Model based monitoring of large scale processes in
process industry
Project members: R. Bos, P.M.J. Van den Hof, X.J.A. Bombois
Sponsored by:
TNOTPD
In process industry a lot of time and effort is spent on modelling
large scale processes using first principles relations. The resulting
models usually consist of a large set of nonlinear partial
differential equations. These equations usually cannot be solved
analytically, which means they have to be solved numerically on a fine
spacial grid. The models can be converted to the familiar statespace
form by assigning one or more states to each point on the spatial
grid. Solving the equations on this grid tends to be computationally
intensive.
Before we can use these first principles models for monitoring
purposes, we face three main problems. The first problem is that the
number of states in the model is often very large. A second problem is
that the stateequations are very computationally intensive. The final
obstacle is that these first principle models tend to be nonlinear.
These difficulties cause that standard solutions to monitoring, such
as Kalman filtering cannot be used. In this project we will attempt
to find alternative strategies for monitoring process
variables.
The methods and techniques developed in the project are tested in
a case study. The case study in this project consists of a dynamic
model of the dryer section of a paper mill, see Figure
2.
This research is being done in cooperation with the Control
Engineering and Process Physics groups of TNOTPD in Delft.
Next: Towards experimentbased robust control design
Up: System identification
Previous: System identification
