From the qualitative and quantitative point of view, an accurate liquid flow measurement is an essential requirement in a process control system. But due to the non linear characteristics of the liquid flow process it is necessary to accomplish an optimization technique. In most of the flow process control system the output flow depends on a number of input parameters like sensor output, pipe diameter, experimental liquid density, conductivity & viscosity. In conventional optimization it is very time consuming to obtain the optimal flow rate from the process after continuously tuning the input parameters. Hence computational intelligent optimization technique is utilized to achieve the optimum flowrate. In present paper contact type anemometer flow sensor is used as a flowsensor placed in three different sets of pipe diameter. Among 134 datasets 117 data is used for constructing the FIS model & 17 data sets for testing purpose. Four different Fuzzy model is designed (named as a Test 1, Test 2, Test 3 & Test 4) by considering the number of inputs & nature of the membership function. The accuracy of these models lying between 86%-92%. It can be observed that among all the four types of Test FIS, four input trapezoidal FIS (Test 2 FIS) is better than the other three Test FIS in terms of the accuracy, RMSE error, variance & stability.
flow sensor, modelling, fuzzy logic controller, membership function
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