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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