Practical challenges in developing data-driven soft sensors for quality prediction

Computer Aided Chemical Engineering

Volumn 25, Issue , 2008, Pages 961-966

  Liu, Jun ^a   Srinivasan, Rajagopalan ^a,b   SelvaGuru, PN ^c  

^a INSTITUTE OF CHEMICAL AND ENGINEERING SCIENCES   (Singapore)

^b NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^c Singapore Refining Company   (Singapore)

Author keywords

neural networks; quality prediction; Soft sensors

Indexed keywords

EID: 53049094932     PISSN: 15707946     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S1570-7946(08)80166-6     Document Type: Article

References (6)

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2. Bonavita N., and Matsko T. Neural network technology applied to refinery inferential analyzer problems. Hydrocarbon Engineering (December 1999)
3. Adnan A., Sani N., Nam S., and Friedman Z. The use of first-principles inference models for crude switching control. ERTC Computer Conference. London, UK (May 2004)
4. Lakshminarayanan S., Tangirala A., Shah S., Akamatsu K., and Ooyama S. Soft sensor design using partial least square and neural networks: Comparison and industrial applications. AICHE annual meeting. Miami, USA (Nov 15-20, 1998)
5. Sturm G.P., and Shay J.Y. Comprehensive report of API crude oil characterization measurements (2000), American Petroleum Institute
6. ASTM Standard D86. Standard Test Method for Distillation of Petroleum Products at Atmospheric Pressure (2007), ASTM International, West Conshohocken, PA. www.astm.org