Dynamic and Steady State Modeling of a LDPE Polymer Process for Application of Advanced Control and Process Monitoring Schemes (Introduction)

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1 University of Alberta & AT Plastics Dynamic and Steady State Modeling of a LDPE Polymer Process for Application of Advanced Control and Process Monitoring Schemes (Introduction) Department of Chemical & Materials Engineering, University of Alberta, Edmonton, Canada Presented by: Ian Alleyne Supervisors: Dr. Shah & Dr. Sundararaj

2 Presentation Guide Motivation for research Description of Polymer Production Process Overview of Control System and Proposed Historian Architecture Expected Steps to Achieve Project Deliverables

3 Motivation for Research Advanced Process Control techniques in recent years have emphasised the value to be gained by using model based methods. Polymer process typically very difficult to model, as a result process control decisions are based on operator experience and general operating procedures. Recent advances in polymer science, reaction engineering, modeling technologies and simulation tools make it possible to develop high fidelity polymer models. Industry realises financial benefits to be gained by using model based techniques for polymer process operations.

4 Process Description This research project would be undertaken at the AT Plastics Edmonton Plant site. This Plant consists of five (5) single stream units the most recent and largest being the 5R EVA Copolymer plant. Production design specifications: Annual capacity of MT Homopolymer and copolymer grades containing up to 28% vinyl acetate Melt Indices ranging 0.3 to 1000 Based on the ICI high pressure low density polyethylene process

5 5R Polymerization Unit Polymer Process Modeling and Advanced Control Ethylene Supply (38bar) Purge Propylene Injection VA Injection Secondary Suction Filter (200um) LP Stock Tank (80m 3, 0.8bar) Booster Compressor (Pignone) Primary Stock Tank (65m 3 ) Primary Compressor (Pignone) Secondary Compressor (Pignone) Return Gas Coolers (51mm, glycol cooled. 3 centrifugal separators) Stirrer Motor (130kW, 1200rpm) Feed Gas Coolers (500m, 34mm, glycol) Rotary Drier (Gala) Dewatering Screen Water Tank Pelletizer (Berstorff) To Vacuum Unit Screen Pack N 2 Changer LP Hopper (9.1m 3 ) Extruder (Berstorff, 560kW, 500mm, 12:1,Water, Vented) Separator (1.8m 3, 30MPa) Product Cooler (81.3m, 34mm, Calflo) Reactor (750L, 150MPa, air heated) Product to Test Hoppers Additive Injection Units Initiator Injection Pumps DJG 1999

6 Resin from 5R Polymerization Plant 5R Product Finishing Polymer Process Modeling and Advanced Control Pellet Transfer Blower Test Hoppers (5te) Lot Transfer Blower Product Devolatilizing Silos (40te) Batch Transfer Blower Fluidized Bed Down-grade Devolatilizing Silos (40te) 6 7 Classifier (Kason) Packaging Silo (40te) Blender (40te Double Cone Tumble Blender) Auto Palletizer and Stretchwrapper Bagging Station (2 spouts) Carton Filling Station Hopper Car Filling Station DJG 1999

7 Process Control System Description The 5R Plant uses a Honeywell TDC 300 Distributed Control System (DCS). This allows operator interface to the system and executes regulatory control on the process loops. To implement online analysis or to collect data for offline analysis a proper historian must be in place. Presently a history module exists on the Honeywell system however this information can only be accessed at the Honeywell Stations. There is a historian existing on the plant, however because of the use of the Modbus Serial Protocol to access the data from the Honeywell system there are performance limitations which limit the number if points to 48, each being accessed every 6 seconds. This proves to be too limited for the online validation of the process model and implementation of an advanced process control scheme. Therefore a modified historian architecture was designed.

8 Historian Architecture Polymer Process Modeling and Advanced Control

9 Project Deliverables The implementation of an advanced process control scheme for polymer manufacturing to achieve lot to lot consistency of the final product to achieve faster and more stable grade transitions (thus giving less off specification product) to achieve higher production throughput and yield optimization system monitoring for early detection of process faults (minimizing downtime)

10 Expected Implementation Procedure Evaluation of available dynamic modeling packages, building of reactor model based on available data (model structure) Validation of model, system for online recursive updates of mechanistic model (model parameters) Modification of Historian for seamless accessing of historical data Implementation of advanced process control techniques for optimization of grade transitions, reduction of off specification product and detection of process faults