Process IdentificationProcess Identification PID Control and TuningPID Control and Tuning Cascade...
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Process Identification PID Control and Tuning Cascade Control Feed Forward Control Non-linear Level Control Ratio Control Override Control Dead Time Compensation Pass Balancing Constraint Control Relative Gains Background of MPC Implementation of MPC Fractionator Control Fractionator Conceptual Mode Inferred Properties Model Inferred Properties Control Benefit Calculations BASIC AND ADVANCED CONTROLS
Process IdentificationProcess Identification PID Control and TuningPID Control and Tuning Cascade ControlCascade Control Feed Forward Control Feed
Process IdentificationProcess Identification PID Control and
TuningPID Control and Tuning Cascade ControlCascade Control Feed
Forward Control Feed Forward Control Non-linear Level Control
Non-linear Level Control Ratio Control Ratio Control Override
Control Override Control Dead Time Compensation Dead Time
Compensation Pass Balancing Pass Balancing Constraint Control
Constraint Control Relative Gains Relative Gains Background of MPC
Background of MPC Implementation of MPC Implementation of MPC
Fractionator ControlFractionator Control Fractionator Conceptual
Model Fractionator Conceptual Model Inferred Properties Model
Inferred Properties Model Inferred Properties Control Inferred
Properties Control Benefit Calculations Benefit Calculations BASIC
AND ADVANCED CONTROLS
Slide 2
Process Identification,
Slide 3
The variables (Flow, Temperature, Pressure, Composition)
associated with chemical process are divided into 2 groups INPUT
VARIABLE: INPUT VARIABLE:: Which denote the effect of surroundings
on the process. OUTPUT VARIABLES: OUTPUT VARIABLES: Which denote
the effect of process on surroundings The input variables can be
further classified into: MANIPULATED (or adjustable): MANIPULATED
(or adjustable): V ariables which can be adjusted freely.
DISTURBANCE VARIABLES: DISTURBANCE VARIABLES: All input variables
other than manipulated variable. Disturbance Variables can be
Measured Unmeasured Unmeasured To AIRFO F,T BASIC DEFINITIONS
Slide 4
Process Identification Why is it required? TO INVESTIGATE, How
the behavior of a process (outputs) changes with time under
influence of changes in external disturbance and Manipulated
Variable. To design an appropriate controller: kBetter insight into
process behavior leads to better control. kFor a given change in
input to a process, we need to know how much the output will
ultimately change. kIn which direction will the change take place?.
How long it will take for output to change? kWhat trajectory the
output will follow.
Slide 5
SELF REGULATORY PROCESS For a step change in input, output
attains a new steady state. Even when no control (feedback)action
exists. NON-SELF REGULATORY PROCESS For a step change in input, the
output does not attain a new Steady State, if no control (feedback)
action exists. e. g increase water to tank and level will continue
to increase unless control is exercised e. g increase feed to
column; temperature will attain a new equilibrium level without any
contrast FC Level will keep building Time
Slide 6
LINEARITY THE RESPONSE IS PROPORTIONAL TO THE MAGNITUDE OF
INPUT. OUTPUT INPUT TIME NON LINEARITY OUTPUT INPUT
Slide 7
INVERSE RESPONSE INPUT OUTPUT TIME INVERSE RESPONSE
Slide 8
PROCESS DYNAMICS Process control is concerned with the
operation of process under steady state and unsteady state
(dynamic)conditions. STEADY STATE Defined by steady state material
and energy balance equations(as got from steady state simulator for
e.g.) Process variables do not change with time. UNSTEADY STATE
(DYNAMIC) Defined by unsteady state equations. Process variables
change with time before attaining a new steady state. The way a
process behaves between 2 steady states is known as its transient
response. Process design affects transient response
Slide 9
Change in output Change in input Where, change is the change
from one steady state to another. PROCESS GAIN is a measure of the
sensitivity of the Process. A Process having a very small gain
would be rather insensitive to input. This can be compensated by
controlling the Process with a controller having a high gain.
