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Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Lecture 3 Dec
• Nonlinear and linear systems– Aeration, Growth rate, DO saturation
• Feedback control• Cascade control• Manipulated variables
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Lecture 6 Dec
• Control goals
• Dec 10 – no lecture
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Lecture Dec 13
• Control of unit processes in the activated sludge system
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Simple Control Structure
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-1
Controller Process
Setpoint = reference value Control signalOutput =measurement
Error
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Feedback Feedforward
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Feedforward Control
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Simple Controllers
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
On-Off Control
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
The PID controller
+++= ∫ dt
deTdte
TeKuu D
iP
10
Proportional gain
Error yd - y
Integral time
Derivative time
Controlsignal
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
P vs. PI Control
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
PI vs. PID Control
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Control Structures for Aeration(a ) C o n s ta n t a e ra tio n ra te
(b ) O p e n lo o p c o n tro l b a s e d o n t im e
O n /o ff
(c ) C lo s e d lo o p c o n tro l
D is s o lv e d o x yg e n s e n s o r
V a r ia b le s p e e d d riv e
P ro g ra m m a b le c o n tro lle r
A e ro b ic re a c to r
C o m p re s s o r
T im e r
C o m m u n ic a tio n lin e
(d ) D is s o lv e d p ro file c o n tro l
(e ) D yn a m ic s e t p o in t c o n tro l
L in e s s e n d in g s e t p o in t
A m m o n ia s e n s o r
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University1
1,5
2
2,5
3
3,5
22-okt 23-okt 24-okt 25-okt 26-okt 27-okt 28-okt 29-okt 30-okt
DO
, m
g/l
DO (6 and 7)
DO (8)
DO (9)
DO concentrations in 3 zones over a 7 day period
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controllednot controlled
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University0
500
1000
1500
2000
2500
22-okt 23-okt 24-okt 25-okt 26-okt 27-okt 28-okt 29-okt 30-okt
Flo
w N
orm
al
M3/
ho
ur
Airflow (6 and 7)
Airflow (8)
Airflow (9)
Total
Air flow rates in 3 zones over a 7 day period
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Cascade Control
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Cascade (Master-Slave) Control
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Cascade Control Applications
• Valve positioners (remove hysteresis).• Fast rejection of disturbances in the
control signal (air/steam header pressure changes).
• Gain scheduling (master controller sees the slave sensor characteristics in place of the process characteristics).
Feedforward Control
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Why Feedforward?
• Measure the disturbance before it hits the plant
• Compensate for the disturbance before it has affected the plant
• The price: must supply a model of the influence of the disturbance
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Feedforward Control
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Model Based Control (MBC)
• Feedforward Control – simplest
• Predictive Control– commercially available - too high price– mostly linear, can handle hard
constraints
• Generic Model Control– nonlinear, can handle hard constraints
• State Feedback Control– older linear technique
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Feedforward Design
• Measure dynamics of manipulated variable and disturbance
• Check realisability– Manipulated variable dynamics faster than
disturbance dynamics
• Implement full or partial controller
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Feedforward Performance
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Predictive Control
• General principle of operation:– use past control actions (and predicted
disturbances) with the model to predict future measured variables
– compare to the goals and constraints
– determine appropriate control actions to take
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Control Handles
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Control HandlesSmall costs• waste sludge
flowrate• return sludge
flowrate• step feed• recycle schemes
Larger costs• chemical
additions• external carbon• sludge
conditioning• aeration
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Manipulated variables
• Hydraulic– Sludge inventory
– Recirculations
• Chemical and carbon dosage• Air or oxygen supply• Pre-treatment of influent WW
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Hydraulic (1)Influent flow control
• Pumping of the influent flow• Sewer control• Equalisation basin• Flow splitting• Bypassing
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Hydraulic (2)Sludge Inventory Control
• Waste sludge return rate• Return sludge flowrate• Step feed flowrates
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Hydraulic (3)Recirculation streams
• Recirculation of nitrate• Recirculations in bio P• Recirculation in two-stage anaerobic
systems• Supernatants• Backwashing
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Hydraulic (4)Batch reactors
• Phase length control in sequential batch reactors
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Källby WWTP, Lund
12345
109876
Inlet
Compressor house
DODO
DO DO, NH4, NO3, PO4
DO, SS
SS
Flow
NH4, NO3, PO4
Flow
Flow
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Pre-denitrification plant
Aerobic reactor
Sludge outtakeSludge recirculation
Influent
Internal recirculation
Effluent
Anoxic reactor
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Finding the Control Goals
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Goals and ObjectivesSocietal goals
• care for surrounding environment
• care for employees• care for society
Process or plant goals
• Meet effluent discharge goals
• Achieve good disturbance rejection
• Minimize operating costs
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Operational Objectives• grow the right biomass population• maintain good mixing• adequate loading and DO conc.• adequate air flow• good settling properties• avoid clarifier overload• avoid denitrification in clarifier
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Major Influent Streams
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Influence of DO conc. in nitrate recycle
RecirculationDO conc.
