An adaptive calibration circuit for level measurement using optimized ANN

Design of an intelligent level measurement technique by Capacitance Level Sensor (CLS) using an Optimized Artificial Neural Network (OANN) is discussed in this paper. The objectives of the present work are (i) to extend the linearity range of measurement to 100% of the full scale, (ii) to make the measurement technique adaptive of variation in permittivity of liquid, liquid temperature, and to achieve objectives (i) and (ii) using an optimized neural network. An optimized ANN is considered by comparing various algorithms, transfer functions of neuron, and number of hidden layers based on minimum mean square error (MSE). The output of CLS is capacitance. A data conversion unit is used to convert it to voltage. A suitable optimized ANN is added, in place of conventional calibration circuit, in cascade to data conversion unit. The proposed technique provides linear relationship of the overall system over the full input range and makes it adaptive of variation in liquid permittivity and/or temperature. When an unknown level is tested with an arbitrary liquid permittivity, and temperature, the proposed technique has measured the level correctly. Results show that the proposed scheme has fulfilled the objectives.