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Details of Grant 

EPSRC Reference: GR/M68909/01
Title: TRI-LINEAR DECOMPOSITIONS IN MULTIVARIATE STATISICAL PROCESS CONTROL
Principal Investigator: Martin, Professor EB
Other Investigators:
Morris, Professor AJ
Researcher Co-Investigators:
Project Partners:
Department: Mechanical and Systems Engineering
Organisation: Newcastle University
Scheme: Standard Research (Pre-FEC)
Starts: 25 March 1999 Ends: 24 May 1999 Value (£): 15,450
EPSRC Research Topic Classifications:
Design of Process systems
EPSRC Industrial Sector Classifications:
Manufacturing Chemicals
Construction Pharmaceuticals and Biotechnology
Related Grants:
Panel History:  
Summary on Grant Application Form
An area of increasing industrial importance is that of agile and responsive manufacturing through batch-type operations. Currently there is a lack of fundamental research into the key methodologies required to develop process performance monitoring schemes which consider both the dynamic trajectories of the batch and also the correlations present between the variables themselves. A key area of research is that of tri-linear decomposition. It is proposed to investigate the use of tri-linear decomposition (TLD), a parallel factor analysis (PARAFAC) and individual difference scaling (INDSCAL) models using both synthetic and actual plant data. The effect of time-line stretching will also be investigated, along with the general problem of how to keep multi-way models updated in the presence of process drift. The Foresight Centre has access to a number of companies that will provide appropriate data, for this study, in particular ICI and Zeneca. A second strand of the work will be to benchmark canonical varieties analysis against other techniques, including principal components analysis (PCA), multi-way PCA, TLD, PARAFAC and the Maximum Likelihood PCA (MLPCA) approaches, that Eigenvector Research has investigated on semiconductor data. The tools and code are in place to allow such comparisons to be made.
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Organisation Website: http://www.ncl.ac.uk