Please use this identifier to cite or link to this item: https://doi.org/10.1016/S1570-7946(08)80166-6
Title: Practical challenges in developing data-driven soft sensors for quality prediction
Authors: Liu, J.
Srinivasan, R. 
SelvaGuru, PN.
Keywords: neural networks
quality prediction
Soft sensors
Issue Date: 2008
Source: Liu, J.,Srinivasan, R.,SelvaGuru, PN. (2008). Practical challenges in developing data-driven soft sensors for quality prediction. Computer Aided Chemical Engineering 25 : 961-966. ScholarBank@NUS Repository. https://doi.org/10.1016/S1570-7946(08)80166-6
Abstract: With improved quality control, a refinery plant can operate closer to optimum values. However, real-time measurement of product quality is generally difficult. On-line prediction of quality using frequent process measurements would therefore be beneficial. In this paper, our learnings from developing and deploying a data-driven soft sensor for a refinery unit are presented. Key challenges in developing a practicable soft sensor for actual use in a plant are discussed and our solutions to these presented. Finally, this paper reports results from the online deployment and demonstrates their value for the plant personnel. © 2008 Elsevier B.V. All rights reserved.
Source Title: Computer Aided Chemical Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/64443
ISBN: 9780444532275
ISSN: 15707946
DOI: 10.1016/S1570-7946(08)80166-6
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