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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

Standard

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/konferenssijulkaisussavertaisarvioitu

Harvard

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

APA

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

Vancouver

Kabugo J, Jämsä-Jounela S-L, Schiemann R, Binder C. 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). IEEE. 2019. s. 264-269. (Conference on Control and Fault Tolerant Systems (SysTol)). https://doi.org/10.1109/SYSTOL.2019.8864766

Author

Kabugo, James ; Jämsä-Jounela, Sirkka-Liisa ; Schiemann, Robert ; Binder, Christian. / Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study. 4th Conference on Control and Fault Tolerant Systems (SysTol). IEEE, 2019. Sivut 264-269 (Conference on Control and Fault Tolerant Systems (SysTol)).

Bibtex - Lataa

@inproceedings{3e44ccebc12949f08468f8c7aa92a791,
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",
year = "2019",
month = "10",
day = "14",
doi = "10.1109/SYSTOL.2019.8864766",
language = "English",
isbn = "978-1-7281-0381-5",
series = "Conference on Control and Fault Tolerant Systems (SysTol)",
publisher = "IEEE",
pages = "264--269",
booktitle = "4th Conference on Control and Fault Tolerant Systems (SysTol)",
address = "United States",

}

RIS - Lataa

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

KW - machine learning

KW - waste-to-energy

KW - soft sensor

KW - Cloud computing

KW - Data analysis

KW - Temperature sensors

KW - Data models

KW - Automation

U2 - 10.1109/SYSTOL.2019.8864766

DO - 10.1109/SYSTOL.2019.8864766

M3 - Conference contribution

SN - 978-1-7281-0381-5

T3 - Conference on Control and Fault Tolerant Systems (SysTol)

SP - 264

EP - 269

BT - 4th Conference on Control and Fault Tolerant Systems (SysTol)

PB - IEEE

ER -

ID: 39374039