique in having access to a commercial distributed control

system such as the TDC3000, we are unaware of any program in the United States that

2

has utilized a DCS computing environment to the extent that we have accomplished at

ASU. We hope this paper will encourage additional interactions of this nature between

industry and academia.

Overview and Objectives of CHE 461

Process Dynamics and Control (CHE 461) is a required senior-level course in the chemical

engineering program at ASU. This three-credit course is offered in the fall semester and

consists of two hours of lecture and a 3-hour lab session per week. The textbooks authored

by Seborg et al. [1] and Ogunnaike and Ray [2] are used. As an introductory process

control course, the bulk of the material covered in CHE 461 represents traditional aspects

of control engineering, such as dynamic modeling, Laplace transforms, transfer functions,

properties of closed-loop systems, controller types and implementation, and frequency

response analysis. An additional number of the topics covered in this course form part

of the process control curriculum for the year 2000, as noted by Edgar [3]. Among these

topics are:

System Identification. Strong emphasis is given to dynamic modeling from plant data (i.e.,

system identification) since this approach is the most commonly used in industrial

practice. We go beyond the graphically-based step testing/process reaction curve

techniques to introduce students to the use of regression-based (i.e., prediction-error)

methods for system identification [4], and the considerations involved in practice to

obtain informative models from data.

Systematic Model Based Control Design. Most introductory texts focus on the use of sim-

ple tuning rules for Proportional-Integral-Derivative (PID) controllers (e.g., Ziegler-

Nichols, Cohen-Coon) as the primary means for teaching controller design, with little

explanation regarding how the rules were derived or their underlying significance. In

CHE 461 we rely instead on a more systematic approach to introducing students to

control design based on the concept of Internal Model Control (IMC) [5]. Students

are presented with a detailed assessment of control performance requirements, and

3

shown that control performance is limited by the presence of nonminimum phase

behavior in the plant (time delay and Right-Half-Plane zeros) and the requirement

that the control law be stable and realizable. The Internal Model Control design

procedure directly addresses these performance requirements, and depending on the

sophistication of the model, the IMC design procedure leads naturally to PID, PID

with filter, and Smith predictor controllers with corresponding tuning rules. The

students then compare these IMC-generated rules with classical tuning rules, and

are able to assess the benefits of the IMC-based approach on both practical and

theoretical grounds.

The application of IMC to PID controller design is based on the work of Rivera et al.

[6], which has found widespread acceptance in the process industries. While aspects

of this work are discussed in the texts by Seborg et al. [1] and Ogunnaike and Ray

[2], additional notes are provided to the students by the instructor [7].

The laboratory segment of CHE 461 is intended to provide students with a “hands-

on,” holistic experience which gives life to the somewhat dry, theoretical concepts taught

in lecture. Main features of the laboratory are described below:

Real-time Control Implementation. It takes more than good control theory to implement

a working, practical control system. Through the use of the TDC3000 platform,

students quickly find out that real-time implementation of a PID controller goes much

beyond writing simple time-domain approximations to Laplace transform equations.

Reading and sending analog signals from the process, real-time schematics, effective

visualization of alarms and trends, building software modules (i.e., point building)

and custom control algorithms, are all important facets of computing in a real-time

environment. Through the laboratory students are introduced to the conceptual

design, “build and program,” commissioning and maintenance phases which define

the control system lifecycle. Properly addressing lifecycle considerations, as noted

by Dupont’s Hanley [8], makes the difference in practice between an effective control

system and one where degraded performance is the norm, only a short time after the

initial commissioning.

4

Computer-aided control system design. Students have access to MATLAB with SIMULINK

[9] on assorted personal computers and workstations located in the undergraduate

laboratory and throughout campus. MATLAB enables the students to perform re-

gression analysis in support of system identification and sensor calibration tasks,

compute controller parameters from model-based tuning rules, and carry out simula-

tion of closed-loop responses prior to controller implementation. Simulation tasks are

accomplished using SIMULINK, MATLAB’s graphical interface. Students quickly

learn that effective use of MATLAB/SIMULINK promotes a “quality” work envi-

roment and error free-work. Another meaningful educational experience resulting

from simulation is the need to explain discrepancies between simulated results and

the values observed from the TDC3000 system.

