QUALITY CONTROL OF POLYETHYLENE POLYMERIZATION REACTOR M. Al-haj Ali, Emad M. Ali CHEMICAL ENGINEERING DEPARTMENT KING SAUD UNIVERSITY.

Slides:



Advertisements
Similar presentations
Case Studies Instrumentation and control Dr. –Ing. Naveed Ramzan
Advertisements

U D A Neural Network Approach for Diagnosis in a Continuous Pulp Digester Pascal Dufour, Sharad Bhartiya, Prasad S. Dhurjati, Francis J. Doyle III Department.
General Concepts of Bioprocess Modeling &
CHE 185 – PROCESS CONTROL AND DYNAMICS
Université de Lyon- CNRS-LAGEP, France Paper C-090 Model Predictive Control of the Primary Drying Stage of the Drying of Solutions.
Department of Electrical Engineering Southern Taiwan University Robot and Servo Drive Lab. 2015/5/19 Reduction of Torque Ripple Due to Demagnetization.
Pressure drop in Packed Bed Reactors Chemical Reaction Engineering I Aug Dec 2011 Dept. Chem. Engg., IIT-Madras.
PROCESS INTEGRATED DESIGN WITHIN A MODEL PREDICTIVE CONTROL FRAMEWORK Mario Francisco, Pastora Vega, Omar Pérez University of Salamanca – Spain University.
Sam Pfister, Stergios Roumeliotis, Joel Burdick
INTEGRATED DESIGN OF WASTEWATER TREATMENT PROCESSES USING MODEL PREDICTIVE CONTROL Mario Francisco, Pastora Vega University of Salamanca – Spain European.
Kraft Pulping Modeling & Control 1 Control of Continuous Kraft Digesters Professor Richard Gustafson.
Friday 3/5/20021 Metallocene Catalyzed Liquid-Pool Polymerization in a Continuous HSR DPI # 114 Mohammad Al-haj Ali DCP\IPP Groups Chemical Engineering.
Enhanced Single-Loop Control Strategies
Process and Tools Integration Introduction to Operability and Control of Integrated Plants 8. August 2005 Sten Bay Jørgensen CAPEC - Department of Chemical.
NORM BASED APPROACHES FOR AUTOMATIC TUNING OF MODEL BASED PREDICTIVE CONTROL Pastora Vega, Mario Francisco, Eladio Sanz University of Salamanca – Spain.
Stephen J Dodds Professor of Control Engineering School of Computing and Technology University of East London, UK MSc in Computer Systems Engineering Material.
Non Isothermal Chemical Reactor ChE 479 Alexander Couzis.
Tracking using the Kalman Filter. Point Tracking Estimate the location of a given point along a sequence of images. (x 0,y 0 ) (x n,y n )
Model Predictive Controller Emad Ali Chemical Engineering Department King Saud University.
Dynamics & Control Processes Modeling and Control of Molecular Weight Distribution in a Liquid-phase Polypropylene Reactor Mohammad Al-haj Ali, Ben Betlem,
Estimation and the Kalman Filter David Johnson. The Mean of a Discrete Distribution “I have more legs than average”
Department of Chemical Engineering – Faculty of Engineering
Process Control Computer Laboratory Dr. Emad M. Ali Chemical Engineering Department King SAUD University.
Overall Objectives of Model Predictive Control
1 Jordan University of Science and Technology Chemical Engineering Department “Modeling & Control of Continuous Fluidized Bed Dryers” BY MOHAMMAD AL-HAJ.
Performance Optimization of the Magneto-hydrodynamic Generator at the Scramjet Inlet Nilesh V. Kulkarni Advisors: Prof. Minh Q. Phan Dartmouth College.
MESA LAB IFAC’14 Paper Review (On selected two papers) Zhuo Li MESA LAB MESA (Mechatronics, Embedded Systems and Automation) LAB School of Engineering,
CSE 425: Industrial Process Control 1. About the course Lect.TuLabTotal Semester work 80Final 125Total Grading Scheme Course webpage:
The University of Memphis Using MERLOT Learning Objects in Mechanical Engineering Dr. Edward H. Perry Department of Mechanical Engineering The University.
Integration of Design and Control : Robust approach using MPC and PI controllers N. Chawankul, H. M. Budman and P. L. Douglas Department of Chemical Engineering.
Stochiometry of Real Combustion P M V Subbarao Professor Mechanical Engineering Department A First Step to Understand &Control Rate of Heat Release ….
Complete Pose Determination for Low Altitude Unmanned Aerial Vehicle Using Stereo Vision Luke K. Wang, Shan-Chih Hsieh, Eden C.-W. Hsueh 1 Fei-Bin Hsaio.
Models for on-line control of batch polymerization processes Student:Fredrik Gjertsen Supervisor, NTNU:Prof. Sigurd Skogestad Supervisor,
OPTIMAL PERIODIC OPERATION OF REVERSE OSMOSIS DESALINATION UNITS A. Ajbar, K. AlHumaizi, E. Ali Chemical Engineering Dept. King Saud University Saudi Arabia.
Identifying Applicability Domains for Quantitative Structure Property Relationships Mordechai Shacham a, Neima Brauner b Georgi St. Cholakov c and Roumiana.
SAFETY ANALYSIS WITH MODEL-BASED DYNAMIC SIMULATION ON MOBILE DEVICES Mordechai Shacham and Michael Elly Ben Gurion University of the Negev Beer-Sheva,
MODEL ERROR ESTIMATION EMPLOYING DATA ASSIMILATION METHODOLOGIES Dusanka Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University.
Optimal Operation of a Wastewater Treatment Unit Using Advanced Control Strategy Emad Ali Chemical Engineering Department King Saud University.
Aircraft Windshield Failures Statistical Methods for Reliability Engineering Professor Gutierrez-Miravete Erica Siegel December 4, 2008.
Adaptive Hybrid EnKF-OI for State- Parameters Estimation in Contaminant Transport Models Mohamad E. Gharamti, Johan Valstar, Ibrahim Hoteit European Geoscience.
بسم الله الرحمن الرحيم Advanced Control Lecture one Mohammad Ali Fanaei Dept. of Chemical Engineering Ferdowsi University of Mashhad Reference: Smith &
OPTIMISATION OF ETHYLENE CRACKER HEMENDRA KHAKHAR.
Chapter 20 1 Overall Objectives of Model Predictive Control 1.Prevent violations of input and output constraints. 2.Drive some output variables to their.
NCAF Manchester July 2000 Graham Hesketh Information Engineering Group Rolls-Royce Strategic Research Centre.
Influence of solar wind density on ring current response R.S. Weigel George Mason University.
Local Predictability of the Performance of an Ensemble Forecast System Liz Satterfield and Istvan Szunyogh Texas A&M University, College Station, TX Third.
Networks in Engineering A network consists of a set of interconnected components that deliver a predictable output to a given set of inputs. Function InputOutput.
Control Systems EE 4314 Lecture 12 February 20, 2014 Spring 2014 Woo Ho Lee
1 Chapter 20 Model Predictive Control Model Predictive Control (MPC) – regulatory controls that use an explicit dynamic model of the response of process.
Robust Nonlinear Model Predictive Control using Volterra Models and the Structured Singular Value (  ) Rosendo Díaz-Mendoza and Hector Budman ADCHEM 2009.
Pressure drop in PBR Lec 10 week 13. Pressure Drop and the Rate Law We now focus our attention on accounting for the pressure drop in the rate law. to.
Dynamic Neural Network Control (DNNC): A Non-Conventional Neural Network Model Masoud Nikravesh EECS Department, CS Division BISC Program University of.
The Unscented Kalman Filter for Nonlinear Estimation Young Ki Baik.
Robust Localization Kalman Filter & LADAR Scans
Temperature Control of An Open-loop Unstable Ethylene to Butene-1 Dimerization Reactor by Emad Ali & Khalid Al-humaizi Chemical Engineering Department.
البحث الثامن بحث منفرد منشور فى مؤتمر دولى متخصص ( منشور التحكيم علي البحث الكامل ) Adel A. Elbaset 14 th International Middle East Power Systems Conference.
Feedforward Control ( 前馈控制 ) Liankui DAI Institute of Industrial Control, Zhejiang University, Hangzhou, P. R. China 2009/04/22.
ERT 321 – Process Control & Dynamics Feedforward & Ratio Control Ms Anis Atikah Ahmad
Problem Solving in Chemical Engineering with Numerical Methods
AGH University of Science
PROCESS CONTROL AND COMPUTATIONAL BIOLOGY RESEARCH TOPICS Prof
Overall Objectives of Model Predictive Control
Enhanced Single-Loop Control Strategies
Christoph J. Backi and Sigurd Skogestad
Nordic Process Control workshop, Telemark, 2009
Masoud Nikravesh EECS Department, CS Division BISC Program
Blood/Subcutaneous Glucose Dynamics Estimation Techniques
Genzer Research Group How does substrate geometry affect the surface-initiated controlled polymerization? Jan Genzer (Department.
ON-LINE TRANSITION BETWEEN INCOMPATIBLE CATALYSTS
© The Author(s) Published by Science and Education Publishing.
Presentation transcript:

