Synchronizing process variables in time for industrial process monitoring and control

• Published in: Computers & chemical engineering Vol. 140; p. 106938
• Main Authors: Offermans, Tim, Szymańska, Ewa, Buydens, Lutgarde M.C., Jansen, Jeroen J.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 02.09.2020
• Subjects: Dynamic synchronization; Industry; Process control; Regression; Soft-sensor; Statistical modelling; Industry; Regression; Dynamic synchronization; Process control; Soft-sensor; Statistical modelling
• ISSN: 0098-1354; 1873-4375
• DOI: 10.1016/j.compchemeng.2020.106938

•Production data often has to be synchronized in time before statistical analysis.•The choice in synchronization method matters for the quality of the analysis.•Synchronization using median-filtering gives the highest analysis quality.•Changes in the state of a process may affect the quality longer than expected. The use of soft-sensors in industry is becoming more popular, as they allow for the prediction of critical product qualities from process variables in real-time. The requirement for this that all process variables are dynamically synchronized is often not met. Although different methods for dynamically synchronizing process variables are reported, no critical comparison of these methods is available. In this study we show that the choice in synchronization method significantly influences a soft-sensor's accuracy. From the methods studied, median filtering using a moving window with a width of 168 minutes placed before the target times led to the highest sensor accuracy for the production plant studied, a method not reported in literature. This optimal width is remarkable, as the total processing time of the plant is 30 minutes. This suggests that changes in the physical state of the plant can affect the production quality than one might expect.