Abstract
Soft-Sensors for Estimating in Real-Time Important Quality Variables Are a Key Technology in Modern Process Industry. the Successful Development of a Soft-Sensor Whose Performance Does Not Deteriorate with Time and Changing Process Characteristics is Troublesome and Only Seldom Achieved in Real-World Setups. the Design of Soft-Sensors based on Local Regression Models is Becoming Popular. Simplicity of Calibration, Ability to Handle Nonlinearities And, Most Importantly, Reduced Maintenance Costs While Retaining the Requested Accuracy Are the Major Assets. in This Paper, We Introduce Several Approaches for Defining an Appropriate Locality Neighborhood and We Propose a Recursive Version of Local Linear Regression for Soft-Sensor Design. to Support the Presentation, We Discuss the Results in Designing a Soft-Sensor for Estimating the Ethane Concentration from the Bottom of a Full-Scale Deethanizer. © 2011 Ifac.
Recommended Citation
Z. Zhu et al., "Local Linear Regression for Soft-Sensor Design with Application to an Industrial Deethanizer," IFAC Proceedings Volumes (IFAC-PapersOnline), vol. 44, no. 1 PART 1, pp. 2839 - 2844, Elsevier, Jan 2011.
The definitive version is available at https://doi.org/10.3182/20110828-6-IT-1002.02357
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Local linear regression; Process monitoring; Soft-sensors
International Standard Book Number (ISBN)
978-390266193-7
International Standard Serial Number (ISSN)
1474-6670
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Elsevier, All rights reserved.
Publication Date
01 Jan 2011