Overview of the Laboratory

We briefly describe the components of the distributed control system in our laboratory. The

TDC3000 is a distributed computing system with various nodes linked to two networks:

a Local Control Network (LCN) and at least one process network. Honeywell offers two

types of process networks, the Data Hiway and the Universal Control Network (UCN). The

system at ASU is UCN-based. Each of the process networks has an interface (gateway) that

allows it to communicate with the LCN. The Universal Control Network interface is called

the Network Interface Module (NIM). The Universal Control Network transmits process

data from process connected devices such as controllers and data acquisition devices (for

measuring temperature, flow, level etc.) through their gateways to the LCN. Visualization

of the entire plant is done by Universal Stations (US), which reside on the LCN. The

Universal Station represents the primary TDC3000 human/machine interface, and provides

a window to information on the entire system, whether it is resident in one of the LCN

modules or in one of the process-connected (UCN) devices.

Figure 1 shows the key features of a TDC3000 system present at ASU. The system

features a single UCN consisting of 3 Process Managers (PM). The Process Manager (PM)

provides a complete range of data acquisition and control capabilities, including digital

5

inputs and outputs, analog inputs and outputs, and basic control for up to 160 regulatory

loops. The PM has a limited amount of programming capability which is particularly

geared towards batch sequencing. Our Local Control Network is composed of one Network

Interface Module, six Universal Stations, one History Module (HM), one Application Mod-

ule (AM), and a Computer Gateway (CG). The Computer Gateway links to a VAXserver

3500, which can be used for computer-aided design functions or to implement large, real-

time control applications. Mass storage of data on hard disk media is provided by the

History Module. The Application Module (AM) permits the implementation of more

complex control calculations and strategies than are possible when using only the PM.

Specifically, the Application Module allows replacing the set of standard PID-type control

algorithms used in the PM with custom algorithms that are written using Honeywell’s

Control Language. As will be noted later in this paper, it is through the Control Language

insertion feature on the AM that students are able to implement IMC-based controllers as

custom PID control algorithms.

Description of the Mixing Tank Dynamics and Sensor

Calibration Experiment

The objective of this experiment is to determine, via first principles modeling and sys-

tem identification techniques, the transfer function relating inlet brine flowrate changes

to changes in the outlet salt concentration of a continuous stirred mixing process. The

experiment requires the student team to generate a suitable calibration between the signal

generated from an on-line conductivity sensor and the salt concentration (in g/ℓ) for the

outlet stream in the tank.

Figure 2 shows both the process and the instrumentation used in this experiment. The

flow of tap water to the process is regulated by measuring the flow with an orifice meter

and changing the valve position on the water line according to an algorithm in a regulatory

control point in the PM. This control loop is assigned the tagname FIC100. Similarly, the

flow of a concentrated salt solution is controlled with loop FIC101. The level in the tank is

measured with a differential pressure cell (d/p) with one leg connected to the bottom of the

6

tank and the other leg open to the atmosphere. The regulatory control point LIC100 com-

pares this level with a desired level and manipulates the flow through the drain line. The

salt concentration leaving and entering the tank is measured with conductivity cells and

read into the system via the PM Analog Input points CI100 and CI102, respectively. The

conductivity measurements are displayed as the PVs (Process Values) of CI100 and CI102.

By setting the appropriate instrument range limit parameters in the system (PVEUHI

and PVEULO) the students are able to implement a linear correlation relating the raw

4-20ma signal from the conductivity cells to a sensible value for concentration in units of

g/ℓ. CIC100 is a regulatory control point used in a subsequent experiment (Lab H: Mix-

ing Tank Control) which adjusts the salt inlet flowrate setpoint (FIC101.SP) to keep exit

stream salt concentration at setpoint (CI100.PV); students are asked to leave this point

on MANUAL throughout the Lab E experiment.

