REDUCTION OF TOMOGRAPHIC DATA FOR USE IN THE CONTROL OF MULTIPHASE PROCESSES

Abstract

Tomograhic sensors are ideally suited to the on-line control of multiphase processes. Little work to date however has been undertaken to determine what type and style of information is required from an image to provide effective process control. In this paper, a possible strategy is presented; namely, a combination of Principal Component Analysis (PCA) and Neural Networks (NN) is used to convert multivariate data from tomographic images into useful information suitable for the control and optimization of chemical processes.

KEYWORDS:

• Multivariate data analysis
• neural networks
• principal component analysis
• process tomography