Result

& performance analysis :

The

objectives are to minimize the flowrate

F of experimental process control setup

by choosing optimal design variables: the Anemometer flowsensor output

x(1), pipe diameter x(2) , liquid

conductivity x(3) and the liquid viscosity x(4).This objective design problem can be written as :

ObjectF=(0.350099*10^-3)*(x(1)^14.20)*(x(2)^-4.37947)*(x(3)^-12.94)*(x(4)^2.940 (3)

It

is worth to pointing out the minimum

value of the objective function . In this paper FPA has to be extended in

combination with constraint handling techniques to deal with mixed integer

problems efficiently under the simplest branch-and -bound method utilized here.

Verification

:

Matlab code

execute for solving the non linear equation in FPA , we get the co efficient of x(1), x(2),x(3) & x(4)

& e ,

y=ax(1)+bx(2)+cx(3)+dx(4)+e

co efficient :

1.6563 0.6446 9.9035

-0.1291 50

and total error

is For 5.3291e-05(fmin) difference between the predicted & the optimized output.

Conclusion :

By

using Flower Pollination Alogorithm method and ANOVA we obtained the a

significant different result of optimum

conditions.The following are the co efficient of the parameters in order of

significantly affecting the liquid flow process : sensor output x(1) ,pipe

diameter x(2),liquid conductivity x(3) & liquid viscosity x(4) are (14.20 ,-4.37947,-12.94,2.94 &.350099*10^-3)in ANOVA

& new set of value coffeicient in FPA is (1.6563, 0.6446 , 9.9035 , -0.1291 & 50).Both the cases The predicted result

is compared to the experiments ,verified.

Therefore the obtained optimum conditions are proven to be effective. The

optimization process that performed by FPA method brings out the best conditions to be

used in a liquid flow process that could actualize a friendly environmental

process due to the total least error.