Practical Process Control Using Control Station

Practical Process Control Using Control Station
Prof. Doug Cooper Chemical Engineering Dept. University of Connecticut (Storrs) Copyright © 2002 Douglas J. Cooper All Rights Reserved

1. Fundamental Principles of Process Control
Motivation for Automatic Process Control Safety First: people, environment, equipment The Profit Motive: meeting final product specs minimizing waste production minimizing environmental impact minimizing energy use maximizing overall production rate Copyright © 2002 Douglas J. Cooper All Rights Reserved

“Loose” Control Costs Money
It takes more material to make a product thicker, so greatest profit is to operate as close to the minimum thickness constraint as possible without going under It takes more processing to remove impurities, so greatest profit is to operate as close to the maximum impurities constraint as you can without going over Copyright © 2002 Douglas J. Cooper All Rights Reserved

Tight Control = Most Profitable Operation
A well controlled process has less variability in the measured process variable, so the process can be operated close to the profitable constraint Copyright © 2002 Douglas J. Cooper All Rights Reserved

An introductory example
Copyright © 2002 Douglas J. Cooper All Rights Reserved

Consider Heating a House
Thermostat controller TC Temperature sensor/transmitter TT heat loss (disturbance) set point Control signal Measurement Computation/ Decision Action furnace fuel flow valve Copyright © 2002 Douglas J. Cooper All Rights Reserved

Automatic Control is Measurement  Computation  Action
Error Is house cooler than set point? ( TSetpoint  Thouse > 0 ) Action  open fuel valve Is house warmer than set point? ( TSetpoint  THouse < 0 ) Action  close fuel valve Copyright © 2002 Douglas J. Cooper All Rights Reserved

Feedback Control Loop Block Diagram: Components and Variables for home heating
house temperature measurement signal Controller error Manipulated fuel flow to furnace House temperature output Tm T Set Point Heat Loss Disturbance TSP Home Heating Process Thermostat Fuel Valve + - Comparator Temperature Sensor/Transmitter Copyright © 2002 Douglas J. Cooper All Rights Reserved

Terminology Control Objective Controlled Process Variable
Measured Variable Set point Error Controller Output Manipulated Variable Disturbances Copyright © 2002 Douglas J. Cooper All Rights Reserved

General Feedback Control Loop Block Diagram
“Actual” controller Manipulated Process variable Controlled Process variable Controller error Controller output Final Control Element Set Point Controller + - Process ySP(t) (t) o(t) m(t) y(t) Comparator Disturbance d(t) measured variable (feedback signal) Measurement Sensor/Transmitter ym(t) Copyright © 2002 Douglas J. Cooper All Rights Reserved

Examples Measurement Sensors:
temperature, pressure, pressure drop, level, flow density, concentration Final Control Element: solenoid, valve, variable speed pump or compressor, heater or cooler Automatic Controllers: on/off, PID, cascade, feed forward, model-based Smith predictor, multivariable, sampled data, parameter scheduled adaptive control Copyright © 2002 Douglas J. Cooper All Rights Reserved

3. Graphical Modeling of Dynamic Process Data
Process Behavior and Controller Tuning Consider cruise control for a car vs a truck how quickly can each accelerate or decelerate what is the effect of disturbances (wind, hills, etc.) Controller (gas flow) manipulations required to maintain set point velocity in spite of disturbances (wind, hills) are different for a car and truck because the dynamic behavior of each "process" is different Dynamic behavior  how the measured process variable responds over time to changes in the controller output and disturbance variables Copyright © 2002 Douglas J. Cooper All Rights Reserved

Understanding Dynamic Process Behavior
To learn about the dynamic behavior of a process, analyze measured process variable test data Process variable test data can be generated by suddenly changing the controller output signal Be sure to move the controller output far enough and fast enough so that the dynamic behavior of the process is clearly revealed as the process responds The dynamic behavior of a process is different as operating level changes (nonlinear behavior) so collect process data at normal operating levels (design level of operation) Copyright © 2002 Douglas J. Cooper All Rights Reserved

Modeling Dynamic Process Behavior
The best way to understand process data is through modeling Modeling means fitting a first order plus dead time (FOPDT) dynamic process model to the data set: where: y(t) is the measured process variable u(t) is the controller output signal The FOPDT model is low order and linear so it can only approximate the behavior of real processes Copyright © 2002 Douglas J. Cooper All Rights Reserved

