Model of Stirred Tank Heater

Slides:

Advertisements

Similar presentations

Dynamic Behavior of Ideal Systems

Advertisements

Closed-Loop Transfer Functions

Copyright © Thomas Marlin 2013

1 ChE / MET Apr 12. Feedback Controller Tuning: (General Approaches) 1)Simple criteria; i.e QAD via ZN I, t r, etc easy, simple, do on existing.

INDUSTRIAL AUTOMATION (Getting Started week -1). Contents PID Controller. Implementation of PID Controller. Response under actuator Saturation. PID with.

Example: PI-control of integrating process. Simulink, tunepid4.

CHE 185 – PROCESS CONTROL AND DYNAMICS

Intro to modeling April Part of the course: Introduction to biological modeling 1.Intro to modeling 2.Intro to biological modeling (Floor) 3.Modeling.

Part IV ：Enhancements to Single-Loop PID Feedback Control

Dynamic Energy Balance. Last time: well-mixed CSTR w/flow & reaction for multiple reactions: rxn #

Modelling & Simulation of Chemical Engineering Systems

Briefing on Process Control and Dynamics Laboratory 20 January 2010.

Specialization project 2012 Temperature control of an unstable chemical reactor By Ola Sæterli Hjetland Supervisors: Sigurd Skogestad, Krister Forsman.

Solving Equations = 4x – 5(6x – 10) -132 = 4x – 30x = -26x = -26x 7 = x.

ChE / MET Mar Mar 07 2 Apr Mar 09.

TCS3 Servo System Proposed Design. Why Model? Meet Resolution Requirements What Resolution Encoder to use? Velocity Configuration PID Values Wind Loading.

A Comparative Study Of Deterministic And Stochastic Optimization Methods For Integrated Design Of Processes Mario Francisco a, Silvana Revollar b, Pastora.

Lecture 7: PID Tuning.

1 CBE / MET Mar 12 Dead Time... “Dead Time is the Killer of Control The presence of dead time, Ө p, is never a good thing in a control loop. Think.

1 CBE 491 / Oct 12 Model of Stirred Tank Heater Goal: set up models to simulate and see effect of tuning parameters 1 st principles (Chaps 3 – 6);

Transfer Functions Chapter 4

Control output i.e. Output = Input, despite disturbances This is achieved by feedback. Open loop systems - i.e. without feedback Process with transfer.

Alternative form with detuning factor F

Chapter 3 mathematical Modeling of Dynamic Systems

Matlab Demo #1 ODE-solver with parameters. Summary Here we will – Modify a simple matlab script in order to split the tasks to be sent to the cluster.

Equations Reducible to Quadratic

5.2 – Solving Inequalities by Multiplication & Division.

Linear Inequalities by Graphing

Transfer Functions Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: The following terminology.

MESB374 System Modeling and Analysis Feedback Control Design Process

Review: Multiple Steady States in CSTR

(Transfer functions and Block Diagrams)

Lecture 2: Mathematical Modeling Copyright © Thomas Marlin 2013 The copyright holder provides a royalty-free license for use of this material at non-profit.

CONTROL SYSTEM DESIGN by using MATLAB

1 CBE 491 / Oct 12 Deadtime in a Process. 2 Dead Time in a Process Show how dead time might show up How it affects block diagrams How it affects.

ERT 210 DYNAMICS AND PROCESS CONTROL CHAPTER 11 – MATLAB TUTORIAL

EEN-E1040 Measurement and Control of Energy Systems Control I: Control, processes, PID controllers and PID tuning Nov 3rd 2016 If not marked otherwise,

WORKSHOP 1 CUSTOM TIRE SUBROUTINE

Workshop for Flipped Class

Presentation at NI Day April 2010 Lillestrøm, Norway

WORKSHOP 7 PID TUNING.

Mathematical Models for Simulation, Control and Testing

Tuning of PID controllers

Optimal control T. F. Edgar Spring 2012.

