Process Control Research Group

Description

The Research Group's activities include:

Process Modelling

A synergetic grey-box approach is applied to solve research problems in process control integrating process systems engineering with systems and control theory. The basis of the approach is process modelling and model analysis using first engineering principles. Formal methods of computer science and artificial intelligence are applied to construct, verify, analyse and simplify process models in a rigorous and automated way. These methods are implemented in intelligent computer-aided modelling tools to support the modeller in process model building and analysis. Our interest also includes the investigation of the effect of algebraic, model building and model simplification transformations on the computational and dynamic properties of process models. Work in this area, a joint effort with Prof. Ian Cameron, CAPE Centre, Dept. of Chemical Engineering, The University of Queensland, Brisbane (Australia), resulted in a joint book "Process Modelling and Model Analysis" published by Academic Press, London.

Nonlinear Process Systems

Nonlinear system theory, nonlinear control and diagnosis belong to the most challenging and developing areas in post-modern systems and control theory. Process systems are known to be highly nonlinear and are governed by the basic laws of thermodynamics. Therefore, the grey-box modelling and control of these systems are based on the joint understanding of modern nonlinear system analysis and control methods and the fundamentals of process systems engineering.

Our interest in nonlinear process systems includes:

• nonlinear reachability and stability analysis
• Hamiltonian process systems
• passivation and loop-shaping controllers
• nonlinear controller structure selection based on the analysis of input-output behaviour

Simple but industrially important process systems, such as heat exchangers and fermentation processes are used as case-studies.

Intelligent discrete process control and diagnosis methods apply discrete event system models. Based on our experience in coloured Petri net models, the aim is to find efficient methods and algorithms for automatic generation, verification and hierarchical decomposition of operating (control), safety, diagnostic and maintenance scheduling procedures for process systems. The underlying engineering knowledge is expressed in terms of discrete event dynamic process models.