Low-dimensional Latent Space Research Articles

Latent variable MPC for trajectory tracking in batch processes

A novel multivariate empirical model predictive control strategy (LV-MPC) for trajectory tracking and disturbance rejection for batch processes is presented. The strategy is based on dynamic principal component analysis (PCA) models of the batch process. The solution to the control problem is computed in the low dimensional latent variable space of the PCA model. The trajectories of all variables over the future horizon are then computed from the latent variable solution of the controller. The excellent control performance and the modest closed-loop data requirements for identification are illustrated for the temperature tracking in simulations of an emulsion polymerization process, an exothermic chemical reaction system and for MIMO temperature and pressure tracking in a nylon polymerization autoclave.

Plastic identification by remote sensing spectroscopic NIR imaging using kernel partial least squares (KPLS)

This work describes the application of partial least squares (PLS) modeling in data reduction purposes for the classification of spectroscopic near infrared (NIR) images. Given multi-dimensional images (i.e. p images taken at p different wave-lengths regions in the NIR-range), PLS projects the (nearly void) high dimensional space into a low dimensional latent space using the coded class information of the sample objects. Hence, PLS can be considered as a supervised latent variable analysis. In addition, data reduction by PLS increases the speed of on-line classification which is attractive in, e.g., process control. In order to apply these conditions on imaging problems a rapid PLS version, kernel PLS, is investigated. Emphasis is put on the performance of PLS as a supervised data decomposition technique for the classification of collinear image data, applied on a real world application. This application entails the discrimination between the materials plastics, non-plastics and image backgrounds.