This paper presents a large-scale application of the normal vector approach to demonstrate that the complexity of robust dynamic optimization with application to the integration of process and control design can be treated successfully for complex nonlinear systems. The case study further demonstrates that our approach can deal with a multi-dimensional uncertainty space. The normal vector approach is able to automatically identify the worst-case scenarios and find a solution that is optimal with respect to the cost function and robust with respect to path constraints on inputs and states in the presence of parameterized disturbances. The tedious analysis of a large number of different disturbance realizations is not required.
|Number of pages||5|
|Journal||Computer Aided Chemical Engineering|
|State||Published - 2011|
Bibliographical noteFunding Information:
This work has been supported by the German Academic Exchange Service (DAAD-ALECOL), and by the German Research Foundation under grant MA 1188/28-1. . This solver uses first- and second-order tangent-linear and adjoint models of the residual of linear implicit autonomous differential algebraic systems in the context of an extrapolated linearly-implicit Euler scheme. A single integration run of the Tennesse-Eastman process model together with first and secondorder takes roughly 13 seconds on a PC with 3 GHz and 2GB RAM. Therefore the integrated process and control system design problem could be solved.
- Grazing bifurcation
- Normal vector
- Robust optimal design
- Tennessee-Eastman process