RT info:eu-repo/semantics/conferenceObject
T1 Machine learning and the digital era from a Process Systems Engineering perspective
A1 Pitarch Pérez, José Luis
A1 Prada Moraga, César de
K1 Modelling
K1 Simulation
K1 Machine learning
K1 Data conditioning
K1 Process modeling
K1 Data reconciliation
K1 Constrained regression
K1 Grey-box models
AB Modern sensorization, communication and computational technolo-gies provide collecting and storing huge amounts of raw data from large cyber-physical systems. These data should serve as the basis to take better decisions at all levels (from the design to operation and management). Nevertheless, raw data needs to be transformed in useful information, usually in the form of prediction models. Machine learning plays thus a key role in this task. The process industry is not alien to this digital transformation, although large processing plants present particularities that differentiate them from other systems. These differences, if neglected, can make machine learning for general purpose fail in extracting the right information from data, leading thus to unreliable process models. As such models are the basis on which the ideas towards the cognitive plant rely, this issue is of key importance for a successful full digitalization of the process industry. Here we discuss these aspects, as well as the more suitable machine-learning ap-proaches, through our experience in an industrial case study.
PB ARGESIM
SN 978-3-901608-92-6
YR 2019
FD 2019
LK http://uvadoc.uva.es/handle/10324/40565
UL http://uvadoc.uva.es/handle/10324/40565
LA eng
NO Emilio Jiménez, Juan Ignacio Latorre (eds.), 10th EUROSIM Congress, La Rioja, Logroño, Spain, July 1-5, 2019
NO Producción Científica
DS UVaDOC
RD 27-abr-2024