Model-Based Control of Granulation Using Empirical and Theoretical Process Modeling

Publication Reference: 
51-06
Author Last Name: 
Doyle
Authors: 
PI Francis J. Doyle
Report Type: 
FRR
Research Area: 
Characterisation
Publication Year: 
2011
Publication Month: 
11
Country: 
United States

Executive Summary

This report summarizes the work done over six years in IFPRI project 51, ¡§Multi-Scale Approach to Modeling and Control of Granulation Processes¡¨. This project covers the research efforts of 4 research students/post-docs at UCSB, as well as collaborative endeavors with both Imperial College, and the Procter and Gamble Company. The studies span thefollowing technical sub-themes:

Efficient computational methods for multi-dimensional population balance models

Empirical methods for identification of granulation models for process control

Model-based control design for a granulation process

Pilot plant testing of model identification and control methods

The subspace modeling work in this study provides insight on improved control and design (including measurement selection) of a granulation processes. Two different control strategies (MPC and PID) are evaluated on an experimentally validated granulation model. This model is based on earlier work done at The University of Sheffield, UK and Organon, The Netherlands. The controller model for the Model Predictive Controller is a linearized state space model, derived from the nonlinear DPB model. It has the four process variables from the experimental design and a feed ratio as input variables. Since the DPB model describes the whole size distribution (GSD), different sets of output variables were chosen and compared. When measuring controller performance based on the full granule size distribution, a PID setup can actually produce results that fluctuate more than the open-loop response. An MPC controller improves stability on both process outputs and the full granule size distribution.