PROCESS GAIN (Kp) or STEADY STATE GAIN: Kp=
Slide 10
TIME CONSTANT ( ) TIME CONSTANT is a measure of the capacitance
of the Process. Capacitance slows down Process dynamics. TIME
CONSTANT can be defined as: Hold-up of the Process Flow through the
Process TIME CONSTANT ( )=
Slide 11
CAPACITANCE SLOWS PROCESS DYNAMICS CASE 1 VERY LOW CAPACITANCE
( ALSO CALLED INSTANTANEOUS PROCESS ) HENCE MINIMAL DYNAMICS CASE 2
---------- IN THIS CASE DYNAMICS ARE SLOWER BECAUSE OF LARGE
CAPACITANCE IN TANK T1T1 T1T1
Slide 12
DELAY OR DEAD TIME ( ) A process transportation delay can also
adversely affect dynamics. A typical example is plug flow through a
pipeline. Any disturbance at the inlet of the pipeline is sensed at
the outlet only after a delay, which can be expressed as: Length of
the pipeline Velocity of the fluid DELAY ( ) =
Slide 13
Capacitance enable the process to remain at or near Steady
State even when distributed.Capacitance enable the process to
remain at or near Steady State even when distributed. Capacitance
helps controlCapacitance helps control while process delay (dead
time) makes control difficult.while process delay (dead time) makes
control difficult. DEAD TIME WHENEVER AN INPUT VARIABLE CHANGES,
THERE IS A TIME INTERVAL (SHORT OR LONG) DURING WHICH NO EFFECT IS
OBSERVED ON THE OUPUTS OF THE SYSTEM. THIS TIME INTERVAL IS CALLED
DEAD TIME, OR TRANSPORTATION LAG, OR PURE LAG, OR DISTANCE VELOCITY
LAG. PIPELINE CONVEYOR OUTPUT THE OUTPUT DOES NOT SENSE THE
DISTURBANCE WHICH HAS ENTERED THE SYSTEM FOR SOME FINITE TIME (
DEAD TIME ) AFTER WHICH IT REACTS ABRUPTLY. OUTPUT DELAY = DEAD
TIME =LENGTH VELOCITY DELAY
Slide 14
Process Identification A TYPICAL SELF REGULATORY PROCESS CAN BE
APPROXIMATED BY A TRANSFER FUNCITON MODEL OF 3 PARAMETERS : TIME
CONSTANT = , DEAD TIME = , PROCESS GAIN = Kp WE CAN USE THESE
PARAMETERS TO MODEL A PROCESS AS FIRST-ORDER, FIRST ORDER WITH DEAD
TIME OR A HIGHER ORDER PROCESS ( RARE ) : TYPICALLY A PROCESS IS
MODELLED AS A FIRST ORDER PROCESS WITH DEAD TIME : OUTPUT CHANGE
INPUT CHANGE Y(s) X(s) Kp. e (- S ) 1 + s TRANSFER FUNCTION = =
=
Slide 15
In this time domain this equation becomes: OUTPUT INPUT Where,
t = elapsed time = time constant of the process Kp = steady state
gain of the process It is evident that when the elapsed time equals
1 time constant then OUTPUT INPUT = Kp * ( 1 - e -t/ ) = Kp * ( 1 -
e -1 ) = 0.632 * Kp
Slide 16
Method: BROIDA Model: Y(s) = K p e - s X(s) s + 1 = 5.5 (t 2 -t
1 ) = 2.8 t 1 1.8 t 2 Where K p = Process Gain = Dead Time = Time
Constant
Slide 17
AA BB ABAB
Slide 18
0 0.2 0.4 0.6 0.8 1.0 1 / A = 0.57 2 / A = 0.20 Get 1, 2 B / A
=0.76 METHOD: OLDNBOURG AND SARTORIUS Y(S) Kp X(S) ( 1 S +1)( 2
s+1) =
Slide 19
Non-self regulatory Process
Slide 20
PID Control &Tuning
Slide 21
Introduction to Basic Controls
Slide 22
HOT WATER SIGNAL TO CTL ROOM STEAM COLD WATER T COND Controlled
Variablee.gWater outlet Temperature Manipulated Variablee.g Steam
Pressure Load Variablee.g Water Flow Basics Concepts and
Terminology for Process Control
Slide 23
The Control Problem : Relationship among controlled,
manipulated and load variable qualifies the need for process
control. kThe Control system is required to keep the controlled
variables at its desired value. kThe Control problem can be solved
in only two ways, each of which corresponds to basic control
system. FEEDBACK SYSTEM : FEED FORWARD SYSTEM : Feed Back System :
YSolves the control problem through trial and error procedure.
YStarts working when there is imbalance between the controlled
variable and set point. MV Load CV PROCESS
Slide 24
WHAT A FEED BACK CAN AND CAN NOT DO uVery rugged works
irrespective of source and type of disturbances. uIs very simple to
implement and tune without much knowledge about the process. Tuning
can be done online. B u t uStarts working only after some damage is
already done. uAn error must exist for the controller to start.
