Www.cybernetica.no Models for online control of batch polymerization processes. Student:Fredrik Gjertsen Supervisor, NTNU:Prof. Sigurd Skogestad Supervisor,

Slides:



Advertisements
Similar presentations
Computer English For Computer Major Master Candidates
Advertisements

Implementation of MPC in a deethanizer at the Kårstø Gas plant
Isoparametric Elements Element Stiffness Matrices
Aug 9-10, 2011 Nuclear Energy University Programs Materials: NEAMS Perspective James Peltz, Program Manager, NEAMS Crosscutting Methods and Tools.
Mathematical Modeling Overview on Mathematical Modeling in Chemical Engineering By Wiratni, PhD Chemical Engineering Gadjah Mada University Yogyakarta.
Genetic Algorithms for Real Parameter Optimization Written by Alden H. Wright Department of Computer Science University of Montana Presented by Tony Morelli.
Specialization project 2012 Temperature control of an unstable chemical reactor By Ola Sæterli Hjetland Supervisors: Sigurd Skogestad, Krister Forsman.
Computer aided design and analysis of hybrid processes P. T. Mitkowski, G. Jonsson, R. Gani CAPEC Department of Chemical Engineering Technical University.
Progress Presentation. Tasks Completed The tasks that were completed in the last week are: The tasks that were completed in the last week are: The implementation.
Friday 3/5/20021 Metallocene Catalyzed Liquid-Pool Polymerization in a Continuous HSR DPI # 114 Mohammad Al-haj Ali DCP\IPP Groups Chemical Engineering.
CH 1 Introduction Prof. Ming-Shaung Ju Dept. of Mechanical Engineering NCKU.
Computer Assisted Process Design---HYSYS Bo Hu. Introduction HYSYS is only one process simulation program out of a number. Steady State Processes ASPEN.
Control of floor heating process Siri Hofstad Trapnes Supervisors: Sigurd Skogestad and Chriss Grimholt Direct heating in the floor and room Keep the temperature.
Large scale data flow in local and GRID environment V.Kolosov, I.Korolko, S.Makarychev ITEP Moscow.
PPT 206 Instrumentation, Measurement and Control SEM 2 (2012/2013) Dr. Hayder Kh. Q. Ali 1.
BAB 11 : Prototyping. Dira Ernawati, ST. MT - P3 2 Prototyping An approximation of the product along one or more dimensions of interest –Industrial designers.
Process Flowsheet Generation & Design Through a Group Contribution Approach Lo ï c d ’ Anterroches CAPEC Friday Morning Seminar, Spring 2005.
Peter Singstad Trondheim, Norway 1. 2 Intensifying a 100 year old process: Control of emulsion polymerisation Invitation to the COOPOL final dissemination.
Energy Saving Improvements for Industrial Ovens Gary Nola, Master Student Claudia Fajardo, Ph.D. David Meade, Ph.D. 4/13/2011.
New M&S Curriculum: The Emerging Strategy Dr. Wayne Summers TSYS Department of Computer Science Columbus State University.
Modeling & control of Reactive Distillation
Travis Dean Thesis Advisor: Clark Savage Turner Cal Poly, CSC Department.
Background Subtraction for Urban Traffic Monitoring using Webcams Master Graduation Project Progress Presentation Supervisor: Rein van den Boomgaard Mark.
Equation-Free (EF) Uncertainty Quantification (UQ): Techniques and Applications Ioannis Kevrekidis and Yu Zou Princeton University September 2005.
Packaging & Distribution Project Summary Report Project Name: Brief Project Description: The Packaging and Distribution Project can deal with three different.
Communication Networks (Kommunikationsnetværk) Specialisations: Distributed Application Engineering Network Planning & Management Ole Brun Madsen Professor.
Models for on-line control of batch polymerization processes Student:Fredrik Gjertsen Supervisor, NTNU:Prof. Sigurd Skogestad Supervisor,
CompSci Self-Managing Systems Shivnath Babu.
Simple Linear Regression. The term linear regression implies that  Y|x is linearly related to x by the population regression equation  Y|x =  +  x.
Hongna Wang Nov. 28, 2012 Journal Report About CFD.
J M Lopez 1 (of 17) 7 th Workshop on Fusion.., Frascati March, Simulator of the JET real-time disruption predictor J.M. Lopez*, S. Dormido-Canto,
3DFM – Agnostic Tracking of Bead Position CISMM: Computer Integrated Systems for Microscopy and Manipulation Project Investigators: Kalpit Desai, Dr. Gary.
CONTROL ENGINEERING IN DRYING TECHNOLOGY FROM 1979 TO 2005: REVIEW AND TRENDS by: Pascal DUFOUR IDS’06, Budapest, 21-23/08/2006.
