Control - iea.lth.se

ControlIntroduction

Gustaf OlssonIEA

Lund University [email protected]

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



Lecture 6 Dec

• Control goals

• Dec 10 – no lecture



Lecture Dec 13

• Control of unit processes in the activated sludge system



Simple Control Structure

�

-1

Controller Process

Setpoint = reference value Control signalOutput =measurement

Error



Feedback Feedforward



Feedforward Control



Simple Controllers



On-Off Control



The PID controller

+++= ∫ dt

deTdte

TeKuu D

iP

10

Proportional gain

Error yd - y

Integral time

Derivative time

Controlsignal



P vs. PI Control



PI vs. PID Control



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


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

��

controllednot controlled


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

��

Cascade Control



Cascade (Master-Slave) Control



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



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



Feedforward Control



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



Feedforward Design

• Measure dynamics of manipulated variable and disturbance

• Check realisability– Manipulated variable dynamics faster than

disturbance dynamics

• Implement full or partial controller



Feedforward Performance



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 Handles



Control HandlesSmall costs• waste sludge

flowrate• return sludge

flowrate• step feed• recycle schemes

Larger costs• chemical

additions• external carbon• sludge

conditioning• aeration



Manipulated variables

• Hydraulic– Sludge inventory

– Recirculations

• Chemical and carbon dosage• Air or oxygen supply• Pre-treatment of influent WW



Hydraulic (1)Influent flow control

• Pumping of the influent flow• Sewer control• Equalisation basin• Flow splitting• Bypassing



Hydraulic (2)Sludge Inventory Control

• Waste sludge return rate• Return sludge flowrate• Step feed flowrates



Hydraulic (3)Recirculation streams

• Recirculation of nitrate• Recirculations in bio P• Recirculation in two-stage anaerobic

systems• Supernatants• Backwashing



Hydraulic (4)Batch reactors

• Phase length control in sequential batch reactors



Källby WWTP, Lund

12345

109876

Inlet

Compressor house

DODO

DO DO, NH4, NO3, PO4

DO, SS

SS

Flow

NH4, NO3, PO4

Flow

Flow



Pre-denitrification plant

Aerobic reactor

Sludge outtakeSludge recirculation

Influent

Internal recirculation

Effluent

Anoxic reactor



Finding the Control Goals



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



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



Major Influent Streams



Influence of DO conc. in nitrate recycle

RecirculationDO conc.



Anoxic reactor - control fast time scale

Reactionrate

control



Anoxic reactor - control medium time scale

Hydrauliccontrol



Anoxic reactor - control slow time scale

Reaction control



Controller Tuning



Objective

• Servo Loops– close following of frequent setpoint

changes

• Regulator Loops– filter disturbances from measured variable

• Averaging Loops– filter disturbances from output variable



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)



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).



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 )



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).



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.



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



IMC Filter (τm) Tuning



IMC Performance



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



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.



Self-Tuner & Auto-Tuners



Exact Linearisation

• This is where an invertible process model is used (essentially a model-based controller).



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

Documents

Control - iea.lth.se