Development of Virtual Flow Sensor using Artificial Neural Networks
Gangiregula Subba Rao1, Venkata Lakshmi Narayana K.2, Naveen Kumar V.3, Pranav Sanghavi4

1Gangiregula Subbarao, Department of Electronics & Communications Engineering, Sri SatyaSai University of Technology and Medical Sciences, Sehore, India.
2Venkata Lakshmi Narayana K., School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
3Naveen Kumar V.*, School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
4Pranav Sanghavi, Department of Computer Science and Electrical Engineering, West Virginia University, Morgan Town, West Virginia, USA.

Manuscript received on November 14, 2019. | Revised Manuscript received on 24 November, 2019. | Manuscript published on December 10, 2019. | PP: 4081-4087 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7703129219/2019©BEIESP | DOI: 10.35940/ijitee.B7703.129219
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Abstract: In this paper, a virtual flow sensor using artificial neural networks (ANN) is proposed to improve the efficiency of an industrial flow control loops. In conventional flow-control loop, flow meters used for sensing flow rate in the feedback path cause pressure drop in the flow. This may increase the energy usage for propelling the fluid. The functional relation between the flow rate and the physical properties of the flow through the final control element such as control valve is known and the said properties namely pressure drop, temperature, and valve position are yielded from an experimental set-up. These properties are used as training data for ANN models to yield the fluid flow rate through the control valve. Here, the ANN acts as a virtual flow sensor. The feasibility of the proposed methodology is validated by using real measurement of flow and used them to model virtual flow sensor using the multi-layer perceptron artificial neural networks (MLP-ANN) with back propagation (BP) algorithm. Moreover, its practical proof of concept is demonstrated by implementing the trained MLP-ANN on a Spartan-3E-starter Field Programmable Gate Array (FPGA) unit through a hardware co-simulation. 
Keywords: Artificial Neural Networks, Back Propagation Algorithm, Control valve, Flow-rate, Multi-layer Perceptron, Virtual Sensor.
Scope of the Article: Artificial Intelligence