There are two main tasks that must be conducted simultaneously during the initial

part of the experiment. One is to create a series of standard solutions that will build

a correlation between concentration (in g/ℓ) and conductivity (in millimhos). The other

task is to run the tank at various steady-state conditions to establish a relationship between

% of scale in the raw signal of CI100 and conductivity, which in turn defines concentration.

CI102, the inlet stream salt concentration, has been calibrated by the instructor prior to

the experiment. Students set all loops to AUTO (with the exception of CIC100) using as

setpoints a fresh water flowrate of ≈ 1.5 gpm, a salt water flowrate of ≈ 0.07 gpm, and tank

level of 30%. The raw signal for CIC100.PV is trended until the steady-state is reached. A

sample solution is then obtained from the mixing tank, and the conductivity is measured

using a bench-scale conductivity sensor. This procedure is repeated using inlet salt flowrate

setpoints of 0.15 and 0.2 gpm. While the initial mixing tank experiments are underway,

the students prepare a series of standard solutions to relate salt concentration (g/ℓ) to

conductivity (millimhos). The conductivity of standard salt solutions corresponding to 1,

2, 4 and 6 g/ℓ are measured using a bench-scale conductivity sensor.

Students rely on MATLAB to estimate the parameters of a calibration curve between

conductivity and salt concentration. The polyfit command is used to obtain a linear

7

relationship to the measured data set. At the MATLAB prompt, students enter the ex-

perimental data and type the following commands,

>>x = [20 37 71 100];% Conductivity [millimho]>>y = [1 2 4 6]; % Salt concentration [g/l]>>p = polyfit(x,y,1);>>ye = polyval(p,x);>>plot(x,y,’o’,x,ye,’-’);>>gtext(’y [g/l] = p(1) x [millimho] + p(2)’);>>xlabel(’Conductivity [millimho]’); ylabel(’Concentration [g/l]’)

to obtain the parameters estimated from linear regression. Graphical inspection of the

curve generated from polyval is used to validate the goodness-of-fit; Figure 3 (top) shows

an example of the calibration curve. The calibration equation between the conductivity and

salt concentration can now be used to generate data points that define the final calibration

curve between salt concentration (in g/ℓ) and % of scale in the CI100 point. Using the

three conductivity measurements from the tank samples and the calibration curve between

conductivity and concentration, the students apply polyfit in MATLAB to obtain a final

calibration curve for the system. An example curve for the CI100 point is shown in Figure 3

(bottom). From this curve, they determine the concentration value for 0% of scale; this

value is the low limit on the instrument range, or PVEULO. The value for 100% of scale,

meanwhile, is the high range on the instrument, or PVEUHI. The students then proceed to

the DETAIL display for both the CI100 and CIC100 points, and enter these parameters.

Having completed the calibration portion of the lab, the students proceed with system

identification and comparison to first-principles modeling.

System identification in the mixing tank consists of generating process reaction curves

by introducing step changes in the setpoint of the inlet salt stream controller. Following

calibration, the tank should be at steady-state with the fresh water flowrate at ≈ 1.5 gpm,

a salt water flowrate of ≈ 0.2 gpm, and tank level of 30%. Students adjust the setpoint

of the brine stream flow controller FIC101 from 0.2 to 0.1 gpm; trending and plotting the

resulting change in salt concentration (CIC100), as noted in Figure 4, is then performed.

Once steady-state is reached, the tank level is adjusted via LIC100 to a higher value (50%

full). Then, the students wait until steady-state, and adjust the set point of the salt water

flowrate back to 0.2 gpm. Graphical (inflection point) analysis is used to estimate the gain,

8

time constant and delay for this system at each volume level . While performing step tests,

members of team measure the tank level and geometry to estimate the true tank volume

for subsequent use in generating a first-principles model.

