Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study

James Kabugo; Sirkka-Liisa Jämsä-Jounela; Robert Schiemann; Christian Binder

doi:10.1109/SYSTOL.2019.8864766

Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study

James Kabugo, Sirkka-Liisa Jämsä-Jounela, Robert Schiemann, Christian Binder

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

46 Lataukset (Pure)

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
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	https://doi.org/10.1109/SYSTOL.2019.8864766
Tila	Julkaistu - 14 lokakuuta 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisuussa
Tapahtuma	International Conference on Control and Fault-Tolerant Systems - Casabalanca, Marokko Kesto: 18 syyskuuta 2019 → 20 syyskuuta 2019 Konferenssinumero: 4 http://www.systol.org/systol19/

Julkaisusarja

Nimi	Conference on Control and Fault Tolerant Systems (SysTol)
Kustantaja	IEEE
ISSN (painettu)	2162-1195
ISSN (elektroninen)	2162-1209

Conference

Conference	International Conference on Control and Fault-Tolerant Systems
Lyhennettä	SysTol
Maa	Marokko
Kaupunki	Casabalanca
Ajanjakso	18/09/2019 → 20/09/2019
www-osoite	http://www.systol.org/systol19/

Pääsy asiakirjaan

10.1109/SYSTOL.2019.8864766

CHEM-Kabugo et al-Process monitoring-2019-ieee-conference-control-faultAccepted author manuscript, 2,15 MBLisenssi: Määrittelemätön

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Siteeraa tätä

Kabugo, J., Jämsä-Jounela, S-L., Schiemann, R., & Binder, C. (2019). Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study. teoksessa 4th Conference on Control and Fault Tolerant Systems (SysTol) (Sivut 264-269). (Conference on Control and Fault Tolerant Systems (SysTol)). IEEE. https://doi.org/10.1109/SYSTOL.2019.8864766

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title = "Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study",

abstract = "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.",

keywords = "Industrial internet of things , machine learning , waste-to-energy , soft sensor, machine learning, waste-to-energy, soft sensor, Cloud computing, Data analysis, Temperature sensors, Data models, Automation",

author = "James Kabugo and Sirkka-Liisa J{\"a}ms{\"a}-Jounela and Robert Schiemann and Christian Binder",

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Kabugo, J, Jämsä-Jounela, S-L, Schiemann, R & Binder, C 2019, Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study. julkaisussa 4th Conference on Control and Fault Tolerant Systems (SysTol). Conference on Control and Fault Tolerant Systems (SysTol), IEEE, Sivut 264-269, International Conference on Control and Fault-Tolerant Systems, Casabalanca, Marokko, 18/09/2019. https://doi.org/10.1109/SYSTOL.2019.8864766

Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study. / Kabugo, James; Jämsä-Jounela, Sirkka-Liisa; Schiemann, Robert; Binder, Christian.

4th Conference on Control and Fault Tolerant Systems (SysTol). IEEE, 2019. s. 264-269 (Conference on Control and Fault Tolerant Systems (SysTol)).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference contribution › Scientific › vertaisarvioitu

TY - GEN

T1 - Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study

AU - Kabugo, James

AU - Jämsä-Jounela, Sirkka-Liisa

AU - Schiemann, Robert

AU - Binder, Christian

PY - 2019/10/14

Y1 - 2019/10/14

N2 - 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.

AB - 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.

KW - Industrial internet of things , machine learning , waste-to-energy , soft sensor

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KW - Data analysis

KW - Temperature sensors

KW - Data models

KW - Automation

U2 - 10.1109/SYSTOL.2019.8864766

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