Optimization of Desiccant Absorption System Using a Genetic Algorithm531
are randomly selected from a Mating Pool, b) a number
of crossover positions along each string are uniformly
selected at random, and c) two new strings are creat ed and
copied to the next generation by swapping string charac-
ters between the crossovers positions defi ned before.
iii) Mutation: Create one new offspring program for
the new population by randomly mutating a randomly
chosen part of the selected program.
iv) Architecture-alte rin g o per ations: Select an architec-
ture-altering operation from the available repertoire of
such operations and create one new offspring program
for the new population by applying the selected architec-
ture-altering operation to the selected program.
3) Designate the individual program that is identified
by result designation (e.g., the best so-far individual) as
the result of the run of genetic programming. This result
may be a solution (or an approximate solution) to the
problem.
The design specifications of the GA are shown in Ta-
ble 1.
For more details of genetic operators and each block in
the flowchart, one may consult literature [13,14].
Here the goal is to find sets of system parameters that
will give a minimum fitness value over the operating
period [0, t]. The GA initializes a random set of popula-
tion of these three vari ab l es (regener at or l en gt h, solutions
flow rate and air flow rate mass).
Main system calculation parameters are presented in
Table 2. It should be noted that, moderate values of the
ambient parameters (temperature and humidity) are se-
lected for simulation purposes. However, variation of the
desiccant initial concentration may affect the value of the
system coefficient of performance (COP) but the opti-
mum design parameters will be the same values obtained
at the specified concentration which is used in the opti-
mization process.
Table 1. Specification of the GA.
Table 2. Calculation parameters of the syste m.
Ambient temperature, ˚C 33
Ambient vapor pressure, mm Hg 20
Desiccant (CaCl2) initi al concentration, % 40
Radiation intensity, kW/m2 0.8
3. Results and Discussion
The performance of the solar collector/regenerator is
influenced by design parameters (regenerator length,
solution fl ow rate, working sol ution concentra t i o n and air
flow rate) and ambient conditions (air temperature and
vapor pressure in the flowing air). These key parameters
are investigated in the following sub-section s. A sensitiv-
ity analysis is performed by varying the parameters of
interest one at a time, while keeping all others fixed at
given values.
In order to analyze the effect of air mass flow rate on
the regeneration process, the solution mass flow rate,
m, is settled at 20 kg/hr and the range of air mass flow
rate, a, is considered in the range (10 kg/hr - 200
kg/hr), then the vapour pressure difference between the
regenerated solution and flowing air is plotted versus the
regenerator length. For a given regenerator length, the
vapour pressure, which is the mass transfer potential, is
directly proportional with the rate of water evaporation,
when the mass transfer coefficient is assumed constant.
As shown in Figures 6 and 7, the vapor pressure differ-
ence has a maximum for a given length of the regenera-
tor. The length, at which the maximum rate of evapora-
tion occurs, increases with the air flow rate. Concerning
the effect of solution inlet concentration on regeneration
process, the decrease of solution concentration can effec-
tively improve the regenerator performance, though it
sacrifices solution outlet concentration.
m
The coefficient of performance (COP) of the system is
illustrated in the surface plot shown in Figure 8. For the
specified operating conditions, a maximum value of the
COP occurs at a given range of air and solution flow
rates. However, the maximum value of COP is dependent
of the design parameters and operating conditions, there-
fore it is essential to select the design parameters for each
ambient condition to maximize the COP of the system.
Table 3 demonstrates the simulation results for the
maximum values of the system COP, when the genetic
algorithm is applied. It can be observed that the maxi-
mum values of COP range from 32.5% to 36.6%. How-
ever, for the three design parameters, optimum values of
COP could be attained for different combinations of in-
put parameters. Comparing the GA outputs presented in
Table 3 with the simulation results p lotted in Figure 8, it
can be found the optimum values of COP obtained from
the GA are in good agreement with the maximum values
presented in Figure 8. Moreover, it should be noted that
the application of genetic algorithm results in direct
evaluation of the optimization parameters. When design-
ing an o ptimal system, multiple options are available and
the decision must be taken on the account of the avail-
ability of the site and the economical considerations.
Population size 20
Individuals in offspring genera t i o n 50
Coding of individ uals Gray-coding
Recombination probability 0.6
Crossover rate 0.7
Mutation rate 0.05
Chromosom e len gth 12
Precision of variables 3
Generation gap 1
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