Whenever the tank reaches steady-state, students are asked to use process values dis-

played by the TDC3000 to generate gain and time constant values from the first-principles

model as an alternative to graphical analysis. The derivation of the first-principles model

is briefly explained. Assuming the fresh water flow rate (qw) and level are maintained

constant, and that the densities of all streams are constant and similar, a component mass

balance results in the following differential equation

Vdc

dt= qccc − (qc + qw)c (1)

V represents the volume in the tank, cc is the inlet brine stream concentration, qc is the

inlet brine flowrate, while c represents the salt concentration in the outlet stream. Using

linearization, the students are able to obtain the transfer function describing changes in

qc to changes in c; the resulting Laplace-domain transfer function (in terms of deviation

variables) is a first-order system of the form

∆c

∆qc=

( cc−cqc+qw

)

( Vqc+qw

)s + 1(2)

with gain K and time constant τ

K = (cc − c

qc + qw) τ =

V

qc + qw(3)

c and qc are the nominal outlet salt concentration and inlet salt flowrate, respectively.

Students need to report on the values found for gain, deadtime and time constant for

the different experimental runs. They are asked to compare their results (in terms of both

model structure and the parameter values) predicted by the first-principles model versus

the experimentally-obtained transfer functions given by graphical analysis. They must also

explain a number of sources of error in this systems, as well as describe the assumptions

made during modeling which contribute to error. Finally, they are asked to comment on

the pros and cons of the modeling techniques they have applied from the standpoint of

both an engineering scientist (i.e., a person interested in the most fundamental approach)

9

versus that of a practicing engineer (i.e., an individual whose main interest is getting the

most practical solution).

Description of the Gas-Oil Furnace Experiment

The objective of this experiment is to provide students with an appreciation for the di-

versity of tasks that must be carried out by practicing control engineers when building a

process control system. A simulated gas/oil furnace (schematic shown in Figure 5) is used

for this purpose. The objective of the control system is to maintain the outlet temperature

of a gas oil stream at setpoint by manipulating fuel gas flow to the furnace. Disturbances

enter the system via changes in the feed flowrate which can be both random and determin-

istic in nature. In addition, the temperature sensor is noisy (the magnitude of the noise

determined by the instructor). The temperature dynamics are modeled by second-order

with delay transfer functions of the form

y(s) =Kpe

−θps

τ 21ps

2 + 2τ1pζps + 1u(s) +

Kde−θds

τ 21ds

2 + 2τ1dζds + 1d′(s) + ν(s) (4)

where y is the change in the outlet temperature, u is the change in the fuel gas flow, d′ is the

change in the feed flowrate, and ν represents the sensor noise in the outlet temperature.

Theoretical issues that are explored in this experiment include identification in a noisy

environment, the difference between regression-based models vs. graphical techniques,

and the application of the IMC design procedure on highly delayed systems to obtain

tuning rules for PID and PID with filter controllers. More practical, engineering functions

carried out in this experiment include such tasks as schematic display building using the

TDC3000 picture editor, point building on the Application Module, data acquisition and

transfer to a personal computer, and use of computer-aided tools (written in MATLAB)

to perform model parameter estimation and subsequently controller design and analysis.

The tasks carried out by students on this simulated system span three laboratory

sessions. During the first session (Lab D), students perform step/pulse testing on the fuel

gas flow to determine the system dynamics. While the size of the step must be sufficiently

large to exceed the magnitude of the noise and disturbances acting on the system, care must

10

be taken to insure that significant “off-spec” product is not generated during identification

testing. The identification data is collected in the Application Module and transferred

via floppy to a personal computer, where MATLAB M-files using the functionality of the

System Identification Toolbox yield estimates for the gain, time constant, and delay time

that are optimal in a least-squares sense. These parameters in turn are compared with

those arising from graphical “inflection point” techniques on the process reaction curve

([1], [2]) a task which is complicated by the presence of noise in the data.