ICAT, November
Håkon Dahl-Olsen, Sridharakumar Narasimhan and Sigurd Skogestad Optimal output selection for batch processes.
Fault Tolerance Benchmarking. 2 Owerview What is Benchmarking? What is Dependability? What is Dependability Benchmarking? What is the relation between.
Adaptive Feedback Scheduling with LQ Controller for Real Time Control System Chen Xi.
Master’s Degree in Computer Science. Why? Acquire Credentials Learn Skills –Existing software: Unix, languages,... –General software development techniques.
1 Modelling of Biodiesel Production Marianne Øien Supervisors: Sigurd Skogestad and Chriss Grimholt.
Internship Report Adrian urcanu Third Consortium Meeting.
At what level do I trust the outcomes of the model? Verification Calibration Validation Exploration of the model structure. the activity of adjusting the.
Large scale data flow in local and GRID environment Viktor Kolosov (ITEP Moscow) Ivan Korolko (ITEP Moscow)
ERP and Related Technologies
Lhoist Business Innovation Center, Nivelles, Belgium Lhoist R&D - Environment Team Master Thesis Alain BRASSEUR May 5 th, 2015 – Nivelles.
Using COMSOL for Chemical Reaction Engineering Your name COMSOL.
Mathematical modeling of a polymerization reactor for kinetic parameter estimation Adriano G Fisch 14/10/2015.
Dr. Mingheng Li’s Senior Projects Highlights All related to industrial energy systems and water desalination. Focus on chemical engineering.
The Challenges of ESRC 1+3 l Finding students who are eligible l Enhanced content of training l Avoiding qual/quant polarisation l Assessment of learning.
ECONOMIC PLANTWIDE CONTROL USING COMMERCIAL PROCESS SIMULATION SOFTWARE PROJECT STATUS ADRIANA REYES LÚA SUPERVISOR: SIGURD SKOGESTAD CO-SUPERVISOR: VLADIMIROS.
Crude Oil Refinery Heat Exchanger Network
TM 720: Statistical Process Control DMAIC Problem Solving
ACOMP Automatic Continuous Online Monitoring of Polymerization reactions ACOMP allows comprehensive, model-independent, on-line monitoring of monomer and.
Panel Discussion: Discussion on Trends in Multi-Physics Simulation
Dr. Mingheng Li’s Senior Projects
OPERATIONS RESEARCH.
Thesis Objectives by Rishabh Lala
SAL Engineering & Technical Institute
PROCESS CONTROL AND COMPUTATIONAL BIOLOGY RESEARCH TOPICS Prof
Mathematical Models for Simulation, Control and Testing
By Debashis Mishra “Asst. Prof.” CMRTC/ JNTUH, Hyderabad.
Two-level strategy Vertical decomposition Optimal process operation
Modelling and Validation
CHEN 4903 Introduction.
יוסי שדמתי רק איכות מניהול סיכונים לאימות ותיקוף תהליכי הרכבה From Risk Management to Processes Validation יוסי.
Modeling of a CO2-stripper
ورود اطلاعات بصورت غيربرخط
Presentation on CAPSTONE PROJECT presented by Cdr MD Masum-ul-Haque, (E), BN ME DEPT MILITARY INSTITUTE of SCIENCE and TECHNOLOGY (MIST)
Simplified First Principle Model for Severe
In silico optimization and experimental validation of analytical properties of protosensors performing robust multiplexed biosensing and logic In silico.
PID control of unstable chemical reactor
Presentation transcript:

Models for online control of batch polymerization processes. Student:Fredrik Gjertsen Supervisor, NTNU:Prof. Sigurd Skogestad Supervisor, external:Peter Singstad, Cybernetica AS Background: Summer internship –Copolymerization reactor model written in the Modelica programming language. Main goal: Offline parameter estimation –The established models are mainly made from first principles, and model validation is required in order to verify the model. –Use experimental data together with the Cybernetica ModelFit software to improve the model. Ultimate scope: Obtain a high-end model for which an MPC controller can be designed, using online estimation and optimization. Controller design is left for masters thesis.

The copolymerization model Two-phase emulsion copolymerization of styrene and butyl acrylate Quite comprehensive model –~ 30 states –~ 35 parameters Considerations with respect to computational time must be done –Implementation in a control system 2

Progress Learn to use software for parameter estimation –Theory of estimation Acquire experimental data from one or more reactors (labscale or industrial scale). Perform off-line parameter fitting –Kinetic adjustment factors –Heat transfer properties 3