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

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Conference article in proceedings
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Author
Date
2019-10-14
Department
Process Control and Automation
Department of Chemical and Metallurgical Engineering
Outotec GmbH
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Mcode
Degree programme
Language
en
Pages
6
264-269
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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.
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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
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DOI
10.1109/SYSTOL.2019.8864766
Citation
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 . in 4th Conference on Control and Fault Tolerant Systems (SysTol) . Conference on Control and Fault Tolerant Systems (SysTol) , IEEE , pp. 264-269 , International Conference on Control and Fault-Tolerant Systems , Casabalanca , Morocco , 18/09/2019 . https://doi.org/10.1109/SYSTOL.2019.8864766
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https://urn.fi/URN:NBN:fi:aalto-201911076068
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[article-cris] Kemian tekniikan korkeakoulu / CHEM