Design and implementation of electrical capacitance tomography using embedded system

Abstract

Electrical Capacitance Tomography (ECT) is emerging technologies which are used to visualise the internal behaviour of process plant, medical and industrial applications comprising dielectric components. The basic concept of ECT system is an effective technique which measures internal permittivity distribution, based on external capacitance measurements. It has the advantages of non-radioactive, non-intrusive, noninvasive, high imaging speed and low cost which makes it better than the conventional process industrial imaging techniques. The working principle of ECT is to measure variations in capacitance between electrode pairs and to generate a cross-sectional image, representing the permittivity distribution and therefore the material distribution of the dielectric materials inside closed pipes. Inter-electrode capacitance measurements are done by placing the electrodes around the non-conductive different dielectrics medium cylinder inside which, the mediums to be imaged are placed. The aim of the present work is to design and implement an electrical capacitance tomography system for identifying unknown object and level, temperature parameters sensing of static dielectric medium within the container. The modeling and design of ECT is virtually implemented using ANSYS. To validate the performance characteristics, ECT sensor is fabricated and tested. Eight electrodes are symmetrically mounted outside the cylinder. The cylinder is made up of Poly-vinyl chloride (PVC) which isnon-conductive dielectric medium. The electrodes are made up of Copper (Cu) which is conductive. The ECT sensor is calibrated by fixed capacitor method by multiple polynomial regression tools. In excitation source generation, this is used to supply fixed frequency and constant voltage to each combinational electrode. The measured current is converted into voltage by the signal conditioning circuit. The converted voltage values are converted to pixel values using an image reconstruction linear back projection algorithm in MATLAB newline