13-10-2012, 05:54 PM
Optimization of Shell and Tube Heat Exchangers Using modified
Genetic Algorithm
Optimization of Shell.pdf (Size: 161.43 KB / Downloads: 107)
Abstract
The objective of this paper is to Develop and Test a model of optimizing the early design
phase of shell and Tube Heat Exchangers via the application of modified Genetic Algorithm
(MGA).The Modified Genetic Algorithm is based on the integration of classical genetic
algorithm structure and a systematic neighborhood structure .The MGA model can help the
designers to make decisions at the early phases of the design process. With a MGA model, it
is possible to obtain an approximately better prediction, even when required information is
not available in the design process. This model proved that MGA is capable of providing
better solutions with higher quality even with inadequate data.
Introduction
Designing a product deals with various parameters such as cost, time, size, area, accuracy
etc. among the above cost estimation is a key factor during the development phases of
manufactured products. Early approximations of cost depend on the structure and
development of the product. The more the size of product the more cost it provides. Studies
have shown that the greatest potential for cost reduction is at the early design phases, where
as much as 80% of the cost of a product is decided. The total development cost provides a
greater effort for the designer to design and it is necessary to step towards optimizing the
product. Making a wrong decision at this stage is extremely costly and further down the
development process. Product modifications and process alterations are more expensive in the
upcoming development cycle. Thus, cost estimators need to approximate the true cost of
producing a product, in addition optimization has to be done in parallel. Rush et all. [10]
examine both traditional and more recent developments in cost estimating techniques in order
to highlight their advantages and limitations. The analysis includes parametric estimating;
feature based costing, artificial intelligence, and cost management techniques. Niazi et all.
[11] provide a detailed review of the state of the art in product cost estimation covering
various techniques and methodologies developed over the years. The overall work is
categorized into qualitative and quantitative techniques. The qualitative techniques are further
subdivided into intuitive and analogical techniques, and the quantitative ones into parametric
and analytical techniques. Curran et all. [12] provide a comprehensive literature review in
engineering cost modeling as applied to aerospace.
Modified Genetic Algorithm (MGA)
Genetic algorithms (GAs) are stochastic optimization techniques founded on the
concepts of natural selection and genetics. The algorithm starts with a set of solutions
called population. Solutions from a population of chromosomes are used to form a new
population. Once the initial population is formed, the GA creates the next generation
using three main operators: (1) reproduction, (2) crossover and (3) mutation.
Reproduction is the process in which the most fits chromosomes in the population
receives correspondingly large number of copies in the next generation. This operation
increases quality of the chromosomes in the next generation and therefore leads to
better solutions of the optimization problem. The crossover operator takes two of the
selected parent chromosomes and swaps parts of them at a randomly selected location.
This provides a mechanism for the chromosomes to mix and match their desirable
qualities in forming offspring. Mutation plays a secondary role in the GA to alter the
value of a gene at a random position on the chromosome string, discovering new
genetic material or restoring last material. New solutions are selected according to their
fitness: the more suitable they are, the more chances they have to reproduce. This
produce repeated until some condition is satisfied. With crossover and mutation taking
place, there is a high risk that the optimum solution could be lost as there is no
guarantee that these operators will preserve the fittest string. To counteract this, elitism
mechanism is often used. In this mechanism, the best individual from a population is
saved before any of these operations take place. After the new population is formed and
evaluated, it is examined to see if this best structure has been preserved. If not, the
saved copy is reinserted back into the population. Using selection, crossover, and
mutation on their own will generate a large amount of different probable solutions.
However, some main problems can arise. Depending on the initial population chosen,
there may not be enough diversity in the initial solutions to ensure the GA searches the
entire problem space.
Heat Exchanger Design
Shell-and-tube heat exchangers are probably the most common type of heat exchangers
applicable for a wide range of operating temperatures and pressures. They have larger ratios
of heat transfer surface to volume than double-pipe heat exchangers, and they are easy to
manufacture in a large variety of sizes and flow configurations. They can operate at high
pressures, and their construction facilitates disassembly for periodic maintenance and
cleaning. Shell-and-tube heat exchangers find widespread use in refrigeration, power
generation, heating and air conditioning, chemical processes, manufacturing, and medical
applications.
A shell-and-tube heat exchanger is an extension of the double-pipe configuration. Instead
of a single pipe within a larger pipe, a shell and-tube heat exchanger consists of a bundle of
pipes or tubes enclosed within a cylindrical shell. One fluid flows through the tubes, and a
second fluid flows within the space between the tubes and the shell . The main purpose of a
heat exchanger is to capture heat that would otherwise be lost through waste gases or liquids
and return that heat to some stage of the production process. Heat exchangers have been used
in various industrial processes for more than 60 years. The most commonly used type of heat
exchanger is the shell-and tube heat exchanger, the optimal design of which is the main
objective of this study. Various strategies are applied for the optimal design of heat
exchangers. The main objective in any heat exchanger design is the estimation of the
minimum heat transfer area required for a given heat duty, as it governs the overall cost of the
heat exchanger.
Optimization
The Proposed approach of Modified Genetic Algorithm uses a solution which is more
suitable for one of the three operators named “reproduction”. Reproduction of new
chromosomes is developed until some condition is satisfied. To make the above said
true,”elitism” mechanism is used. In this mechanism, the best individual chromosome species
from a population is saved before any of these operations take place. Then the new generation
chromosomes are developed until the condition set for their generation is satisfied. In the
Modified genetic Algorithm, we make use of classical genetic algorithm which concentrates
on the reproduction side and neighborhood unit concentrates on receiving and regenerating
the new best solution with more high quality. By the use of Modified Genetic Algorithm , lot
of neighbor chromosomes solutions with higher quality is obtained when these solutions
are evaluated using an algorithm, faster convergence and fine tuning together with
improvement in the solution quality can be achieved.
Conclusion
In this work, an optimization model for the design of shell and tube exchanger using
Modified Genetic Algorithm has been proposed. The optimization strategy uses MGA
which is formed by the Integration of Classical Genetic Algorithm and Neighborhood
units. GA are in general more effective in producing optimum solutions and when this
Algorithm is combined with Neighborhood units optimum solutions with best and high
quality is achieved. Table 1. Gives the optimization results of Heat Exchanger.