Av Finn Aakre Haugen IA3112 Automatiseringsteknikk og EK3114 Automatisering og vannkraftregulering Høstsemesteret 2017 Gain scheduling.

Tuning of PID controllers

Dynamic Behavior of Ideal Systems

Objective- To solve equations in the form ax - b = c.

محاسبات عددی و برنامه نویسی

G1 and G2 are transfer functions and independent of the

USING GRAPHS TO SOLVE EQUATIONS

Ch. 2: Dynamics: Tower Diameter

Important Properties of Laplace Transforms

Introduction to Process Control

CBE / MET Feb 12.

Example : Optimal blending of gasoline

Av Finn Aakre Haugen IA3112 Automatiseringsteknikk og EK3114 Automatisering og vannkraftregulering Høstsemesteret 2018 Gain scheduling.

Solving Special Cases.

Dynamical Systems Basics

How a control system may become unstable

Root Locus Plot of Dynamic Systems

Solving for x and y when you have two equations

How a control system may become unstable

Simulink Examples Ex1: Simulate the following equation Blocks needed:

Solving Special Cases.

Where do these graphs intersect

Objectives: To graph lines using the slope-intercept equation

Exercise 2-1 For the level controlled process, h2 is selected as its controlled variable, and Qin is the main input of the process. Suppose the sectional.

G1 and G2 are transfer functions and independent of the

Balanced scales and equations

Presentation transcript:

Model of Stirred Tank Heater 14 Mar 12 Model of Stirred Tank Heater CBE / MET 433 Goal: set up models to simulate and see effect of tuning parameters 1st principles (Chaps 3 – 6); transfer functions (just looked at this a bit) process simulators (AspenPlus Dynamics; CBE 450/550 class)

Stirred Tank Heater (w/ PI Controller) + + + - energy balance on tank w/o control PI controller equation

Stirred Tank Heater (w/ PI Controller) Let:

ODE Solver (POLYMATH; MATLAB; MATHCAD; etc) Polymath code: step= if (t<1) then (0) else (1) Ti = 0 + step * 10

ODE Solver: POLYMATH Polymath code (stirred tank heater): d(C) / d(t) = -1/tau*C + KT/tau*Ti + K1KT/tau*M C(0) = 0 d(errsum) / d(t) = R – C errsum(0) = 0 tau = 5 # min KT = 0.5 # %TO/degC K1KT = 0.8 # %TO/%CO R = 0 # set point stays same M = Kc*(R-C) + Kc/tauI*errsum step = if (t<1) then (0) else (1) Ti = 0 + step * 10 # step change disturbance Kc = 1.3 # %CO/%TO tauI = 10 # min t(0) = 0 t(f) = 100 # min In Class Demo / Exercise: Polymath Demonstration Build model in Polymath (ODE solver) Solve; graph C vs t Explore: Try P-only controller Adjust Kc and tauI to get QAD Try different Kc/tauI sets Can you get underdamped response? What is response to step change in R(t); holding Ti at the SS value?

Model of Stirred Tank Heater CBE / MET 433 Model of Stirred Tank Heater Goal: set up models to simulate and see effect of tuning parameters 1st principles (Chaps 3 – 6); transfer functions (just looked at this a bit) process simulators (AspenPlus Dynamics; CBE 450/550 class)

Stirred Tank Heater (transfer function simulator) + + + - Transfer function simulator: Loop Pro Developer (Control Station) In Class Demo / Exercise: Build model in Loop Pro Developer (Custom Process) Turn on PI Controller and set Kc and tauI Explore: Change load (Ti) up by 10 to 60%; observe system response Change back to 50%; observe response Try P-only controller Adjust Kc and tauI to get QAD Try different Kc/tauI settings Can you get underdamped response? What is response to step change in R(t) to 60%?

Model of Stirred Tank Heater CBE / MET 433 Goal: set up models to simulate and see effect of tuning parameters 1st principles (Chaps 3 – 6); transfer functions (just looked at this a bit) process simulators (AspenPlus Dynamics; CBE 450/550 class)

SAVE your Polymath and Loop Pro Developer Models !!