Case studies in process modelling and condition monitoring using artificial neural networks

Abstract

Increasingly artificial neural networks are finding applications in a process engineering environment. Recently the Department of Trade and Industry in the UK has supported the transfer of neural technology to industry with a £5.7M campaign. As part of the campaign, the University of Newcastle and EDS Advanced Technologies Group have set up a Process Monitoring and Control Club.

This paper presents two case studies from the work of the Club. Firstly, the ability of neural networks to provide enhanced modelling performance over traditional linear techniques is demonstrated on real process data. Secondly, the ability of neural networks to capture non-linear system characteristics is exploited in a novel way in a condition monitoring exercise. The process studied in both applications is the melter stage of the BNFL Vitrification Process. The process involves the encapsulation of highly active liquid waste in glass blocks to provide a safe and convenient method of storage.