A Grid-based Environment for Multi-parametric PSE Applications: Batch Plant Design Case Study
Antonin Ponsich (Laboratoire de Génie Chimique, UMR CNRS 5503)Iréa Touche (Laboratoire de Génie Chimique, UMR CNRS 5503)
Catherine Azzaro-Pantel (Laboratoire de Génie Chimique, UMR CNRS 5503)
Serge Domenech (Laboratoire de Génie Chimique, UMR CNRS 5503)
Luc Pibouleau (Laboratoire de Génie Chimique, UMR CNRS 5503)
Michel Daydé (Institut de Recherche en Informatique de Toulouse, IRIT UMR 5505)
Abstract:
Complex optimization problems are of high interest for Process
Systems Engineering. The selection of the relevant technique for the
treatment of a given problem has already been studied for batch plant
design issues. Classically, most works reported in the dedicated literature
yet considered item sizes as continuous variables. In a view of realism, a
similar approach is proposed in this paper, with discrete variables for representing
equipment capacities, which leads to a combinatorial problem.
For this purpose, a Genetic Algorithm was used, which is multiparametric
by nature and a grid approach is perfectly relevant to this case study,
since the GA code must be run several times, with different values of
some input parameters, to guarantee its stochastic nature. This paper
is devoted to the presentation of a grid-oriented GA methodology. Some
significant results are highlighted and discussed.
Keywords:
Large Scale Simulations in CS&E (earth, environment, finance, geoscience, engineering, ...)
