Generic Reality Visualisation Models Through Fusion of Process Tomography Sensor Data

B S Hoyle1, X Jia2, X Z Wang2, R A Williams2

1 School of Electronic and Electrical Engineering, University of Leeds, b.s.hoyle@leeds.ac.uk

2 School of Process, Environmental and Mineral Engineering, University of Leeds, Leeds, LS2 9JT, UK

ABSTRACT

The paper reports early results of a study that aims to demonstrate a critical enabling step in the widespread exploitation of the multi-dimensional process sensing offered by process tomography (PT). PT technology has been developed to an advanced state and has now demonstrated a unique insight into many processes. However, most current applications are laboratory or pilot-plant based, clearly due in part to the difficulty in developing applications from a basic level.

It is proposed that generic system integration tools that deal systematically with PT data could accelerate the assessment and application of PT sensing, and hence deliver the large benefits expected to arise from large-scale exploitation of multi-dimensional sensing. Computational fluid dynamic (CFD) simulation models form a critical and commonplace stage in process design and undoubtedly expedite new developments. These may be used in simplified form in model-based on- line control systems.

In contrast, where multi-dimensional PT sensor data is available, a suite of contrasting tools: reality visualisation modelling (RVM), are proposed as the complementary generic set of processing methods needed to take internal process state data, and yield directly usable process information, either for design model confirmation, or for effective plant control, or monitoring. Further Generic-RVM (GRVM) extends this concept through a generically defined process topology, complete with method and model set, to expedite mass-market application.

Keywords Control, data fusion, process modelling, visualisation, interpretation.