Reduction of tomographic data for use in the control of multiphase processes

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