IJIRST (International Journal for Innovative Research in Science & Technology)ISSN (online) : 2349-6010

 International Journal for Innovative Research in Science & Technology

Nonlinearity Error Compensation of Venturi Flow Meter Using Evolutionary Optimization Algorithms


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International Journal for Innovative Research in Science & Technology
Volume 3 Issue - 7
Year of Publication : 2016
Authors : S. Murugan ; Dr. SP. Umayal; Dr. K. Srinivasan; M.Aruna

BibTeX:

@article{IJIRSTV3I7005,
     title={Nonlinearity Error Compensation of Venturi Flow Meter Using Evolutionary Optimization Algorithms},
     author={S. Murugan, Dr. SP. Umayal, Dr. K. Srinivasan and M.Aruna},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={7},
     pages={30--39},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I7005.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}



Abstract:

Linearization of sensor is one of the significant issues that must always be considered to guarantee a measurement system’s accuracy. Often in the progress of linearization, certain other errors also minimized. It is necessary for most of the sensor systems to have a linear performance. But since in practice there are some factors which brings non-linearity in a system. This paper focuses on the compensation of problems faced due to the non- linear response characteristics of venturi. The evolutionary algorithms used in this work are extreme learning machine (ELM), differential evolution (DE) and artificial neural network trained by genetic algorithm (GA-ANN). These algorithms when connected in series with the sensor offers extended linearity characteristics. The overall system provides accurate measurement for the whole range. A computer simulation is carried out using the experimental dataset of venturi sensor. It is observed that ELM method yields the lowest training time of zero seconds to obtain best linearity in the overall response when compared to others. At the same time DE algorithm and GA-ANN produces the lowest MSE value and better linearity. The proposed algorithm offers a less complexity structure and simple in testing and validation procedure. This hybrid technique is used to make a sensor output as more linear as possible. Further this adaptive algorithm is preferable for real time implementation also.


Keywords:

Venturi, Nonlinearity, Extreme Learning Machine (ELM), Differential Evolution (DE) algorithm, ANN trained by Genetic Algorithm (GA)


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