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Development of a virtual instrument system for industrial process tomography.

Research output: Contribution to conferenceConference paper

Published

  • R. Deloughry
  • I. Davenport
  • X. Ma
  • P. V. S. Ponnapalli
Publication date09/2003
Number of pages5
Original languageEnglish

Conference

Conference3rd World Congress on Industrial Process Tomography
CityBanff, Canada
Period2/09/035/09/03

Abstract

Electrical tomographic imaging systems consist of a sensing element, measurement and signal conditioning electronics and a host computer. A variety of different hardware/software configurations, mainly PC based, have been developed over the years to accommodate the various developments in Process Tomography (PT). A requirement from industry, as some PT developments seek to move towards industrial applications, is for a robust and flexible system with back up support for both hardware and software. A program of work is currently being undertaken at Manchester Metropolitan University (MMU) to develop a Virtual Instrument (VI) (Bitter, 2000) for application in industrial Process Tomography. The aim of the work is to develop a generic Virtual Instrument (VI) system. Initial investigative work has developed a VI system capable of showing the flow image, the frame history and display of the mass flow using capacitive data measurements, from the Electrical Capacitance Tomographic (ECT) system developed for Montell (Deloughry, 2001). ECT data from a test rig at UMIST, related to the movement of plastic pellets has been processed using the same VI system and tomographic images produced. Electromagnetic Tomography Imaging (EMT) data of cold reference bars, taken from a research program at Lancaster University to monitor molten metal flow in a nozzle, has been used to produce VI images and Matlab based imaging algorithms have been incorporated in LabView software. A structure for the implementation of the proposed VI system will be outlined, the limitations and perceived advantages of the system are discussed. Future work related to the application of the system to the closed loop control of a pneumatic flow using Neural Networks is presented.