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

Loading...
Thumbnail Image
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Date
2019-10-14
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
264-269
Series
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
Other note
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