Result In this paper FPA has to be

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 :

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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.