In this paper an intelligent flow measurement technique is designed by ultrasonic transducers with the help of optimized Fuzzy Logic controller.The main objectives of the present work is to make the intelligent flow measurement technique adaptive to variations in pipe diameter,liquid density& liquid temperature & make a linear relationship between input & output parameter.The output of an ultrasonic flow transducer is frequency which is converted to voltage by using the signal conditioning circuit. For calibration purpose an optimum Fuzzy logic controller is placed instead of conventional calibration circuit and it is test & trained for various values of pipe diameter,liquid density ,liquid temperature & signal conditioning output.The proposed technique is then subjected to the practical data for validation which is done with the help of actual flowrate & output of the intelligent technique.

In process industry liquid flowrate is one of the important variable which need to be controlled in a process to obtain the better quality and reduce the cost of production. As the liquid Flow rate in a process industry depends upon a number of parameter so the process will give the unexpected 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 (FPA) methods and ANOVA to obtain the optimum conditions of a flowrate in a process industry and to gain the percentage of contributions of each parameter by. A verification test was carried out to inspect the optimum output among the ANOVA & FPA. For generating the objective function 120 sets of data is used in ANOVA while 18 sets of data are used for the verification purpose.

Abstract Controlling liquid flow is one of the most important parameters in the process control industry. It is challenging to optimize the liquid flow rate for its highly nonlinear nature. This paper proposes a model of liquid flow processes using an artificial neural network (NN) and optimizes it using a flower pollination algorithm (FPA) to avoid local minima and improve the accuracy and convergence speed. In the first phase, the NN model was trained by the dataset obtained from the experiments, which were carried out. In these conditions, the liquid flow rate was measured at different sensor output voltages, pipe diameter and liquid conductivity. The model response was cross-verified with the experimental results and found to be satisfactory. In the second phase of work, the optimized conditions of sensor output voltages, pipe diameter and liquid conductivity were found to give the minimum flow rate of the process using FPA. After cross-validation and testing subdatasets, the accuracy was nearly 94.17% and 99.25%, respectively.

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Estimation of a highly accurate model for  liquid flow process industry and control of the liquid flow rate from experimental data is an important task for engineers due to its non linear characteristics. Efficient optimization techniques are essential to accomplish this task.In most of the process control industry flowrate  depends upon a multiple number of parameters like sensor output,pipe diameter, liquid conductivity ,liquid viscosity & liquid density etc.In traditional optimization technique its very time consuming for manually control the parameters to obtain the optimial flowrate from the process.Hence the alternative approach , computational optimization process is utilized by using the different computational intelligence technique.In this paper three different selection of Genetic Algorithm is proposed & tested against the present liquid flow process.The proposed algorithm is developed based on the mimic  genetic evolution of species that allow the consecutive generations in  population to adopt their environment.Equations for Response Surface Methodology (RSM) and Analysis of Variance (ANOVA) are being used as non-linear models and these models are optimized using the proposed different selection of Genetic optimization techniques. It can be observed that the among these three different selection of Genetic Algorithm ,Rank selected GA  is better than the other two selection (Tournament & Roulette wheel) in terms of the accuracy of final solutions, success rate, convergence speed, and stability.