The tasks of controller design, analysis, and implementation are carried out in two lab

sessions (Labs J1 and J2). In the classroom, students have been shown that the IMC

design procedure applied to a first-order with delay system leads to a Smith Predictor

controller [5]. By applying a Pade approximation to the delay, however, the result is a

second-order model with Right-Half Plane Zero, for which the IMC design procedure yields

a PID with filter controller of the form

c(s) = Kc

(

1 +1

τIs+ τDs

)

1

τFs + 1(5)

with an associated tuning rule

Kc =1

K

(2τ + θ)

2(λ + θ)τI = τ + θ/2 τD =

τθ

2τ + θτF =

λθ

2(λ + θ)(6)

K is the model gain, τ is the time constant, and θ is the time delay obtained from system

identification. λ is an adjustable parameter for the control system which is directly related

to the closed-loop speed-of-response. The students then implement a digital “velocity

form” version of this PID controller as a regulatory control point in the TDC3000. For

a specific sampling time T and backward difference approximation of the derivative and

integral modes, the controller is described by a four-term difference equation:

∆uk = K1ek + K2ek−1 + K3ek−2 + Km1∆uk−1 (7)

α = 1 +τFT

K1 =Kc

α(1 +

T

τI+

τDT

)

K2 = −Kc

α(1 +

2τDT

)

11

K3 =KcτDTα

Km1 =τFTα

The implementation of the custom control law is achieved by using Control Language

insertion in an AM-based regulatory control point, a feature provided by Honeywell which

allows users to specify their own control laws. The location of the control algorithm code

relative to other functions carried out by a Regulatory Control Point is shown in Figure 6.

The block of code (and its corresponding custom data segment) used in this lab is shown in

Figure 7. The IMC tuning rule is built into the code, which greatly increases the ease-of-use

of the algorithm; on-line control adjustment is achieved by specifying a single parameter

(the desired closed-loop speed-of-response) instead of trial-and-error tuning of Kc, τI , τD,

and τF . Figure 8 illustrates the sequence of modeling and control tasks carried out by

students in this experiment. The initial task of step testing is performed by introducing

a small (±3.0%) change from the nominal value of 5.0 MSCF/hr in the fuel gas flowrate.

Students are asked to visually ascertain if the test has been sufficiently informative; if not,

a -6% change (from 5.3 MSCF/hr to 4.7 MSCF/hr) in the fuel gas flow is introduced. A

final +3% step change brings the process to its original operating condition. The result of

this series of step tests is equivalent to a “double pulse” input, as seen in Figure 8. Figure 8

also shows the closed-loop step responses obtained from the two PID controllers built by

students in this experiment. The point PID10# (PID102 in Figure 8) uses a standard

(Honeywell-provided) three-term PID controller implementation; the point PIDWF10#

(PIDWF102 in Figure 8) implements a custom PID controller with filter according to the

structure in (5) using the CL code in Figure 7. Both controllers use the IMC tuning rules

according to [6]. Because its tuning rule is built directly into the control code, the custom

point PIDWF10# has a significant advantage over PID10# from the standpoint of ease-

of-use. Students compare both controllers for a 50 ◦C setpoint change, and find that the

custom PID with filter controller not only returns the process to setpoint faster, it does so

with a smoother manipulated variable response than the standard PID controller.

Tasks carried out by students during Lab J2 include disturbance rejection of load

changes in the feed flowrate, and the use of “bumpless” PID controller forms. Because of

12

the harshness of pure derivative action on step changes, students are asked to modify the

ideal PID algorithm using Honeywell’s EQNB, written as

u(s) = Kc(1 +1

τIs)e(s) −KcτDs y(s) (8)

Equation (8) is referred to as a “bumpless” implementation of PID control because it ap-

plies derivative action to the process measurement y in lieu of the control error e. Students

are also encouraged to try bumpless action using Honeywell’s EQNC

u(s) = Kc1

τIse(s) −Kc(1 + τDs) y(s) (9)

and to try out ASUtune [10], a demonstration prototype developed in our laboratory for

research purposes which fully integrates the system identification and controller design

tasks.