Thus incapable of perfect control. uMay perform poorly if lags
& delays are large.
Slide 25
NEGATIVE FEEDBACK For a feedback loop to be successful, it must
have negative feedback.The controller must change its output in the
direction that opposes the change in measurement variable While
negative feedback is necessary, combination of negative feedback
and lags in the process means that oscillation is the natural
response of a feedback control loop to an upset. The
characteristics of this oscillation are the primary means for
evaluating the performance of the control loop. Engineers are
interested in period and the dampening ratio of the cycle. For good
control,the cycle in pv and mv should steadily decay and end with
the pv returned to sp and mv at the new value. Oscillation
represents the trial and error search for the new solution to the
control problem as the controller is not aware of load
variables.
Slide 26
Feedback Control Modes (PID) PROPORTIONAL CONTROLS : The
controller response should be proportional to the size of the
error. OP ERROR Where, ERROR = (SP - PV) OP = Kc * (SP - PV) + BIAS
BIAS = 50 OP = Kc * E DYNAMIC PROPERTIES OF PROPORTIONAL ACTION
]The output change occurs simultaneously with error change. No
delay occurs in the proportional response. Each value of the error
for given proportional gain generates a unique value of the output.
This is limitation of proportional only controller. ]The
proportional only controller always has offset which varies with
the load.
O = Kc. E. dt + Bias A B C D E F TIME INTEGRAL ACTION RESPONDS
TO SIGN SIZE AND DURATION OF ERROR SET POINT OUTPUT Measurement
Integral Controls Ti
Slide 30
Integral Time min / repeat 1 repeat Where as proportional
action has unique output at one error, the integral action can
achieve any output value and stopping when error is zero. This
property of integral action eliminates the offset. For constant
error, up to time Ti; O = Kc. E. dt = Kc. Ti / Ti = Kc Ti
Slide 31
Proportional plus integral Control. O = Kc * E + Kc edt +
Constant,Where E = SP - PV here bais = Kc / Ti Edt 1 repeat
Proportional Response Ti OUTPUT 2525 50 75 100 LOAD +20 -20 The
Integral changes the bias term as a function of the error. Integral
action introduce the lag in the controller which increases the
period. Like proportional action integral action increases the gain
of the controller. Too much of either can cause the loop to cycle.
Ti
Slide 32
Proportional and Integral Control O = Kc * Error + Kc Error dt
qWhen compared with Proportional Controller the only difference is
bias term. qIn proportional controller the Bias is fixed where as
integral action in above equation uses the integral of error to
adjust the bias - stopping when bias is zero. qThe proportional
mode will be more effective than integral mode in responding
quickly to process upsets EFFECT OF INTEGRAL TIME q Larger the
integral time, longer it will take to reach set point qAs the
integral time is decreased, process becomes more oscillatory
Ti
Slide 33
Slide 34
PI - CONTROL (CONTD.) rThe lowest value that does not make
process significantly oscillatory. rThe shape of the response is
basically determined by proportional setting and integral times is
adjusted so as to remove the offset as quickly as possible without
making process oscillatory. r Another performance criterion is
quarter decay ratio. rFor any process,there will be a gain for
which proportional only controller will give 1/4 decay ratio. For
this or any smaller gain, the integral time can be adjusted to give
1/4 decay ratio If primary purpose of Integral Action Is To
Eliminate Offset And If Any Integral Time Setting Could Eliminate
Offset, Then What Determines Proper Integral Time ?
Slide 35
Slide 36
PI - CONTROL (CONTD.) However,when speed of response is an
important consideration,then the largest gain consistent with the
response objectives should be used For any response
criterion(minimum overshoot,1/4th decay ratio or other),the
following tuning procedure will give faster response from a PI
controller Remove integral action; Adjust Kc to give desired
response ignoring offset; Adjust the Ti to eliminate offset
Slide 37
DERIVATIVE CONTROL DERIVATIVE CONTROL OUTPUT OF CONTROLLER IS
PROPORTIONAL TO RATE OF CHANGE OF ERROR OP = Kc. Td.de/dt + BIAS
Derivative action works on change in the measurement. whenever
measurement stops changing the derivative contribution returns to
zero. Derivative action generates an immediate response
proportional to its rate of change. This is also called leading
action. The proportional response has been advanced in time. the
size of this advance is the derivative time Td. The leading
characteristics shortens the periods.
