Multi-objective optimization for chemical processes and controller design: Approximating and classifying the Pareto domain

2006 ◽  
Vol 30 (6-7) ◽  
pp. 1155-1168 ◽  
Author(s):  
Hayley Halsall-Whitney ◽  
Jules Thibault
Keyword(s):  
Controller Design ◽  
Chemical Processes ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Domain

scholarly journals Methodology to Solve the Multi-Objective Optimization of Acrylic Acid Production Using Neural Networks as Meta-Models

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1184
Author(s):  
Geraldine Cáceres Sepulveda ◽  
Silvia Ochoa ◽  
Jules Thibault
Keyword(s):  
Neural Networks ◽  
Acrylic Acid ◽  
Performance Criteria ◽  
Computational Time ◽  
Multi Objective Optimization ◽  
Meta Model ◽  
Multi Objective ◽  
Pareto Domain ◽  
Computationally Intensive ◽  
Time Required

It is paramount to optimize the performance of a chemical process in order to maximize its yield and productivity and to minimize the production cost and the environmental impact. The various objectives in optimization are often in conflict, and one must determine the best compromise solution usually using a representative model of the process. However, solving first-principle models can be a computationally intensive problem, thus making model-based multi-objective optimization (MOO) a time-consuming task. In this work, a methodology to perform the multi-objective optimization for a two-reactor system for the production of acrylic acid, using artificial neural networks (ANNs) as meta-models, is proposed in an effort to reduce the computational time required to circumscribe the Pareto domain. The performance of the meta-model confirmed good agreement between the experimental data and the model-predicted values of the existent relationships between the eight decision variables and the nine performance criteria of the process. Once the meta-model was built, the Pareto domain was circumscribed based on a genetic algorithm (GA) and ranked with the net flow method (NFM). Using the ANN surrogate model, the optimization time decreased by a factor of 15.5.

A Novel Scheduling Approach to Waste Minimization in Process Industry

2010 ◽  
Vol 121-122 ◽  
pp. 143-147
Author(s):  
Xiao Qiang Zhao
Keyword(s):  
Optimal Design ◽  
Optimization Problems ◽  
Chemical Processes ◽  
Production Costs ◽  
Process Industry ◽  
Waste Minimization ◽  
Multi Objective Optimization ◽  
Pollution Load ◽  
Multi Objective

Waste minimization opportunities could be of great importance for decreasing the waste pollution load and production costs. A formulation of multi-objective optimization problems is proposed for the optimal design of chemical processes with waste minimization. As an illustrating example, a problem of optimal design of multipurpose chemical processes was solved. The approach developed is shown to be effective for solving problems of optimal design of chemical processes with waste minimization.

Robust H∞ Controller Design for Aircraft Lateral Dynamics using Multi-objective Optimization and Genetic Algorithms

2008 ◽  
Vol 41 (2) ◽  
pp. 8834-8839 ◽  
Author(s):  
M Giacomán-Zarzar ◽  
R Ramirez-Mendoza ◽  
PJ Fleming ◽  
I Griffin ◽  
A Molina-Cristóbal
Keyword(s):  
Genetic Algorithms ◽  
Controller Design ◽  
Multi Objective Optimization ◽  
Lateral Dynamics ◽  
Multi Objective
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