Optimization of continuous distillation columns using stochastic optimization approaches

Ramanathan, S. P. ; Mukherjee, S. ; Dahule, R. K. ; Ghosh, S. ; Rahman, I. ; Tambe, S. S. ; Ravetkar, D. D. ; Kulkarni, B. D. (2001) Optimization of continuous distillation columns using stochastic optimization approaches Chemical Engineering Research and Design, 79 (3). pp. 310-322. ISSN 0263-8762

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Official URL: http://linkinghub.elsevier.com/retrieve/pii/S02638...

Related URL: http://dx.doi.org/10.1205/026387601750281671

Abstract

The present work describes the use of two stochastic optimization formalisms, namely, genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA), for the optimization of continuous distillation columns. Both the simple and azeotropic systems are considered in the analysis. In particular, for a specified degree of separation the problem of finding the optimal values of: (i) the number of stages, (ii) reflux ratio (entrainer quantity in the case of azeotropic distillation), (iii) feed location(s), have been addressed. The GA-based optimization has several attractive features such as: (i) convergence to the global rather than to a local minimum, (ii) the objective function need not satisfy smoothness, differentiability, and continuity criteria, (iii) robustness of the algorithm. The other optimization technique used in the study i.e., SPSA, is a rapid gradient-descent related method for multivariate optimization and is especially well-suited in situations where direct computation of the objective function gradient is not feasible, or the objective function measurements could be noisy. The feasibility of utilizing the GA and SPSA techniques has been demonstrated by considering the separation of three binary and two azeotropic systems of industrial relevance.

Item Type:Article
Source:Copyright of this article belongs to Institution of Chemical Engineers.
Keywords:Distillation Optimization; Continuous Distillation; Azeotropic Distillation; Genetic Algorithms; Simultaneous Perturbation Stochastic Approximation; Napthali-sandholm Model
ID Code:17335
Deposited On:16 Nov 2010 08:03
Last Modified:06 Jun 2011 09:20

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