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 these models. 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 algorithm that integrates modeling and feature extraction. The idea behind the IMSLVR modeling algorithm is to filter the process data at different decomposition levels, model the filtered data from each level, and then select the LVR model 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. 1. Introduction In the chemical process industry, models play a key role in various process operations, such as process control, monitoring, and scheduling. For example, the control of a distillation column requires the availability of the distillate and bottom stream compositions. Measuring compositions online is very challenging and costly; therefore, these compositions are usually estimated (using inferential models) from other process variables, which are easier to measure, such as temperature, pressure, flow rates, heat duties, and others. However, there are several challenges that can affect the accuracy of these inferential models, which include the presence of collinearity (or redundancy among the variables) and the presence of measurement noise in the data. The presence of collinearity, which is due to the large number of variables associated with inferential models, increases the uncertainty about the estimated model parameters and degrades its prediction accuracy. Latent variable regression (LVR), which is a commonly used framework in inferential
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