Control Introduction. Gustaf Olsson IEA Lund University.
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1 Control Introduction Gustaf Olsson IEA Lund University
2 Lecture 3 Dec Nonlinear and linear systems Aeration, Growth rate, DO saturation Feedback control Cascade control Manipulated variables
3 Lecture 6 Dec Control goals Dec 10 no lecture
4 Lecture Dec 13 Control of unit processes in the activated sludge system
5 Simple Control Structure Setpoint = reference value Error Control signal Output = measurement Controller Process -1
6 Feedback Feedforward
7 Feedforward Control
8 Simple Controllers
9 On-Off Control
10 The PID controller u 1 de = u + K e + e dt + T 0 P D T dt i Control signal Proportional gain Error y d -y Integral time Derivative time
11 P vs. PI Control
12 PI vs. PID Control
13 Control Structures for Aeration (a) Constant aeration rate Aerobic reactor (d) Dissolved profile control Compressor (b) Open loop control based on tim e Timer Ammonia sensor (e) Dynamic set point control On/off (c) Closed loop control Variable speed drive Programmabl e controller Communicati on line Dissolved oxygen sensor Lines sending set point
14 DO concentrations in 3 zones over a 7 day period 3,5 not controlled controlled DO (6 and 7) DO (8) DO (9) 3 2,5 DO, mg/l 2 1,5,QJLOGVHQ 1 22-okt 23-okt 24-okt 25-okt 26-okt 27-okt 28-okt 29-okt 30-okt
15 Air flow rates in 3 zones over a 7 day period 2500 Airflow (6 and 7) Airflow (8) Airflow (9) Total 2000 Flow Normal M3/hour ,QJLOGVHQ 0 22-okt 23-okt 24-okt 25-okt 26-okt 27-okt 28-okt 29-okt 30-okt
16 Cascade Control
17 Cascade (Master-Slave) Control
18 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).
19 Feedforward Control
20 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
21 Feedforward Control
22 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
23 Feedforward Design Measure dynamics of manipulated variable and disturbance Check realisability Manipulated variable dynamics faster than disturbance dynamics Implement full or partial controller
24 Feedforward Performance
25 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
26 Control Handles
27 Control Handles Small costs waste sludge flowrate return sludge flowrate step feed recycle schemes Larger costs chemical additions external carbon sludge conditioning aeration
28 Manipulated variables Hydraulic Sludge inventory Recirculations Chemical and carbon dosage Air or oxygen supply Pre-treatment of influent WW
29 Hydraulic (1) Influent flow control Pumping of the influent flow Sewer control Equalisation basin Flow splitting Bypassing
30 Hydraulic (2) Sludge Inventory Control Waste sludge return rate Return sludge flowrate Step feed flowrates
31 Hydraulic (3) Recirculation streams Recirculation of nitrate Recirculations in bio P Recirculation in two-stage anaerobic systems Supernatants Backwashing
32 Hydraulic (4) Batch reactors Phase length control in sequential batch reactors
33 Källby WWTP, Lund Compressor house Inlet SS DO DO, NH4, NO3, PO4 Flow Flow Flow NH4, NO3, PO4 DO DO DO, SS
34 Pre-denitrification plant Influent Anoxic reactor Aerobic reactor Effluent Internal recirculation Sludge recirculation Sludge outtake
35 Finding the Control Goals
36 Goals and Objectives Societal 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
37 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
38 Major Influent Streams
39 Influence of DO conc. in nitrate recycle Recirculation DO conc.
40 Anoxic reactor - control fast time scale Reaction rate control
41 Anoxic reactor - control medium time scale Hydraulic control
42 Anoxic reactor - control slow time scale Reaction control
43 Controller Tuning
44 Objective Servo Loops close following of frequent setpoint changes Regulator Loops filter disturbances from measured variable Averaging Loops filter disturbances from output variable
45 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)
46 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 stimulusresponse experiment on the process, and design controller parameters according to some procedure (among the many techniques, we recommend IMC tuning).
47 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 )
48 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).
49 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.
50 Tuning Rules Model K P K I K D T I T D First order τ 1 - τ - 2 nd order overdamped 2 nd order underdamped Integrator Kτ m τ τ 2 Kτ m 2ζτ Kτ m Kτ m 1 Kτ 1 + m 1 Kτ m τ τ 2 Kτ m 1 τ 1 + τ 2 τ 2 Kτ m 2 ζτ τ1τ 2 τ + τ 1 τ 2ζ 2 Integrator plus 1 st order Kτ m 1 0 Kτ m τ - τ Kτ m
51 IMC Filter (τ m ) Tuning
52 IMC Performance
53 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
54 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.
55 Self-Tuner & Auto-Tuners
56 Exact Linearisation This is where an invertible process model is used (essentially a modelbased controller).
57 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
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