### In improper setting of parameters. The improper parameter

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.

Keywords
– Liquid flow process ,Optimization , ANOVA, FPA .

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Introduction
.

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 proveides 5 different variables where four inputs (sensor output, pipe diameter,
liquid conductivity ,liquid viscosity ) & single output thats flow rate .An
objective function is constructed with help of the four parameters which makes
this non linear.

2
LITERATURE REVIEW

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.

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.An inverse model can be learned by an
artificial neural network to calculates the inputs to a process based on the
forward (input to output) neural net model in 28.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.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.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 45.

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.