Slide 38
D
Slide 39
ABCD 0 MEASUREMENT DERIVATIVECONTRIBUTION
Slide 40
Td P+D PROPORTIONAL ONLY DERIVATIVE TIME MEASUREMENT
Slide 41
CONTINUOUS MODE (ANALOGUE CONTROLLER): MV(t) = Kc [E(T) + 1
E(t)dt + Td d (E(t)) ] + CONSTANT DIGITAL MODE (FULL POSITION OR
POSITION ALGORITHM): n MVn = Kc (En + t.E + Td (En - En -1 ) ) +
CONSTANT i=1 Ti t DIGITAL MODE (INCREMENTAL OR VELOCITY): MVn = Kc
(En En-1 + En ( t) + Td (En- 2En-1 + En-2)) Ti t t = CONTROLLER
EXECUTION PERIOD Ti dt
Slide 42
The velocity mode is popular because of: Bumpless transfer No
immediate change in the MV when the controller is put on AUTO or
MANUAL mode. The derivative mode for the velocity case is usually
on the PROCESS variable rather than the error to prevent large
change from occurring when there is a SET POINT CHANGE. The final
equation for the VELOCITY ALGORITHM is: MVn = Kc (En En-1 + En ( t)
+ Td (-PVn + 2PVn-1 - PVn-2) Ti t Apart from the current value, the
two previous values of the PROCESS VARIABLE (PV) have got to be
stored.
Slide 43
Tuning parameters are selected based on process dynamics The
ratio /( + ) is a measure of control difficulty: 0:control is easy
1:control is very difficult A reasonable basis for parameter
selection is: Kc * Kp = LOOP GAIN:3 + ---- large (very fast
response) :0.7 ---- small (sluggish response) Ti / + ----for RESET
action : around 0.5 Td / + ----for DERIVATIVE action: around
0.1
Slide 44
Slide 45
Slide 46
ZIEGLER-NICHOLS recommended settings be: MODEPPIPID
Kcd/R0.9d/R1.2d/R Ti-3.3 2.0 Td--0.5 The goal behind these settings
is to obtain a 4:1 DECAY RATIO of adjacent peaks in CLOSED LOOP
dynamic response. Comments on the method: Closed loop response
tends to be oscillatory. The derivative time is too large.
Slide 47
Slide 48
The suggested settings are: MODEP PI PID Kc0.5 Ku0.45Ku0.6Ku
Ti-Pu/1.2Pu/2 Td--Pu/8 Comments on the method: -The procedure
causes prolonged process upsets.
CASCADE CONTROL Cascade control is a multi-loop control in
which there is a secondary inner loop with a second controller. The
set point of this secondary controller is given by the primary
controller. Cascade control therefore relies on a secondary
controller measured variable
Slide 53
For cascade control to be effective the dynamics of the inner
loop should be much faster than that of the outer loop. The
response of the inner loop should be at least 3 and preferably 5 to
10 times faster than the response of the outer loop: 3 * ( + )
Secondary ( + ) Primary PCV SCV LV OPEN LOOP CLOSED LOOP WITHOUT
CASCADE CLOSED LOOP WITH CASCADE
Slide 54
Tuning of Cascade Control Loops The stepwise procedure is as
follows: The secondary loop is to be first tuned using the methods
described earlier (Open loop response and tuning graphs). Obtain
the open loop response between the secondary set point and primary
controlled variable. Calculate the primary tuning constants from
tuning graphs. Fine tune the primary controller.
Slide 55
Feed Forward Control
Slide 56
FEED FORWARD CONTROL WE WOULD LIKE TO: Control all inputs to
the unit (and hold its state variables) at Set Points. A static
unit-- ideal. If a disturbance does enter, (uncontrollable), use
some other variable to compensate for it before it affects the
output. If the unit has got affected,(output is disturbed), bring
it back to steady state as soon as possible.
