Design Of Estimator For Level Monitoring Using Data Driven Model

A state observer estimates the state variables depending on the measurements of the output over a period for an observable system. Luenberger observers can be used when the sensor produces minimal noise. Whereas, for stochastic systems having measurement and process noise Kalman filters are more suitable. This paper reports a state observer model for a liquid level monitoring system using both Luenberger and Kalman methods. A CFD simulation is carried out to investigate the laminar type of water flow through an orifice meter with a definite pipe diameter, which aids in the calculation of pressure difference resulting in liquid level estimation.