Development of Autoranging Functionality to Enhance the Sensitivity of Helical Capacitance Level Sensors via Neural Networks.

Sensitivity is a critical performance parameter of sensors used in industrial process measurement systems. Conventional sensors are typically calibrated for a predefined operating range, resulting in reduced measurement sensitivity and accuracy when users require measurements within alternate subranges of the sensor's operating band. To address this limitation, this paper proposes an intelligent autoranging technique that dynamically enhances sensor sensitivity across user-defined measurement ranges without the need for manual recalibration. The proposed approach integrates an adaptive calibration framework based on artificial neural network (ANN) algorithms to automatically adjust the sensor response for different operating intervals. The methodology is implemented and experimentally validated using a capacitance level sensor (CLS) incorporating a 60 cm helical electrode structure for liquid-level measurement. The experiments were conducted using water as the test liquid under controlled laboratory conditions. The sensor capacitance, varying from 0.929 nF to 421 μF over a liquid-level range of 0 to 60 cm with a resolution of 0.1 cm, is converted into a measurable voltage signal ranging from 0.010939 V to 2.953 V through a dedicated signal conversion and conditioning circuit. The experimental results demonstrate that the proposed ANN-based autoranging strategy significantly improves sensor performance by increasing the full-scale sensitivity from 4.9 V/m to 58 V/m across different user-selected measurement ranges. The developed technique offers a flexible and intelligent solution for enhancing sensor sensitivity and adaptability in process industry applications, enabling accurate measurements over varying operating ranges without additional calibration procedures.