Abstrakti
This work presents a process data analytics platform built around the concept of industry 4.0. The platform utilizes the state-of-the-art industry internet of things (IIoT) platforms, machine learning (ML) algorithms and big-data software tools. The industrial applicability of the platform was demonstrated by the development of soft sensors for use in a waste-to-energy (WTE) plant. In the case study, the work studied data-driven soft sensors to predict syngas heating value and hot flue gas temperature. From data-driven models, the neural network based nonlinear autoregressive with external input (NARX) model demonstrated better performance in prediction of both syngas heating value and flue gas temperature in a WTE process.
Alkuperäiskieli | Englanti |
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Otsikko | 4th Conference on Control and Fault Tolerant Systems (SysTol) |
Kustantaja | IEEE |
Sivut | 264-269 |
Sivumäärä | 6 |
ISBN (elektroninen) | 978-1-7281-0380-8 |
ISBN (painettu) | 978-1-7281-0381-5 |
DOI - pysyväislinkit | |
Tila | Julkaistu - 14 lokak. 2019 |
OKM-julkaisutyyppi | A4 Artikkeli konferenssijulkaisuussa |
Tapahtuma | International Conference on Control and Fault-Tolerant Systems - Casabalanca, Marokko Kesto: 18 syysk. 2019 → 20 syysk. 2019 Konferenssinumero: 4 http://www.systol.org/systol19/ |
Julkaisusarja
Nimi | Conference on Control and Fault Tolerant Systems (SysTol) |
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Kustantaja | IEEE |
ISSN (painettu) | 2162-1195 |
ISSN (elektroninen) | 2162-1209 |
Conference
Conference | International Conference on Control and Fault-Tolerant Systems |
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Lyhennettä | SysTol |
Maa/Alue | Marokko |
Kaupunki | Casabalanca |
Ajanjakso | 18/09/2019 → 20/09/2019 |
www-osoite |