Sanderson Building, Lecture Theatre 1, King's Buildings, University of Edinburgh
Process optimization is the method of choice for improving the performance of chemical processes while enforcing the satisfaction of operating constraints. Long considered as an appealing tool but only applicable to academic problems, model-based optimization has now become a viable technology. But even when process models are available, the presence of plant-model mismatch and process disturbances makes the direct use of model-based optimal inputs hazardous. In the past 20 years, the field of “measurement-based optimization” (MBO) has emerged to help overcome this difficulty. MBO encompasses several methods and tools from sensing technology and control theory into the optimization framework. This way, process optimization does not rely exclusively on the (possibly inaccurate) process model but also on process information stemming from measurements. The first widely available MBO approach was the two-step approach that adapts the model parameters on the basis of the deviations between predicted and measured outputs, and uses the updated process model to recompute the optimal solution. Though this approach has become a standard in industry, it has recently been proven that, in the presence of plant-model mismatch, this method is very unlikely to drive the process to optimality. In this talk, I will discuss two of the alternative MBO approaches, to which I had the opportunity to contribute, namely the modifier adaptation approach, well suited for steady-state optimization problems and NCO-tracking, more suited for dynamic optimization problems. The main advantage of these methods lies in their proven ability to converge to the true plant optimal solution, despite structural plant-model mismatch. Two case studies will be presented: (i) modifier adaptation for the constrained maximization of electrical efficiency of an experimental fuel cell stack under random load changes and (ii) NCO-tracking of a 1-ton industrial emulsion copolymerization reactor. Research directions and potential collaborations will also be streamlined.
Gregory Francois received a chemical engineering degree and a DEA in Process Engineering from the Chemical Engineering School of Nancy (ENSIC), France, in 1998, a PhD in Process Systems Engineering from Swiss Federal Institute of Technology of Lausanne (EPFL) in 2004 and a Habilitation Thesis in Process Systems Engineering from the Lorraine University in 2014. He started as a Senior Lecturer in Chemical Engineering at the UoE late 2015. His research interests include steady-state and dynamic process optimisation, optimal control, process modelling and control, with application to a wide range of chemical processes, energy systems (Fuel Cells, Kites, …), bioengineering (Diabetes)… More specifically, he is particularly interested in the methodological development and the application of novel optimisation techniques whereby measurements and feedback are used to compensate for the detrimental effect of parametric and structural plant-model mismatch to the optimisation of real processes. Before joining the University of Edinburgh he occupied several academic positions mainly at EPFL and in France.