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Data-driven soft-sensors for online monitoring of batch processes with different initial conditions

Author
Shokry , A.; Vicente, P.; Escudero, G.; Pérez-Moya, M.; Graells, M.; Espuña, A.
Type of activity
Journal article
Journal
Computers & chemical engineering
Date of publication
2018-10-04
Volume
118
First page
159
Last page
179
DOI
10.1016/j.compchemeng.2018.07.014
Repository
http://hdl.handle.net/2117/131976 Open in new window
URL
https://www.sciencedirect.com/science/article/pii/S0098135418307117 Open in new window
Abstract
A soft-sensing methodology applicable to batch processes operated under changeable initial conditions is presented. These cases appear when the raw materials specifications differ from batch to batch, different production scenarios should be managed, etc. The proposal exploits the capabilities of the machine learning techniques to provide practical soft-sensing approach with minimum tuning effort in spite of the fact that the inherent dynamic behavior of batch systems are tracked through other o...
Citation
Shokry , A. [et al.]. Data-driven soft-sensors for online monitoring of batch processes with different initial conditions. "Computers & chemical engineering", 4 Octubre 2018, vol. 118, p. 159-179.
Keywords
Artificial neural networks, Batch processes, Ordinary Kriging, Photo-Fenton, Soft-sensors, Support vector machines
Group of research
CEPIMA - Centre d'Enginyeria de Processos i Medi Ambient
GPLN - Natural Language Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
TALP - Centre for Language and Speech Technologies and Applications

Participants