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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 | Citation: | 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 |
Appears in Collections: | Staff Publications |
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