Practical challenges in developing data-driven soft sensors for quality prediction

Abstract

With improved quality control, a refinery plant can operate closer to optimum values. However, real-time measurement of product quality is generally difficult. On-line prediction of quality using frequent process measurements would therefore be beneficial. In this paper, our learnings from developing and deploying a data-driven soft sensor for a refinery unit are presented. Key challenges in developing a practicable soft sensor for actual use in a plant are discussed and our solutions to these presented. Finally, this paper reports results from the online deployment and demonstrates their value for the plant personnel. © 2008 Elsevier B.V. All rights reserved.