Dahlin Control of a Wood Flake Conveyor Dryer

Slides:



Advertisements
Similar presentations
PID Implementation Issues
Advertisements

CHE 185 – PROCESS CONTROL AND DYNAMICS
PID Controllers and PID tuning
ADVANCED MOTION CONTROL First and Second Order Motion by Peter Nachtwey.
Neural Simulation and Control.. Simulation Input/Output models Proces u(k) y(k+d) d(k) The NARMA model:
CHE 185 – PROCESS CONTROL AND DYNAMICS
CHE 185 – PROCESS CONTROL AND DYNAMICS
Process Control: Designing Process and Control Systems for Dynamic Performance Chapter 6. Empirical Model Identification Copyright © Thomas Marlin 2013.
Hur får man system att uppföra sig som man vill? Reglerteknik och intressanta tillämpningar Carl-Fredrik Lindberg,
Electric Drives FEEDBACK LINEARIZED CONTROL Vector control was invented to produce separate flux and torque control as it is implicitely possible.
CHE 185 – PROCESS CONTROL AND DYNAMICS
CIMExcel Software Inc. Slide 1 CIMExcel Software Inc. Greg Yorke, Ph.D, P.Eng. Control Systems Engineering Company located in Vancouver Modeling, Simulation,
Lecture 7: PLC: Review Questions
280 SYSTEM IDENTIFICATION The System Identification Problem is to estimate a model of a system based on input-output data. Basic Configuration continuous.
Practical Process Control Using Control Station
PID Temperature Controller
Overall Objectives of Model Predictive Control
Unit 3a Industrial Control Systems
Proportional/Integral/Derivative Control
SIGCOMM 2002 New Directions in Traffic Measurement and Accounting Focusing on the Elephants, Ignoring the Mice Cristian Estan and George Varghese University.
PSE and PROCESS CONTROL
ELG 4152 :Modern Control Winter 2007 Printer Belt Drive Design Presented to : Prof: Dr.R.Habash TA: Wei Yang Presented by: Alaa Farhat Mohammed Al-Hashmi.
Model Reference Adaptive Control (MRAC). MRAS The Model-Reference Adaptive system (MRAS) was originally proposed to solve a problem in which the performance.
Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 1 UNITRONICS D erivative P I roportional.
CONTROL ENGINEERING IN DRYING TECHNOLOGY FROM 1979 TO 2005: REVIEW AND TRENDS by: Pascal DUFOUR IDS’06, Budapest, 21-23/08/2006.
Chapter 20 1 Overall Objectives of Model Predictive Control 1.Prevent violations of input and output constraints. 2.Drive some output variables to their.
Automatic Control System V. Compensation. If the satisfactory design of the process dynamics can’t be obtained by a gain adjustment alone, then some methods.
Control systems KON-C2004 Mechatronics Basics Tapio Lantela, Nov 5th, 2015.
Lecture 25: Implementation Complicating factors Control design without a model Implementation of control algorithms ME 431, Lecture 25.
Implementing PID on a microcontroller
Feedback Controllers Chapter 8
CISSP Common Body of Knowledge Review by Alfred Ouyang is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
Christopher Price Component Level HVAC Control NIST Project
ChE 433 DPCL Model Based Control Smith Predictors.
Standards Certification Education & Training Publishing Conferences & Exhibits Automation Connections ISA EXPO 2006.
Cascade Control Systems (串级控制系统)
A Presentation on Adaptive Neuro-Fuzzy Inference System using Particle Swarm Optimization and it’s Application By Sumanta Kundu (En.R.No.
A PID Neural Network Controller
ChE 433 D(  V )PCL Highlights What I want you to take away on a 3 x 5 card.
Date of download: 7/12/2016 Copyright © ASME. All rights reserved. From: Computer Simulation of Drying of Food Products With Superheated Steam in a Rotary.
Date of download: 9/20/2016 Copyright © ASME. All rights reserved. From: Simulation and Optimization of Drying of Wood Chips With Superheated Steam in.
Workshop for Flipped Class
PLC Terminology and Application
Presentation at NI Day April 2010 Lillestrøm, Norway
PID tuning & UNICOS PID auto-tuning
SOUTHERN TAIWAN UNIVERSITY ELECTRICAL ENGINEERING DEPARTMENT
PID Temperature Controller
PID Controllers Jordan smallwood.
Okwuchi Emereole and Malcolm Good, University of Melbourne
Performance Supervision System
Course PEF3006 Process Control Fall 2017 Lecture: Process dynamics
Date of download: 12/27/2017 Copyright © ASME. All rights reserved.
Overall Objectives of Model Predictive Control
PID Temperature Controller
Av Finn Aakre Haugen IA3112 Automatiseringsteknikk og EK3114 Automatisering og vannkraftregulering Høstsemesteret 2017 Gain scheduling.
Auto-tuning of PID controllers
Changing between Active Constraint Regions for Optimal Operation: Classical Advanced Control versus Model Predictive Control Adriana Reyes-Lúa, Cristina.
Course PEF3006 Process Control Fall 2018 Lecture: Process dynamics
Enhanced Single-Loop Control Strategies
Features of PID Controllers
Chapter 3 – Combinational Logic Design
Brief Review of Control Theory
Nordic Process Control workshop, Telemark, 2009
PLC 5 and ControlLogix Subroutine Parameters
A practical approach for process control optimization during start-up
Av Finn Aakre Haugen IA3112 Automatiseringsteknikk og EK3114 Automatisering og vannkraftregulering Høstsemesteret 2018 Gain scheduling.
Feedback Controllers Chapter 8
How a control system may become unstable
How a control system may become unstable
A Tutorial Overview Proportional Integral Derivative.
Presentation transcript:

Dahlin Control of a Wood Flake Conveyor Dryer Robert Spring, P. Eng., CAP

Presenter Robert Spring, P.Eng, CAP A specialist in heat energy and wood drying. Interests include optimization, sensors and advanced controls. Work experience in the mineral processing, pulping, paper-making and wood products industries.

Why Dry?

Conveyor Dryers – Transportation Delay Transport Delay = 15 minutes Energy Usage = 10,000 Households

Dryer Schematic

PID versus Dahlin - Simulations

“Not-dumb” Use of Information   Error Past Move Dead-time Fixed Parameters

Design the Trajectory

Synthesis Steps – Model Identification Process Gain Process Time Constant   Dead-time Control Time Interval

Synthesis Steps – Controller Implementation  

Adaptive Dead-time    

Bumpless Transfer and Anti-windup  

Bumpless Transfer and Anti-windup  

Feed-forward

Feed-forward  

Disturbance Model Time Constant Gain   Dead-time

Feed-forward  

Benefit of Feed forward - Simulations

Before and After Dahlin Control σ=1.8% σ=0.7%

PLC Implementation

Ladder Logic Sample  

Structured Text Sample // b*x(t) NewSpeed := Lambda *OldSpeed; // DelayAdj changes with the speed Index := DelayAdj; // Reference into stored vector of past speeds SpeedDelayedN := Speed_Time_Series[Index]; // (1-b)*x(t-n) NewSpeed := NewSpeed+(1.0-Lambda)*SpeedDelayedN;  

Packaging as a Subroutine or Add-on

Conclusions – Dahlin Control Suited for: long dead-time variable dead-time feed-forward compensation Simple to program (ladder logic or structured text) Single tuning parameter

Result: Make Every Board the Same