These in turn are economically important and essential in the competitive industries such as distillation, refinery, crystallisation, fermentation, bioreaction, neutralisation, polymerisation, etc. Modelling, identification and simulation are routinely applied in modern chemical industries for dynamic analysis and design of control systems. Precise models of processes play a key role in understanding dynamics, predicting future outputs and revising existing designs. Models represented by differential equations give a good insight into the processes according to chemical and physical laws governing the processes. Simplified and linearised versions of the models can be directly used in model-based control design such as internal model control and model predictive control.
Neural networks are powerful non-linear models.
They have been recently studied and applied to inferential modelling and process modelling for quality variables in chemical processes. On-line identification creates possibilities for adaptive control and learning. However, research is still at an early stage. Therefore, papers on non-linear modelling directly for the purpose of control are most welcome for the session. Due to the scale of the processes in the chemical industry, optimisation can be difficult and attractive.