Integrated multiscale latent variable regression and application to distillation columns

• Authors: Muddu Madakyaru;Mohamed N. Nounou;Hazem N. Nounou
• Venue: Modelling and Simulation in Engineering
• Year: 2013

Proper control of distillation columns requires estimating some key variables that are challenging to measure online (such as compositions), which are usually estimated using inferential models. Commonly used inferential models include latent variable regression (LVR) techniques, such as principal component regression (PCR), partial least squares (PLS), and regularized canonical correlation analysis (RCCA). Unfortunately, measured practical data are usually contaminated with errors, which degrade the prediction abilities of inferential models. Therefore, noisy measurements need to be filtered to enhance the prediction accuracy of thesemodels. Multiscale filtering has been shown to be a powerful feature extraction tool. In this work, the advantages of multiscale filtering are utilized to enhance the prediction accuracy of LVR models by developing an integrated multiscale LVR (IMSLVR) modeling algorithmthat integratesmodeling and feature extraction. Theidea behind the IMSLVRmodeling algorithmis to filter the process data at different decomposition levels,model the filtered data fromeach level, and then select the LVRmodel that optimizes a model selection criterion. The performance of the developed IMSLVR algorithm is illustrated using three examples, one using synthetic data, one using simulated distillation column data, and one using experimental packed bed distillation column data. All examples clearly demonstrate the effectiveness of the IMSLVR algorithm over the conventional methods.