Process Monitoring Platform based on Industry 4.0 tools: a waste-to-energy plant case study
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A4 Artikkeli konferenssijulkaisussa
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Date
2019-10-14
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Language
en
Pages
6
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4th Conference on Control and Fault Tolerant Systems (SysTol), pp. 264-269, Conference on Control and Fault Tolerant Systems (SysTol)
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.Description
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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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