Simulation plays a particularly useful role in completing the Lab J2 tasks by helping

students predict closed-loop system responses and carry out “what-if” scenarios. Figure 9

shows the SIMULINK graphical window built by students which compares the effect of IMC

tuning on PI, bumpless PID, and PID with filter controllers. The closed-loop responses

(Figure 10) show the pros and cons of a “bumpless” implementation; while bumpless action

removes the initial “kick” in the manipulated variable response for step setpoint changes, it

does not reduce the control system’s sensitivity to measurement noise. Students can elect

to eliminate this problem by either accepting an inferior controlled variable performance

resulting from a PI controller, or by relying on the increased sophistication and better

performance of the custom-built PID with filter algorithm.

Summary and Conclusions

In this paper, we have described how an industrial-scale control testbed has been effectively

used to introduce students to issues in real time process control (both fundamental and

practical) that go beyond traditional classroom and laboratory instruction in this topic.

Having used the system for six semesters, we have solid evidence that student enthusiasm

and grasp of the material is significantly enhanced as a result of working on the TDC3000

platform. While learning to use a commercial system has involved significant effort, the

results have been well worth it.

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Acknowledgments

We would like to thank Honeywell Industrial Automation and Control for making the

Control Systems Engineering Lab at Arizona State a reality. Specifically, we would like

to recognize the following Honeywell managers and engineering staff who have played a

major role in the development of the Laboratory: Rod Woods, Reed Baron, Ed Massey,

Russ Henzel, Ed Williamson, Jim McCarthy, and Jim Nichols.

References

[1] D. E. Seborg, T.F. Edgar, and D.A. Mellichamp, Process Dynamics and Control,

Wiley, New York, 1989.

[2] B. A. Ogunnaike and W.H. Ray,Process Dynamics, Modeling, and Control,

Oxford University Press, 1994.

[3] T. F. Edgar, “Process Control Education in the Year 2000: A Round Table Discus-

sion,” Chem. Eng. Education, spring 1990, pp. 72-77.

[4] L. Ljung. System Identification: Theory for the User, Prentice-Hall, New Jersey,

1987.

[5] M. Morari and E. Zafiriou, Robust Process Control, Prentice-Hall, Englewood

Cliffs, NJ, 1989.

[6] D. E. Rivera, M. Morari, and S. Skogestad, “Internal Model Control 4. PID Controller

Design,” Ind. Eng. Chem. Process Des. Dev., vol 25, 1986, p. 252.

[7] D. E. Rivera, “Internal Model Control: An Approach for Undergraduates,” CHE 461

Class Notes, Arizona State University, fall 1991, revised, 1995.

[8] Hanley, J.P., “How to Keep Control Loops in Service,” pgs. 30-32, Intech, October,

1990.

[9] “MATLAB: High Performance Numeric Computation and Visualization Software,”

The MathWorks, Inc., Natick, MA, 1984-1995.

14

[10] D.E. Rivera and S. Adusumilli, “ASUtune: A Demonstration Prototype for Integrated

Identification and PID Controller Design - User’s Manual”, in preparation.

15

List of Figures

1 ASU TDC3000 LCN/UCN system schematic (top) and representative clus-

ter of Universal Stations and VAX workstations (bottom). . . . . . . . . . . 17

2 Brine-water mixing tank schematic (top) and photograph (bottom). . . . . 18

3 Calibration curve (concentration vs. conductivity) for low concentration

salt solutions (top); calibration curve (outlet stream salt concentration [g/ℓ]

vs. PV [%]) for the CI100 analog input point (bottom). . . . . . . . . . . . 19

4 Mixing Tank Process Reaction Curves as visualized from a TDC3000 Uni-

versal Station. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

5 Gasoil furnace control real-time schematic (top), and team of motivated

undergraduates creating one (bottom). . . . . . . . . . . . . . . . . . . . . . 21

