Partial least squares model based process monitoring using near infrared spectroscopy

Abstract

On-line analyzers are widely used in chemical and oilindustry to estimate product properties and monitor production process. Partial Least Squares regression (PLS) is known as bilinear factor model as it projects input (X) and output (Y) data into low dimensional spaces. We present how this projection can be utilised in process monitoring and validation of on-line analysers. We apply the proposed methodology in a diesel fuel mixer where main product properties are estimated from near infrared spectra. Results show that the developed 2 Dimensional Partial Least Squares (2DPLS) model not only gives better property estimation performance than the currently applied Topological Near Infrared modelling tool (TOPNIR), but it is also able to provide informative map of operating regimes of the process.