Modelling of Liquid Flow control system Using Optimized Genetic Algorithm

  • Pijush Dutta Global Institute of Management & Technology
  • Asok Kumar
Keywords: Liquid flow control process, Anemometer type flow sensor, Modelling, Genetic Algorithm, Analysis of Variance (ANOVA), Response Surface Methodology (RSM).


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.

Author Biography

Pijush Dutta, Global Institute of Management & Technology
Electronics & Communication Engineering


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How to Cite
Dutta, P., & Kumar, A. (2020). Modelling of Liquid Flow control system Using Optimized Genetic Algorithm. Statistics, Optimization & Information Computing, 8(2), 565-582.
Research Articles