Designing a highly accurate model for liquid flow process industry and controlling 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 flow rate depends on 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 obtaining the optimum flow rate which is manually controlled in the process. Hence the different computational optimization processes are utilized by using different intelligence techniques. In this paper three different selection of hybrid Genetic Algorithm- Neural network model is proposed & tested against the present liquid flow process. Equations for neural network are being used as non-linear model and these models are optimized using the proposed different selection of Genetic optimization techniques which is based on mimic of the genetic evolution of species that allow the consecutive generations in population to adapt their environment. From the numerical result it is observed that among the three different selection rank selected hybrid Genetic Algorithm- Neural network (GA-ANN) model is better than the other two selections (Tournament & Roulette wheel) in terms of the accuracy (98.42%) of final solutions, minimum absolute error (0.6463), computational time, and stability.
liquid flow control process, anemometer type flow sensor, modelling, genetic algorithm, neural network model
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