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Data-driven Soft Sensors in the Process Industry.

Kadlec, P., Gabrys, B. and Strandt, S., 2009. Data-driven Soft Sensors in the Process Industry. Computers and Chemical Engineering, 33 (4), 795-814.

Full text available as:

CACE_KadlecGabrysStrandt_2008.pdf - Accepted Version


DOI: 10.1016/j.compchemeng.2008.12.012


In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work.

Item Type:Article
Uncontrolled Keywords:Soft Sensors; Process industry; Data-driven models; PCA; ANN;
Group:Faculty of Science & Technology
ID Code:8498
Deposited On:19 Dec 2008 09:07
Last Modified:14 Mar 2022 13:19


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