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Comparison of stochastic methods with respect to performance and reliability of low‐temperature gas separation processes
Author(s) -
Tahouni Nassim,
Smith Robin,
Panjeshahi M. Hassan
Publication year - 2010
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.20265
Subject(s) - simulated annealing , convergence (economics) , reliability (semiconductor) , genetic algorithm , mathematical optimization , computer science , process (computing) , stochastic optimization , work (physics) , materials science , mathematics , thermodynamics , physics , operating system , power (physics) , economics , economic growth
In this paper, the performances of two popular stochastic methods, the genetic algorithm (GA) and simulated annealing (SA), are verified in the optimization of low‐temperature gas separation processes. While the feasibility of GA optimization of low‐temperature processes has recently been addressed, our work studied the quality of GA solutions. Having optimized the solutions of three different case studies, it was observed that SA is more robust and reliable than GA when applied to such systems, and by adjusting the key parameters in the SA method, the optimization process can avoid pre‐mature convergence and is able to give the best near‐global results.