Original scientific paper
Methods for Plant Data-Based Process Modeling in Soft-Sensor Development
Dražen Slišković
; University of Osijek, Faculty of Electrical Engineering, Kneza Trpimira 2b, HR-31000, Osijek, Croatia
Ratko Grbić
; University of Osijek, Faculty of Electrical Engineering, Kneza Trpimira 2b, HR-31000, Osijek, Croatia
Željko Hocenski
; University of Osijek, Faculty of Electrical Engineering, Kneza Trpimira 2b, HR-31000, Osijek, Croatia
Abstract
There has been an increased use of soft-sensors in process industry in recent years. These soft-sensors are computer programs that are used as a relatively cheap alternative to hardware sensors. Since process variables, which are concerned with final product quality, cannot always be measured by hardware sensors, designing the appropriate soft-sensor can be an interesting solution. Additionally, a soft-sensor can be used as a backup sensor, when the hardware sensor is in fault or removed due to maintenance or replacement. Soft-sensor is based on the mathematical model of the process. Since industrial processes are generally quite complex, a theoretical modeling approach is often impractical, expensive or sometimes even impossible. Therefore, process model building is based on measured data. This approach significantly gets complicated if only plant data, taken from the process database, are available. In this paper the most popular methods for plant data-based modeling that appeared in the last two decades are summarized and briefly explained. Apart from giving a short survey of the most important papers, tips about choosing the appropriate methodology for process model building are also provided.
Keywords
Data-based process modeling; Plant data; Soft-sensor; Modeling methods overview; Papers overview
Hrčak ID:
78298
URI
Publication date:
6.3.2012.
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