Smoothing-Combined Soft Sensors for Noise Reduction and Improvement of Predictive Ability

Abstract

Soft sensors estimate values of difficult-to-measure process variables (y) from values of easy-to-measure process variables (X). Although adaptive soft sensors have been developed to reduce degradation of soft sensor models, noise in data has harmful effects to predictive ability of soft sensors. Many chemometric methods such as partial least-squares regression and support vector regression can handle noise. However, these methods do not consider characteristics of operating data or time-series data. Data measured closely in time have strong relationships and correlations. We propose to combine soft sensors with smoothing methods such as simple moving average, linearly weighted moving average, exponentially weighted moving average and Savitzky-Golay filtering. Before model construction and prediction, a smoothing method is applied to each X-variable. Case studies using simulated and industrial data sets confirm that the use of the proposed methods enables soft sensors to predict y-values smoothly and accu...