Research Interests
Process Modelling:
Process models are needed to better analyze and design controllers for processes. We are currently investigating methods for parameter estimation of nonlinear continuous-time systems as well as unstable systems. The development of tuning techniques is being explored for use online for process control.
Models are not perfect; therefore, a key aspect of my research is the implementation of uncertainty analyses to assess process variability under uncertainty. I strongly believe that model uncertainty is essential for designing efficient mathematical tools that can be realistically applied in practice for process improvement.
Process Control:
We develop methods and procedures used by thousands of practitioners, researchers and students around the world for process control system analysis, tuning and training.
We want to find an improved control structure where acceptable operation under all conditions is achieved with constant set points for the controlled variables.
More generally, the idea is to use the model offline to find properties of the optimal solution suited for online implementation.
Current research efforts in our lab seek to extend these methods to such important areas:
Whole-plant modelling, simulation, and control.
Automated controller design and tuning.
Controller performance monitoring.
Multivariable process identification and control.
IoT, Image Processing, Big Data and Machine Learning in Process Industry.