FOPDT The important parameters that result are:
When a first order plus dead time (FOPDT) model is fit to dynamic process data The important parameters that result are: Steady State Process Gain, KP Overall Process Time Constant, P Apparent Dead Time, P Copyright © 2002 Douglas J. Cooper All Rights Reserved

The FOPDT Model is All Important
model parameters (KP, P and P) are used in correlations to compute initial controller tuning values sign of KP indicates the action of the controller (+KP  reverse acting; KP  direct acting) size of P indicates the maximum desirable loop sample time (be sure sample time T  0.1P) ratio P /P indicates whether MPC (Smith predictor) would show benefit (useful if P  P) model becomes part of the feed forward, Smith predictor, decoupling and other model-based controllers Copyright © 2002 Douglas J. Cooper All Rights Reserved

Step Test The controller is set to manual mode
Process starts at steady state Controller output signal is stepped to new value Measured process variable allowed to complete response Copyright © 2002 Douglas J. Cooper All Rights Reserved

Process Gain From Step Test Data
KP describes how much the measured process variable, y(t), changes in response to changes in the controller output, u(t) A step test starts and ends at steady state, so KP can be computed from plot axes where u(t) and y(t) represent the total change from initial to final steady state A large process gain means the process will show a big response to each control action Copyright © 2002 Douglas J. Cooper All Rights Reserved

KP for Gravity Drained Tanks
Steady state process gain has a: size (0.095), sign (+0.095), and units (m/%) Copyright © 2002 Douglas J. Cooper All Rights Reserved

Non-Interacting Gravity Drained Tanks
I.C.: t=0  h1 = h1s TANK 2 I.C.: t=0  h2 = h2s Copyright © 2002 Douglas J. Cooper All Rights Reserved

Overall Time Constant From Step Test Data
Time Constant P describes how fast the measured process variable, y(t), responds to changes in the controller output, u(t) P is how long it takes for the process variable to reach 63.2% of its total change, starting from when the response first begins Copyright © 2002 Douglas J. Cooper All Rights Reserved

P for Gravity Drained Tanks
Locate where the measured process variable first shows a clear initial response to the step change – call this time tYstart From plot, tYstart = 9.6 min Copyright © 2002 Douglas J. Cooper All Rights Reserved

2) Locate where the measured process variable reaches y63.2, or where y(t) reaches 63.2% of its total final change Label time t63.2 as the point in time where y63.2 occurs Copyright © 2002 Douglas J. Cooper All Rights Reserved

y(t) starts at 1.93 m and shows a total change y = 0.95 m y63.2 = 1.93 m (y) = 1.93 m (0.95 m) = 2.53 m y(t) passes through 2.53 m at t63.2 = 11.2 min Copyright © 2002 Douglas J. Cooper All Rights Reserved

- The time constant is the time difference between tYstart and t63.2 - Time constant must be positive and have units of time From the plot: P = t63.2  tYstart = 11.2 min  9.6 min = 1.6 min Copyright © 2002 Douglas J. Cooper All Rights Reserved

Apparent Dead Time From Step Test Data
P is the time from when the controller output step is made until when the measured process variable first responds Apparent dead time, P, is the sum of these effects: transportation lag, or the time it takes for material to travel from one point to another sample or instrument lag, or the time it takes to collect analyze or process a measured variable sample higher order processes naturally appear slow to respond Notes: Dead time must be positive and have units of time Tight control in increasingly difficult as P  0.7P For important loops, work to avoid unnecessary dead time Copyright © 2002 Douglas J. Cooper All Rights Reserved

Processes Have Time-Varying Behaviors
The predictions of a FOPDT model are constant over time But real processes change every day because surfaces foul or corrode mechanical elements like seals or bearings wear feedstock quality varies and catalyst activity drifts environmental conditions like heat and humidity change So the values of KP, P, P that best describe the dynamic behavior of a process today may not be best tomorrow As a result, controller performance will degrade with time and periodic retuning may be required Copyright © 2002 Douglas J. Cooper All Rights Reserved

Processes Have Nonlinear Behaviors
The predictions of a FOPDT model are constant as operating level changes The response of a real process varies with operating level Copyright © 2002 Douglas J. Cooper All Rights Reserved

Practical Process Control Using Control Station

Similar presentations

Presentation on theme: "Practical Process Control Using Control Station"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Practical Process Control Using Control Station

Similar presentations

Presentation on theme: "Practical Process Control Using Control Station"— Presentation transcript:

Similar presentations

About project

Feedback