Particle swarm optimization technique for the optimal design of shell and tube heat exchangers
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Sandip Kumar Lahiri
Abstract
Owing to the wide utilization of heat exchangers in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters until given heat duty and set of geometric and operational constraints are satisfied.Although well proven, this kind of approach is time consuming and may not lead to cost effective design. The present study explores the use of non-traditional optimization technique: calledParticle swarm optimization (PSO), for design optimization of shell and tube heat exchangers from economic point of view. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tubelength, bafflespacing, number of tube passes, tubelayout, type of head, baffle cutetc and minimization of total annual cost is considered as design target. The presented PSO technique is conceptually simple, has only a few parameters and is easy to implement.Furthermore, the PSO algorithm explores the good quality solutions quickly, giving the designer more degrees of freedom in the final choice with respect to traditional methods. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm . The PSO method leads to a design of a heat exchanger with a reduced cost of heat exchanger as compare to cost obtained by previously reported GA approach.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Response Surface Modeling and Optimization of Immobilized Candida antarctica Lipase-Catalyzed Production of Dicarboxylic Acid Ester
- Search for Optimum Operating Conditions for a Water Purification Process Integrated to a Heat Transformer with Energy Recycling using Artificial Neural Network Inverse Solved by Genetic and Particle Swarm Algorithms
- Generic Mathematical Model for PSA Process
- A Combined Computational Fluid Dynamics and Artificial Neural Networks Model for Distillation Point Efficiency
- Multi-level Reactor Optimisation in the Conceptual Design of Processes with Heterogeneous Catalytic Reactors
- ANN and ANFIS Models for COP Prediction of a Water Purification Process Integrated to a Heat Transformer with Energy Recycling
- Adsorption of Cadmium on Gel Combustion Derived Nano ZnO
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