Multiobjective multiproduct batch plant design under uncertainty

2006 ◽  
pp. 2249-2254 ◽  
Author(s):  
A. Dietz ◽  
A. Aguilar-Lasserre ◽  
C. Azzaro-Pantel ◽  
L. Pibouleau ◽  
S. Domenech
Keyword(s):  
Design Under Uncertainty ◽  
Plant Design ◽  
Batch Plant

Multiobjective Multiproduct Batch Plant Design Under Uncertainty: Application to Protein Production

2009 ◽  
pp. 1101-1106 ◽  
Author(s):  
Alberto Aguilar-Lasserre ◽  
Rubén Posada-Gómez ◽  
Giner Alor-Hernández ◽  
Guilllermo Cortés-Robles ◽  
Constantino Moras-Sánchez ◽  
...  
Keyword(s):  
Protein Production ◽  
Design Under Uncertainty ◽  
Plant Design ◽  
Batch Plant

Flexibility, reliability and maintenance considerations in batch plant design under uncertainty

1996 ◽  
Vol 20 ◽  
pp. S1209-S1214 ◽  
Author(s):  
E.N. Pistikopoulos ◽  
T.V. Thomaidis ◽  
A. Melin ◽  
M.G. Ierapetritou
Keyword(s):  
Design Under Uncertainty ◽  
Plant Design ◽  
Batch Plant

scholarly journals Ecodesign of Batch Processes: Optimal Design Strategies for Economic and Ecological Bioprocesses

2007 ◽  
Vol 5 (1) ◽  
Author(s):  
Adrian Dietz ◽  
Catherine Azzaro Pantel ◽  
Luc Guy Pibouleau ◽  
Serge Domenech
Keyword(s):  
Genetic Algorithm ◽  
Discrete Event ◽  
Operating Conditions ◽  
Batch Processes ◽  
Stochastic Algorithm ◽  
Plant Design ◽  
Environment Design ◽  
Design Strategies ◽  
Design Variables ◽  
Batch Plant

This work deals with the multicriteria cost-environment design of multiproduct batch plants, where the design variables are the equipment item sizes as well as the operating conditions. The case study is a multiproduct batch plant for the production of four recombinant proteins. Given the important combinatorial aspect of the problem, the approach used consists in coupling a stochastic algorithm, indeed a Genetic Algorithm (GA) with a Discrete Event Simulator (DES). To take into account the conflicting situations that may be encountered at the earliest stage of batch plant design, i.e. compromise situations between cost and environmental consideration, a Multicriteria Genetic Algorithm (MUGA) was developed with a Pareto optimal ranking method. The results show how the methodology can be used to find a range of trade-off solutions for optimizing batch plant design.

scholarly journals Enhanced genetic algorithm-based fuzzy multiobjective strategy to multiproduct batch plant design

2009 ◽  
Vol 9 (4) ◽  
pp. 1321-1330 ◽  
Author(s):  
A.A. Aguilar-Lasserre ◽  
L. Pibouleau ◽  
C. Azzaro-Pantel ◽  
S. Domenech
Keyword(s):  
Genetic Algorithm ◽  
Plant Design ◽  
Batch Plant
Sign in / Sign up

Export Citation Format

Share Document