Slide 57
WHEN TO USE FF CONTROL UPSETS ARE MEASURABLE FEEDBACK IS SLOW
IN ACTION SIMPLE MODEL CAN BE DEVELOPED DISADVANTAGE OF FF
DISTURBANCES MUST BE MEASURED ONLINE A REASONABLY GOOD MODEL IS
REQUIRED. MAY BE DETRIMENTAL OTHERWISE IDEAL FF MAY BE PHYSICALLY
UNREALISABLE BUT APPROXIMATIONS ARE USUALLY GOOD ENOUGH
Slide 58
Gp Gc FEED BACK U Gu Gp FF CONTROL MV BASIC FB STRUCTURE BASIC
FF STRUCTURE SINCE DISTURBANCE IS MEASURED BEFORE PERFECT CONTROL
IS POSSIBLE PERFECT CONTROL IS POSSIBLE ENHANCES FB ACTION ENHANCES
FB ACTION DOES NOT CAUSE INSTABILITY..ALGEBRAIC EQUATION CONFIRMS
IT. DOES NOT CAUSE INSTABILITY..ALGEBRAIC EQUATION CONFIRMS IT. Y
sp + MV PV U
Slide 59
FC FF FF COMPENSATES FOR MEASURED UPSETS
Slide 60
K u (1+ p s ) e -( u- p)s K p (1+ u s) -
Slide 61
In the Laplace domain the feed forward controller can be
represented as: - F.F.Controller= Kff* ( 1 + LD S ) *e - ff S (1 +
LG S) GAIN = - Ku / Kp LEAD = LD = p LAG = LG = u ff = u - p ff
should be 0.0 The LEAD/LAG algorithm of feed forward controller
DYNAMICALLY COMPENSATES for upset and control dynamics.
Slide 62
LEAD LAG 63.2 % Recovery
Slide 63
Slide 64
STEP-2: FINE TUNE THE DYNAMIC COMPENSATION
Slide 65
P > U EFFECT OF DEADTIME ACTION CANNOT BE PREPONED !!! PP CV
SET POINT CV MV DV
Slide 66
DYNAMIC FEEDFORWARD TO FULLY COMPENSATE FOR A DISTURBANCE, MVS
EFFECT ON PV SHOULD BE FELT AS FAST AS THE EFFECT OF DISTURBANCE.
ACTION CANNOT BE TAKEN BEFORE DISTURBANCE IS MEASURED.
Slide 67
Non-linear Level Control
Slide 68
Objective : To utilise hold up to reduce variation in outflow
to reduce disturbances in downstream processes. Method: Reduce
controller gain as the level approaches set point. Increase
controller gain as the level approaches high or low limit.
Slide 69
Algorithm: When PV is outside High-Low limits set gain high
(say Kh) When PV is between set point and high limit set gain =
Kh*(SP-PV)/(HL-SP) When PV is between set point and low limit set
gain= Kh*(PV-SP))/(SP-LL)
Slide 70
Ratio Control
Slide 71
RATIO CONTROL Ratio control is a special type of feed forward
control where 2 disturbance (load) are measured and held in a
constant ratio to each other. It is mostly used to control the
ratio of flow rates of two streams. Both flow rates are measured
but only one can be controlled. The stream where flow rate is not
under control is usually referred to as wild stream.
Slide 72
FT Divider FT RC X - + Wild Stream Controlled Stream FT FC X
Wild Stream Controlled Stream FT X - + Desired Ratio Alternate
Configurations of Ratio Control
Slide 73
Applications of Ratio Control Ratio control is used extensively
in the industry with the following as most commonly encountered
examples: Keep constant ratio between feed flow rate and the steam
(heating media) in reboiler in distillation column. Hold constant
the reflux ratio in a distillation column. Control the ratio of 2
reactants entering a reactor at a desired value.
Slide 74
Applications of Ratio Control Hold the ratio of 2 blended
streams constant in order to maintain the composition of the blend
at the desire ratio. Keep the ratio of fuel/air in a burner at its
optimum value. Maintain the ratio of the liquid flow rate to vapour
flow rate in an absorber constant, in order to achieve the desired
composition in the exit vapour stream. Maintain the ratio of steam
flow rate to strippers to bottom product flow rate from stripper in
order to achieve optimum use of steam.
Slide 75
Example of Ratio Control This controller adjust steam flow in
ratio of bottom product of stripper Striping Steam Flow = Ratio *
RCO Flow Steam RCO Ratio Control Ratio FC
Slide 76
Example of Ratio Control
Slide 77
Override Control
Slide 78
During the normal operation of a plant or during its start-up
or shutdown, it is possible that dangerous situation may arise
which may lead to destruction of equipment and operating personnel.
In such cases it is necessary to change from the normal control
action and attempt to prevent a process variable from exceeding an
allowable upper or lower limit.
Slide 79
Override Control This can be achieved through the use of
special types of switches. The high selector switch (HSS) is used
whenever a variable should not exceed an upper limit. The low
selector switch (LSS) is employed to prevent a process variable
from exceeding lower limit.