QUALITY CONTROL OF POLYETHYLENE POLYMERIZATION REACTOR M. Al-haj Ali, Emad M. Ali CHEMICAL ENGINEERING DEPARTMENT KING SAUD UNIVERSITY

Motivation Production of Various Grades of Polymer Optimum Grade Transition Methods: Open-loop optimization Feedforward-feedback control

Motivation Industrial Practice: Grade is maintained by controlling p M2 /p M1 and p H2 /p M1 Different grades have different gas ratios Direct Measurement of MI and Density Cost effectiveness Soft sensors

Process

Process Model McAuely Model Partial pressure dynamics Bed Temperature dynamics Recycle Temperature dynamics Benda correlation Instantaneous MI and Density correlation Sato et al Model Dynamic MI and Density

Mathematical Equations

Mathematical Equations … (1) (2) (3) (4)

Nonlinear Model Predictive Controller P: Prediction horizon M: Control Horizon  : output weights  : Input weights

Control Horizon

Robustness Additive correction: Kalman filter:

Control Objective Controlled Variables: MI Density Production rate Manipulated Variables: F M1 F M2 F H2 F c

Simulation Results Perfect model  M=1, P=2,  T  hr   =[ ]   =[ ]

Manipulated Variables

Simulation Results Imperfect Model: +10% error in reaction rate constant +5% error in  parameters

Manipulated Variables

Conclusions The settling time for MI ranges between 15 to 26 hours. The settling time for the polymer density ranges between 16 to 18 hours. Fastest response: T=0.1, P=M,  =[ ] large gas inventory, long residence times, and broad residence time distributions Molecular weight distribution control

Thank you

Supplementary results