A Practically Validated Adaptive Calibration Technique using Optimized Artificial Neural Network for Level Measurement by Capacitance Level Sensor

Design of an adaptive calibration technique using an optimized artificial neural network for liquid-level measurement is discussed in this paper. The objective of the present work is to design and validate an adaptive calibration technique so as (1) to extend the linearity range of measurement to 100% of full-scale input range and (2) to make the measurement technique adaptive of variations in tank diameter, permittivity of liquid, liquid temperature, and to achieve objectives (1) and (2) using an optimized neural network. An optimized artificial neural network is a network having least possible number of hidden layers to achieve minimum mean square error between outputs and targets by comparing various algorithms, schemes, and transfer functions of neuron. The output of capacitance level sensor is capacitance. A data conversion unit is used to convert it to voltage. A suitable optimized artificial neural network is designed and used in place of conventional calibration circuit. The proposed technique is tested with simulated data and validated with practical data. Results show that proposed technique has fulfilled the set objectives.