2026-04-26T23:38:01Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/405652021-12-03T07:41:01Zcom_10324_1168com_10324_931com_10324_894col_10324_1304
UVaDOC
author
Pitarch Pérez, José Luis
author
Prada Moraga, César de
2020-03-03T18:22:23Z
2020-03-03T18:22:23Z
2019
Emilio Jiménez, Juan Ignacio Latorre (eds.), 10th EUROSIM Congress, La Rioja, Logroño, Spain, July 1-5, 2019
978-3-901608-92-6
http://uvadoc.uva.es/handle/10324/40565
10.11128/arep.58
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.
eng
CC0 1.0 Universal
Modelling
Simulation
Machine learning
Machine learning and the digital era from a Process Systems Engineering perspective
info:eu-repo/semantics/conferenceObject
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URL
https://uvadoc.uva.es/bitstream/10324/40565/1/Eurosim2019_paper_23.pdf
File
MD5
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application/pdf
Eurosim2019_paper_23.pdf