Selection of process control structures through Mathematical programming
Event details
| Date | 17.11.2016 |
| Hour | 10:15 › 11:15 |
| Speaker | Ioannis K. Kookos |
| Location | |
| Category | Conferences - Seminars |
The design of chemical processes is carried out with the aim to generate an optimal plant which satisfies all operating constraints in a constantly changing environment. Continuous processes have traditionally been designed to achieve optimality around a nominal operating point, based on the assumption that an appropriate and efficient control system will be added at a later stage. This sequential approach to plant design and control can result in suboptimal plants which may be unable to meet operational requirements. One approach to tackle the combined operability and economics objectives is to assess the effect of disturbances on process economics. As different process designs and control system designs have different dynamic characteristics the aim is to design them simultaneously so as to achieve robust economic performance, i.e., reduce sensitivity of economic performance to disturbances.
A systematic method, that is known as the back-off methodology for simultaneous design and control, based on these ideas has been proposed by J.D. Perkins at Imperial College and was latter refined by Perkins and his co-workers. The back-off methodology is based on the assumption that the optimal steady state operating point is defined by the intersection of active constraints. However, perfect control of the active constraints cannot be achieved and, to make things even worse, the set of active constraints can vary under dynamic conditions and/or active constraints may not be measurable. In addition, there is usually available a large number of potential manipulated variables and secondary measurements, consideration of which results in an exploding number of possible combinations that need to be considered before an optimal structure is established.
In the mathematical formulation of the classical back-off methodology use is made of linear control theory combined with a linear model of process economics, to exploit the benefits of both linear control theory and linear programming so as to quickly evaluate alternative control structures and locate the optimal structure and possibly optimal design. The obvious shortcoming is related to the loss of accuracy that accompanies the use of linear models that can be severe for plants with highly nonlinear dynamics.
In this presentation the back-off methodology is first reviewed and then the necessary modifications for the incorporation of a nonlinear model of process economics are described. To evaluate the usefulness of the new systematic methodology two case studies will be considered: an evaporator and a ternary reactive distillation with a chemically inert component in the feed. The results obtained for both case studies demonstrate the potential of the proposed methodology for solving challenging control structure selection problems in an efficient and effective way.
Bio: Associate Professor Ioannis K. Kookos holds a BSc degree in Chemical Engineering from the NTU of Athens and an MSc (DIC) and PhD from Imperial College (IC) London, Centre for Process Systems Engineering. He has been a postdoctoral researcher at IC and then a lecture at University of Manchester, Institute of Science & Technology (UMIST). In 2005 he joined the Dept of Chemical Engineering at the University of Patras, Greece as an Assistant Professor.
His research interests lie in the fields of process design, modeling and control. More recently his research has focused on the development of mathematical programming techniques for the optimal design of bio-refinery systems for the production of platform chemicals and bio-fuels and the implementation of circular economy for the valorization of industrial waste streams. He is a co-author in approximately 50 peer reviewed papers and 30 conference publications and author of 3 books (in Greek). He has also supervised more that 40 Diploma, MSc and PhD students.
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