Innovative data regression incorporating deterministic knowledge for soft sensing in the process industry. (August 2019)

• Record Type: Journal Article
• Authors: Copertaro, Edoardo; Chiariotti, Paolo; Revel, Gian Marco; Paone, Nicola
• Is Part Of: Journal of process control. Volume 80(2019)
• Page Start: 180
• Page End: 192
• Publication Date: 2019-08
• Subjects: Cement industry -- Soft sensing -- Process monitoring -- Process control -- Finite principle modeling; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2019.06.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: The method includes deterministic knowledge into data regression. The method predicts unburned calcium oxide in clinkering process. The method predicts max temperature in new clinkering process based on microwaves. The method is trained and validated using real data. Abstract: Soft sensing is a monitoring technique for the indirect assessment of a target variable by means of direct measurements of others and the application of data mining on the historical log, as well as simplified models of the system. Due to technical and economic advantages respect to hardware sensing, soft sensing has been increasingly used in many scenarios, in particular within the process industry. Despite the literature being wide regarding the application of conventional regression techniques on data provided by the monitoring hardware, a systematic approach for supporting and improving data regression through the deterministic knowledge of the process is still missing. This contribution presents an innovative regression method based on first principle modeling. The method is introduced and tested under the case scenario of conventional clinker making process, for predicting the instantaneous fraction of the unburned calcium oxide. A second test case involves an innovative clinkering stage based on microwaves application.