In the liquid

flow process industry, the flow of the

liquid change in irregular manner due to the inefficient processes. As the Flow

rate in a process industry depends upon a number of parameter so the

process will not be give the expected output as it is caused by the improper setting of parameters.

The improper parameter settings could threaten the processes.In this paper, we

utilize the Flower Pollination Algorithm methods and ANOVA to obtain the optimum

conditions of a flow process and to gain

the percentage of contributions of each parameter. A verification test was

carried out to inspect among the ANOVA & FPA ,FPA produce the optimum

result than ANOVA.120 sets of data is used for constructing the objective

function by using ANOVA while 18 sets of data are used for the verification purpose.In

most of the industrial applications, there is a need to calculate the inputs to

a process that will drive its outputs in a desired way and thus achieve some

optimum (desired) goal.In such applications,a mathematical input–output model

for the process is usually derived.The model could be based on the physical phenomena

or available historical input–output data.Once the model is developed,

mathematical techniques can be applied to determine the inputs to the process

that will satisfy a certain given criteria.combustion engines 21–24, two-stage combustor burning ethylene

(doped with ammonia) in air 25, catalytic

distillation 26 and desulphurization of

hot metal and steel 27 those are the

industrial process where the modelling and optimization research have been

conducted.The developed optimization algorithm is tested on a novel flow

thermal sensor whose inputs are the flow velocity and fluid temperature and

output is the voltage measurement.29

present thermal flow sensor has a high sensitivity at low flow rates because of

the non-linear transfer function of the sensor which makes the device

especially suitable for very low flow rates measurements.From the experimental

set up provides 5 different variables where four inputs (sensor output, pipe

diameter, liquid conductivity ,liquid viscosity ) & single output, flow

rate .An objective function is constructed with help of the four parameters

which makes this process non linear.Liquid

flow optimization is the one of the process where the optimized flow in a

process plant can be achieved from a set of value of the process parameters. An

artificial neural net model that approximates the calibration data for the

sensor and design an optimized algorithm which

determines the flow velocity of the flowing gas in a pipe if the thermal

flow sensor voltage measurement and fluid temperature are known.The problem

reduces to minimizing a positive cost function that measures the difference

between the neural net approximated voltage and its desired value discussed in

31.In most of the industrial applications, there is a need to calculate the

inputs to a process that will drive its outputs in a desired way and thus

achieve some optimum (desired) goal.In such applications,a mathematical input–output

model for the process is usually derived.The model could be based on the

physical phenomena or available historical input–output data. Once the model is

developed, mathematical techniques can be applied to determine the inputs to

the process that will satisfy a certain given criteria.An

advantage of the method is that it keeps the forward ANN which is obtained from

the computationally expensive training and can be re-used for other purposes

such as prediction and adaptive control.The developed optimization algorithm is

tested on a novel flow thermal sensor whose inputs are the flow velocity and

fluid temperature and output is the voltage measurement.The development

of a Fuzzy Temperature compensation scheme (FTCS) for hot wire mass airflow

(MAF) sensor is used to compensate the measurement error occurred by using

Sugeno type FIS for temperature of 60C-100C.It verify the performance of the

proposed hot wire MAF sensor temperature-compensation scheme.The effectiveness

of the proposed fuzzy compensation scheme is verified with the estimation error

within only ±1% over full scale value 32.The output of the thermal sensor is

the increase with wire temperature that is the time constant of the heated wire

which is again related to the velocity of flow.At very low flow velocities the

response is determined by the time constant of the wire while at high

velocities the response is almost like a constant current hotwire

anemometer.the present thermal flow sensor can be used over a large range of

velocities as well as measurements of steadyor slowly varying unsteady flows in

industrial application.The calibration data of the sensor consists of a set

of a set of curves at different fluid

density,viscosity,thermal conductivity and pipe diameter where the the output voltage

of the sensor is a function of flow velocity.A Fuzzy model is implemented which

approximate the calibration data for the sensor and shows the better accuracy.30 present thermal

flow sensor has a high sensitivity at low flow rates because of the non-linear

transfer function of the sensor which makes the device especially suitable for

very low flow rates measurements.The sensitivity of the measured velocity is

approximately 0.3% at low flow velocities and it increases with velocity to

reach 3% at high velocities. The development of a Fuzzy Temperature

compensation scheme (FTCS) for hot wire mass airflow (MAF) sensor is used to

compensate the measurement error occurred by using Sugeno type FIS for

temperature of 60C-100C.It verify the performance of the proposed hot wire MAF

sensor temperature compensation scheme.The effectiveness of the proposed fuzzy

compensation scheme is verified with the estimation error within only ±1% over

full scale value 33.Real-world optimization problems are very complex and

challenging to solve, and many applications have to deal with these problems.

To solve such problems, approximate optimization algorithms have to be used,

though there is no guarantee that the optimal solution can be obtained 1.Over

the last few decades optimization algorithms have been applied in extensive

numbers of difficult problems. Several nature-inspired algorithms have been

developed over the last few years by the scientific community 2 4 5. The

reproduction of flower is achieved via the pollination process. Flower

pollination can be described as the distribution processes of pollen through a

wide range of pollinators such as insects, birds, bats and some other animals 7. The purpose of this

study was to find the optimum conditions of the process since they were

unknown.The application of FPA &

ANOVA method is expected to help reduce the amount of time for which the liquid

flow process produce the optimum output.