6 AM Regulatory Control Point Processing Steps, with Control Language

Insertion points. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

7 Control Language code for custom PID control algorithm, Furnace Experi-

ment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

8 Furnace System Identification and Controller Testing, as visualized from the

TDC3000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

9 MATLAB-SIMULINK graphical window for simulating IMC controller tun-

ings using PI, “bumpless” PID, and PID with filter controllers . . . . . . . 25

10 Simulated furnace closed-loop responses, from SIMULINK: solid:PID with

filter; dashed: “bumpless” PID (EQNB); dotted:setpoint; dash-dotted:PI. . 26

16

Network InterfaceModule

ProcessManager

LOCAL CONTROL NETWORK

ComputerGateway

Application Module

HistoryModule

Universal Station

UNIVERSAL CONTROL NETWORK

VAX Server 3500

Universal Station

COB CONSOLE1 . . . 6

ProcessManager

ProcessManager

ASU TDC3000 LCN/UCN SYSTEMASU TDC3000 LCN/UCN SYSTEM

Figure 1: ASU TDC3000 LCN/UCN system schematic (top) and representative cluster of

Universal Stations and VAX workstations (bottom).

17

Figure 2: Brine-water mixing tank schematic (top) and photograph (bottom).

18

10 20 30 40 50 60 70 80 90 100 1100

1

2

3

4

5

6

Conductivity [millimho]

Con

c. [g

/l]

y [g/l] = 0.0617 x [millimho] - 0.2759

0 10 20 30 40 50 60 70 80 90 100-2

0

2

4

6

8

PV [%]

Con

c. [g

/l]

y [g/l] = 0.0649 x [%] - 0.2902

Figure 3: Calibration curve (concentration vs. conductivity) for low concentration salt

solutions (top); calibration curve (outlet stream salt concentration [g/ℓ] vs. PV [%]) for

the CI100 analog input point (bottom).

19

CIC100.PV

LIC100.PV

FIC100.PV FIC101.PV

30 % level 50 % level

Figure 4: Mixing Tank Process Reaction Curves as visualized from a TDC3000 Universal

Station.

20

Figure 5: Gasoil furnace control real-time schematic (top), and team of motivated under-

graduates creating one (bottom).

21

PV PROCESSING CONTROL PROCESSING

GeneralInput

Processing

PV InputProcessing

PVCalculation

PV Filteringand RangeChecking

PV SourceSelection

PV AlarmProcessing

InitialControl

Processing

ControlInput

Processing

Target Value, or Advisory DeviationAlarm Processing

DeviationAlarm

Processing

ControlAlgorithm

Calculation

ControlOutput

Processing

AlarmDistributionProcessing

GeneralOutput

Processing

BACKGRND

CTL_ALG

PST_GO

PRE_CTPR

PRE_SP

PRE_CTAG

PST_CTAG

PST_CTPRPST_PVPR

PRE_PVA

PST_PVFL

PST_PVAG

PRE_PVAG

PRE_PVPR

PRE_GI

PV_ALG

CL BLOCKINSERTION POINTS

PST_CTAG, etc., are insertion point names.

Figure 6: AM Regulatory Control Point Processing Steps, with Control Language Insertion

points.

22

--===========================================================

CUSTOM

PARAMETER KP "Gain - Process"

PARAMETER TAUP "Lag Time constant - Process; Units = Seconds "

PARAMETER THETAP "Dead time - Process; Units = Seconds "

PARAMETER STP "Sample time - Process; Units = Seconds "

PARAMETER RESET_C:LOGICAL "set ON to restart(initialize) controller "

PARAMETER TRACK_PV:LOGICAL " set ON to have PV tracking "

PARAMETER TAUCL "Adjustable parameter:Closed loop time constant"

PARAMETER KC "PID w/filter Gain"

PARAMETER TI "PID w/filter Integral time"

PARAMETER TD "PID w/filter Derivative time"

PARAMETER TAUF "PID w/filter Filter time constant"

PARAMETER ER "Current error:e(k)"

PARAMETER MOVE "Move :m(k)-m(k-1)"

END CUSTOM

--=================================================================

BLOCK PADEPRG (GENERIC $REG_CTL; AT CTL_ALG)