Slide 80
Slide 81
Slide 82
DEAD TIME COMPENSATION
Slide 83
Feedback control using a conventional PID controller is
seriously limited when it comes to handling processes with
substantial time delays. These time delays are commonly called
deadtimes. Deadtime is described mathematically by: Laplace domain
: Y(s) X(s) Time domain: Y (t) = X (t - ) Where = dead time DEAD
TIME COMPENSATION = e - s
Slide 84
BLOCK DIAGRAM REPRESENTATIVE OF SMITH PREDICTOR DEAD TIME
COMPENSATION E* = R-Y* E = E*-E = E* - Y - Y = R - Y* - Y + Y = R -
(Y* - Y + Y)
Slide 85
- + + Controller PROCESS K p s + 1 K p. e - s s + 1 - - + - + -
- + Load Variable E Y Y* Y Y* - Y + E* = R-Y* E = E*-E = E* - Y - Y
= R - Y* - Y + Y = R - (Y* - Y + Y) DEAD TIME COMPENSATION
(Alternate Configuration)
Slide 86
Pass Balancing
Slide 87
Typical Constraints Max Allowable Flow Change Max % Flow
Imbalance Max Pass Flow Set Point Min Absolute Flow Through Each
Pass X FC Crude Flow Ratios Heater Pass Balancing XXX Pass Flows
Pass COT
Slide 88
Pass Balancing 50 m3/h 305 C 50 m3/h 310 C 50 m3/h 290 C 50
m3/h 295 C
Slide 89
Objective of scheme: It should distribute the change in flow to
all the passes It should manipulate individual pass flow to get all
passes Coil Outlet within 1 C. Manipulated Variables: Passes Flows
Control Variable: Pass Coil Outlet Temp Constraints: Furnace tube
skin Temp Fire box temperatures Pass flow valves Openings Minimum
number of passes available for control.
Slide 90
Pass Balancing Pass Balancing: Step1: Check if Minimum passes
are in Cascade Step2: Check if C/V OP are within limit. If not drop
that MV. Step3: Check if Skin temperatures are within limit Feed
Controller: 1.0 Calculate the Total Feed flow as a PV TOTFEED.PV =
F1+F2+F3+F4 2.0 Set Point of Total Feed Controller TOTFEED.SP :
Operator Entered Value 3.0 Change in feed flow control F1_New =
F1*TOTFEED.SP/TOTFEED.PV 4.0 Check for delta change in feed Feed =
F1_new - F1 5.0 if abs ( Feed) > Max Change Then Clamp Value to
Max Change
Slide 91
Pass Balancing: 1.0 Calculate weighted average temp TAVG =
(F1*T1+F2*T2+F3*T3+F4*T4)/(F1+F2+F3+F4) 2.0 Calculate Pass
Balancing Factor X1 = T1/TAVG X2 = T2/TAVG .. 3.0 Calculate Change
in Flow required for PBAL F1 = F1*(T1/TAVG-1) 4.0 Check for delta
change in feed if abs ( Feed) > Max Change Then Clamp Value to
Max Change 5.0 Set new value of pass flow.
Slide 92
Exercise on passbalancing F1 = 50*(295/300-1) = F2 =
50*(290/300-1) = F3 = 50*(310/300-1) = F4 = 50*(305/300-1) =
-0.833333 -1.666667 1.66667 0.83333 Total Feed = 50+50+50+50 =200
Tavg = (295*50 + 290*50 + 310*50 +305*50)/200 = 300 50 m3/h 305 C
50 m3/h 310 C 50 m3/h 290 C 50 m3/h 295 C
Slide 93
Multi-constraint Control
Slide 94
Multi-Constraint Controller IR V Objective: Maximisation of
Re-boiler Duty Constraints: Internal Reflux Vapour Velocity
Pressure Controller CV Opening
Slide 95
Multi Constraint Controller Define: Objective Function of
Manipulated Variable MIN / MAX Identify the Constraints C1, C2, C3
are three constraints Limits of Constraints Decide constraints and
delta constraints limits Initial proportional and integral gains
Calculate ERR = CV(current value) - CL(constraint limit)
Proportional output PO = Kp*[CV - OV (old value)] Integral Output
IO = Kp*ERR*CT/Ti
Slide 96
Multi Constraint Controller Select most limiting constraint The
limiting Constraint will have Minimum margin from limit If
objective is MIN, select maximum IO else select minimum IO. If
controller is wound up If wound up HI or HILO & IO>0 set
selected IO=0 If wound up LO or HILO & IO