Quality Control of Polymer Production Processes

1. Quality Control of Polymer Production Processes J. Proc. Control, 10, 135-148 (2000) M. Oshima, M. Tanigaki

2. Introduction • Polymer plant operation • Grade transition • Maximizing production • Safe operation of reactor • Quality control for the objectives • On-linesoft sensingand optimal grade changeovercontrol

3. Polymer Production Plant Blender Monomer Catalyst Products Comonomer Reactor Extruder Separator

4. Prospective Control System Production Planning Polymerization Rate Control Optimal Changeover Operation Product Quality Control Product Blending Planning Concentration Ratio Control Reaction Temp. Control Online Property Sensor Product Blending and Storing Scheduling Pressure Control Material Feed Rate Control Reactor Level Control Process Monitoring Polymerization Reactor Control System Catalyst Processing Polymerization Reactor Separation Extruder Blending Storing

5. Needs for Quality Modeling Low-Order Structure Chain Branching LCB SCB Stereoregularity Isotactic Sindiotactic Atactic Distributed Parameter MWD PSD CCD High-Order Structure Morphology Molecular Mobility Crystal Structure Polymer Properties Melt Index Density Shear Viscosity Melting Point End-User Properties Color Mechanical property Strength Electrical property Catalyst Processing Polymerization Reactor Separation Extruder Blending Storing Macroscale Micro-scale Residence Time Distribution Process&Plant

6. Basic Structure of Inferential System Product Controller Process Online data Sample Model parameters Quality Lab. On-line Inferential system Infrequently measured data Predicted polymer quality • Mechanistic model derived from first principles • Empirical model derived from lab. data • Black box model by neural nets & statistical methods

7. An Examples of Three Kinds Model • Mechanistic model (McAuley & MacGregor, 1991) • Empirical model (Watanabe et. al., 1993) • Neural net model (Koulouris, 1995)

8. MI Estimation by Models Mechanistic model Regression model Neural net model

9. MI Estimation with EKF Estimation by mechanistic model with EKF Estimation by regression model with EKF

10. Risk of Extrapolation Regression model Neural net model Mechanistic model Learning data

11. Grade Changeover Operation H2 Pattern A Grade B Pattern B Grade A Grade B Grade A Grade A Grade B 1. Not time but grade optimal operation 2. Runaway reaction Instantaneous grade

12. Control System • Iterative open-loop optimization • A new optimal trajectory is recomputed • The first input action is implemented at every new measurements • Combination of FF&FB controllers • A optimal trajectory is pre-calculated of both MV & CV • MV is introduced to the plant in a FF manner • CV is deviated from the desired optimal trajectory, FB controller is activated to compensate the deviation

13. Results of Control Manual operation Control is activated

14. Optimal Blending Q14 Q13 Q12 Q11 Reactor control is not good enough to satisfy the customer’s demands Q(t) Tank 1 Tank 2 Tank 3 Tank 4 Q44 Q33 Q43 Q21 Q32 Q42 Q21 Q31 Q41 MILP Problem

15. Blending Optimization Result

16. Conclusion • Integration of process control, sensing and optimization is indispensable • Most important factor is quality modeling