Modelling for Process and Control Design
The integration of several processing aspects early into process design is an obvious aim to reduce the
need for iteration during process development. If ideal CAPE tools could be developed to handle the
types of information required at different abstraction levels it is indeed conceivable to design processes
sequentially. For this purpose suitable mathematical models are required to provide key information
about the material properties and the phenomena in possible processing steps in production facilities.
However also models to represent the desired quality properties of the products must be available for
such an integrated methodology to become possible.
The designer’s intentions with an engineering design and its operations design are to develop processes
and systems to achieve a desired functionality. Combining physics based mathematical models and
formalized models of plant purposes and functions seem to be intuitively obvious, however this link is
usually not made explicitly available in traditional system modelling. For data driven modelling the link
between system model and plant purposes is perhaps even weaker. This presentation intends to
present recent results on formalizing links between process or their behavioural models and formalized
models of plant purposes thereby pointing at directions for promising research endeavors.
The outset is taken from a Functional modeling paradigm called Multilevel Flow Modelling (MFM), Lind
(1994, 1999) which links the modelling purposes with their phenomenological realization. This linkage is
achieved by systematically representing the plant purposes and their derived sub-goals and functions
within a multilevel abstraction framework and subsequently designing the means to achieve each of the
(sub-) purposes. Starting from a MFM model a process plant may be conceptually designed and a
qualitative operations model may be developed. Subsequently mathematical models may be developed
when the means for achieving the desired functionality are specified during the design process.
Mathematical models of plant physics can then be used for detailed design and optimization. MFM
provides a systematic framework based on the notion of means-end abstraction hierarchies. Thereby
functional modeling provides a scientific basis for managing complexity. A Functional modelling
framework may be implemented to facilitate model development and application in a computer
environment. Defining means-end causal relations makes it possible to perform qualitative causal
reasoning in a MFM. Thereby it is possible to develop fault trees and consequence trees and to perform
goal reasoning to develop goal trees. These capabilities of functional models extend the application
potential of Functional Modelling significantly beyond that of conventional mathematical modeling
representing physical phenomena.
Functional models clearly are useful for many different purposes. A number of applications have been
illustrated recently. One such example is to model the function of regulatory networks in
microorganisms thereby illustrating how functions at many levels of means-end abstraction levels are
combined to achieve a desired functionality in nature, Gernaey et al (2006). Another example is to
facilitate HAZOP analysis of an integrated chemical plant through a functional process plant model to
reason about causes and consequences of deviations in process plants states, Rossing et al (2008). A
HAZOP study can reveal glitches in process design which subsequently can be mediated by process
design or control design modifications; thereby a HAZOP study may be used for revealing the desired
functionality of a control system for ensuring safe plant operation. A potential future usage of functional
modeling may be to represent a process design and its potentially possible control designs and thereby
enabling reasoning on simultaneous process and control designs. This could include paying proper
regard to the HAZOP analysis results as reflected in the control system requirements. This research
points towards the possibility for development of an operations management system specification for
ensuring safe operation.
Consequently it can be highly advantageous for engineering to develop functional modelling as a
separate scientific discipline for handling the early phase of process model development in a
scientifically sound manner. Several aspects of functional modelling requires special attention, one
particular aspect is the issue of identification of functional models. In short the functional modeling
perspective indeed may be used in many different functions in engineering.
References
Gernaey K.V., Lind M. and Jorgensen S.B. (2006) Towards understanding the role and function of regulatory
networks in microorganisms. In: Puigjaner, L. and Heyen G. (Eds.) Computer Aided Process \& Product Engineering,
vol. 1, Chapter 7, pp. 223-264. Wiley-VCH, Weinheim (Germany).
Jorgensen, S. B.; D. Bonné and L. Gregersen, 2006a, "Monitoring and Control of Batch Processes", Chapter in
“Batch Processes”. Ed. K. Korovessi & A. Linninger. Marcel Dekker, pp. 419-462
Jorgensen, S. B.; D. Bonné, 2006b "Systematic Methodology for Reproducible and Optimizing Batch Operation"
Computer Aided Chemical Engineering, 21B, pp. 1275-1280
Lind, M. , 1994 "Modeling Goals and Functions of Complex Industrial Plant", Applied Artificial Intelligence, 8, pp
259--283
Lind, M. , 1999 "Plant Modeling for Human Supervisory Control", Transactions of the Institute of Measurement and
Control, 21, pp 171—180
Mendoza, O.B.; J. Petersen, S. B. Jorgensen, 2008, "Multirate Process Data Assimilation using Extended Kalman
Filtering". IFAC annual Meeting, Seoul Korea.
Rawlings, James B.; Dennis Bonné, John B. Jorgensen, Aswin N. Venkat, and Sten Bay Jorgensen, 2008,
"Unreachable Setpoints in Model Preditive Control", Transactions of Automatic Control, 53 pp. 2209-2215.
Rossing, Netta Liin; Morten Lind, Niels Jensen and Sten Bay Jorgensen, 2008, "A Goal Based Methodology for
HAZOP Analysis", Proceedings of CSEPC conference, Harbin, China, 8-10 September