In process will not be give the expected

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.