Sorry: This page from 2009 is outdated!
PRMC FAQ: Frequently answered questions:
A process model is defined as an implementation of an algorithm to predict
the behaviour of an open or closed system as sketched below:
The information on the current system state is stored in
the models internal memory and as long as the software
replacement condition is fulfilled, the model can be used to
simulate the reality. Such a process simulation
is a subset
of process modelling as it is an experiment performed on a model.
Computer simulation (Wikipedia)
ontrol is an advanced method of
process control that goes beyond usual PID controllers.
Model predictive controllers rely on dynamic process modells
The models are used to predict the behavior of dependent variables (outputs)
of a dynamical system with respect to changes in the process independent variables (inputs).
In metallurgical processes, independent variables are most often setpoints of
while dependent variables are most often constraints in the process
(e.g., product purity, equipment safe operating limits).
The model predictive controller uses the models and current plant
measurements to calculate future moves in the independent variables
that will result in operation that honors all independent and dependent
variable constraints. The MPC then sends this set of independent
variable moves to the corresponding regulatory controller setpoints
to be implemented in the process.
Having non-linear process modells as described on this site, non linear
MPC set value prediction can be developed, see e.g. our
EAF MPC demonstration
Model Predictive Control (Wikipedia)