Long-Term Industrial Applications of Inferential Control Based on Just-In-Time Soft-Sensors: Economical Impact and Challenges

Abstract

Many research works on soft-sensors have been conducted. Although it is common practice to evaluate the estimation performance of soft-sensors by using industrial process data, few papers have reported long-term application results of process control using soft-sensors in real processes. In the present work, a practical configuration of an inferential control system was developed that integrated a commercial model predictive control (MPC) software and a just-in-time (JIT) soft-sensor. The developed system has adopted locally weighted partial least squares (LW-PLS) to build soft-sensors. LW-PLS is a kind of JIT modeling method that can cope with changes in process characteristics as well as process nonlinearity. Thus, LW-PLS helps engineers to reduce their burden of model maintenance, which has been recognized as the most serious problem in practice. The usefulness of the developed LW-PLS-based soft-sensors and inferential control systems is demonstrated through their successful industrial applications to a cracked gasoline (CGL) fractionator and a purification section for an acetyl plant. Inferential control systems have been used for more than a year at Showa Denko K.K. (SDK) in Japan. The operation cost and environmental burden have been significantly reduced. In the CGL fractionator, for example, about 0.6% of operation cost was cut successfully. In addition, the present work aims to describe challenges, revealed by the long-term applications of JIT soft-sensors: the parameter tuning, the selection of input variables, the definition of similarity in JIT modeling, the management of the database, and the assessment and enhancement of soft-sensor reliability.