- author = "Petr Kadlec and Bogdan Gabrys and Sibylle Strandt",
- title = "Data-driven Soft Sensors in the process industry",
- journal = "Computers \& Chemical Engineering",
- year = "2009",
- volume = "33",
- number = "4",
- pages = "795--814",
- keywords = "genetic algorithms, genetic programming, Soft Sensors, Process industry, Data-driven models, PCA, ANN",
- ISSN = "0098-1354",
-
URL = "
https://eprints.bournemouth.ac.uk/8498/1/CACE_KadlecGabrysStrandt_2008.pdf",
-
DOI = "
10.1016/j.compchemeng.2008.12.012",
- size = "20 pages",
- abstract = "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 our main contributions",
-
notes = "Survey
Smart Technology Research Centre, Computational Intelligence Research Group, Bournemouth University, Poole BH12 5BB, United Kingdom",