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Anoxic reactor - control fast time scale
Reactionrate
control
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Anoxic reactor - control medium time scale
Hydrauliccontrol
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Anoxic reactor - control slow time scale
Reaction control
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Controller Tuning
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Objective
• Servo Loops– close following of frequent setpoint
changes
• Regulator Loops– filter disturbances from measured variable
• Averaging Loops– filter disturbances from output variable
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Control Action Guidelines• use as few as possible• use only P action for liquid levels• use only P action on the inner loop of cascade
loops• use only I action for averaging loops• add I action to remove stationary errors• add D action for high order process dynamics
where the initial reaction is slow• use extra care with D action when the
measurement is noisy (filter the measurement)
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Tuning Procedures• there are nearly as many techniques as
there are control engineers but all:– identify a simple model of the process (the
form of the model),
– estimate parameters for the model form chosen, usually by some type of stimulus-response experiment on the process, and
– design controller parameters according to some procedure (among the many techniques, we recommend IMC tuning).
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Tuning Cascade Control
• Slave controller high-gain proportional servo loop (tune first).
• Master controller lower-gain regulator with integral action to remove offset.
• ( Analogous to the human master-slave relationship )
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Identification (form)
• Depends on tuning rules and process knowledge.
• IMC supports integrator, integrator + first order, first order, second-order overdamped, second-order underdamped, and a few others (see literature).
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Estimation (parameters)
• A simple open-loop step test.• A closed-loop step test with proportional
only control and a gain high enough to give a decay ratio of about one third.
• Fitting the chosen model to time series data, using standard least squares regression.
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Tuning RulesModel KP KI KD TI TD First order
mKττ
mKτ
1
- τ -
2nd order overdamped
mKτττ 21 +
mKτ
1
mKτττ 21 21 ττ +
21
21
ττττ+
2nd order underdamped
mKτζτ2
mKτ
1
mKττ 2
ζτ2
ζτ2
Integrator
mKτ1
0 0 - -
Integrator plus 1st order
mKτ1
0
mKττ
- τ
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
IMC Filter (τm) Tuning
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
IMC Performance
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Time-Varying Loops
• To maintain constant performance the controller must retune to compensate for changes in process characteristics.
• Approaches are:– scheduling
– self tuning– exact linearisation
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Scheduling
• Changing loop characteristic must be related to a measurable parameter - the controller input, output or some other measurement.
• Construct a “schedule” for the controller gain that compensates.
• Schedules for nonlinear valve characteristics is common as function of controller output.
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Self-Tuner & Auto-Tuners
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Exact Linearisation
• This is where an invertible process model is used (essentially a model-based controller).
Control of Biological WWT 2002
Gustaf Olsson, IEA, Lund University
Summary
• PI(D) controllers can solve most problems in WWT
• Controller tuning rules• Cascade control common practice• Use feedforward to meet disturbances• More difficult problems
– Time varying systems– Nonlinear systems