Data-driven Soft Sensor Approach For Quality Prediction in a Refinery Process

Abstract

In petrochemical industry, the product quality encapsulates the commercial and operational performance of a manufacturing process. Usually, the product quality is measured in the analytical laboratory and it involves resources and considerable time delay. On-line prediction of quality using frequent process measurements would be beneficial in terms of operation and quality control. In this article, a novel soft sensor technology based on partial least squares (PLS) regression between process variables and quality variable is developed and applied to a refinery process for quality prediction. The modeling process is described, with emphasis on data preprocessing, PLS regression, multi-outliers' detection and variables selection in regression. Enhancement of PLS is also discussed to take into account the dynamics in the process data. The proposed approach is applied to data collected from a refinery process and its feasibility and performance are justified by comparison with laboratory data.