Global optimization methods for chemical process design: Deterministic and stochastic approaches |
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Authors: | Soo Hyoung Choi Vasilios Manousiouthakis |
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Affiliation: | (1) School of Chemical Engineering and Technology, Chonbuk National University, 561-756 Jeonju, S. Korea;(2) Chemical Engineering Department, University of California Los Angeles, 90095 Los Angeles, CA, USA |
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Abstract: | Process optimization often leads to nonconvex nonlinear programming problems, which may have multiple local optima. There
are two major approaches to the identification of the global optimum: deterministic approach and stochastic approach. Algorithms
based on the deterministic approach guarantee the global optimality of the obtained solution, but are usually applicable to
small problems only. Algorithms based on the stochastic approach, which do not guarantee the global optimality, are applicable
to large problems, but inefficient when nonlinear equality constraints are involved. This paper reviews representative deterministic
and stochastic global optimization algorithms in order to evaluate their applicability to process design problems, which are
generally large, and have many nonlinear equality constraints. Finally, modified stochastic methods are investigated, which
use a deterministic local algorithm and a stochastic global algorithm together to be suitable for such problems.
Partly presented at PSE Asia 2000 (December 6–8, Kyoto, Japan) |
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Keywords: | Global Optimization Deterministic Stochastic Approach Nonconvex Nonlinear Program |
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