LOCAL ALPHA,ER1,ER2,MOVE1,KERR,KERR1,KERR2,KMOV1

-- the SP tracks the PV when the point is in MANUAL MODE

IF (TRACK_PV = ON AND MODE=MAN) THEN SET SP=PV

IF RESET_C = ON THEN(

&-- initialize controller coefficents,errors and moves

& SET ER,ER1,ER2,MOVE,MOVE1,KERR,KERR1,KERR2,KMOV1 = 0.0;

& SET RESET_C = OFF)

--

SET ER = SP - PV

-- Calculate controller tuning parameters

SET KC = (2*TAUP+THETAP)/(KP*2*(TAUCL+THETAP))

SET TI = TAUP + THETAP/2

SET TD = TAUP*THETAP/(2*TAUP+THETAP)

SET TAUF = TAUCL*THETAP/(2*(TAUCL+THETAP))

-- calculate controller coefficients

SET ALPHA = 1 + TAUF/STP

SET KERR = KC/ALPHA*(1 + STP/TI + TD/STP)

SET KERR1 = -KC/ALPHA*(1 + 2*TD/STP)

SET KERR2 = KC*TD/(ALPHA*STP)

SET KMOV1 = TAUF/(STP*ALPHA)

-- calculate current move

SET MOVE = KERR*ER + KERR1*ER1 + KERR2*ER2 + KMOV1*MOVE1

-- store previous errors and moves

SET ER2 = ER1

SET ER1 = ER

SET MOVE1 = MOVE

--calculate current output

SET CV = OPEU + MOVE

END PADEPRG

Figure 7: Control Language code for custom PID control algorithm, Furnace Experiment.

23

Step Testing

PID102

PIDWF102

Flow

Temperature

Flow

Temperature

Figure 8: Furnace System Identification and Controller Testing, as visualized from the

TDC3000.

24

-K-

Kc2

-K-

1/taui2

+++

Sum12

1tF3.s+1

Filter2

y1To Workspace10

+

+Sum

-K-

1/taui

1/s

Integrator

-K-

Kc1

0.

Gain White Noise

+

+Sum3

Step Fcn1

d1To Workspace9

-5

100s+1

Transfer Fcn1

+

+

Sum2

r1To Workspace2

Clock

u1To Workspace

25200s+1

Transfer FcnTransport Delay

+-

Sum1Step Fcn

y2To Workspace11

0.

Gain1 White Noise1

+

+Sum5

Step Fcn2

d2To Workspace12

-5

100s+1

Transfer Fcn2

+

+

Sum6

r2To Workspace3

u2To Workspace1

25200s+1

Transfer Fcn3Transport Delay1

+-

Sum7Step Fcn31/s

Integrator1

-K-

-Kc2*taud2

du/dt

Deriv.1

+++

Sum8

y3To Workspace13

0.

Gain2 White Noise2

+

+Sum9

Step Fcn4

d3To Workspace14

-5

100s+1

Transfer Fcn4

+

+

Sum10

r3To Workspace4

u3To Workspace5

25200s+1

Transfer Fcn5Transport Delay2

+-

Sum11Step Fcn5-K-

1/taui3

1/s

Integrator2

-K-

taud3

du/dt

Deriv.2

-K-

Kc3

Figure 9: MATLAB-SIMULINK graphical window for simulating IMC controller tunings

using PI, “bumpless” PID, and PID with filter controllers

25

0 200 400 600 800 1000 1200 1400 1600 1800 2000-0.5

0

0.5

1

1.5

Time

y

Dashed-Dotted: PI; Dashed: PID; Solid: PID w/filter; Dotted: setpoint

0 200 400 600 800 1000 1200 1400 1600 1800 2000-0.05

0

0.05

0.1

0.15

Time

u

Dashed-Dotted: PI; Dashed: PID; Solid: PID w/filter

Figure 10: Simulated furnace closed-loop responses, from SIMULINK: solid:PID with filter;

dashed: “bumpless” PID (EQNB); dotted:setpoint; dash-dotted:PI.

26