A Multiobjective Optimization Algorithm for Fluid Catalytic Cracking Process with Constraints and Dynamic Environments

The optimization problems in a fluid catalytic cracking process with dynamic constraints and conflicting objectives are challenging due to the complicated constraints and dynamic environments. The decision variables need to be reoptimized to obtain the best objectives when dynamic environments arise...

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Bibliographic Details
Published in:Mathematics (Basel) 2024-07, Vol.12 (14), p.2285
Main Authors: Liu, Guanzhi, Pang, Xinfu, Wan, Jishen
Format: Article
Language:English
Subjects:
Catalytic converters
Catalytic cracking
Constraints
Design
Design optimization
dynamic constrained multiobjective optimization
Dynamic response
dynamic response strategy
Energy consumption
Energy industry
Evolutionary algorithms
Feasibility
Fluid catalytic cracking
Fossil fuels
Gasoline
Genetic algorithms
Linear programming
Mathematical models
Multiple objective analysis
offspring generation
Optimization algorithms
Profits
Strategy
Variables
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Summary:The optimization problems in a fluid catalytic cracking process with dynamic constraints and conflicting objectives are challenging due to the complicated constraints and dynamic environments. The decision variables need to be reoptimized to obtain the best objectives when dynamic environments arise. To solve these problems, we established a mathematical model and proposed a dynamic constrained multiobjective optimization evolution algorithm for the fluid catalytic cracking process. In this algorithm, we design an offspring generation strategy based on minimax solutions, which can explore more feasible regions and converge quickly. Additionally, a dynamic response strategy based on population feasibility is proposed to improve the feasible and infeasible solutions by different perturbations, respectively. To verify the effectiveness of the algorithm, we test the algorithm on ten instances based on the hypervolume metric. Experimental results show that the proposed algorithm is highly competitive with several state-of-the-art competitors.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12142285