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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

  • Outotec GmbH

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.

Details

Original languageEnglish
Title of host publication4th Conference on Control and Fault Tolerant Systems (SysTol)
Publication statusPublished - 14 Oct 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Control and Fault-Tolerant Systems - Casabalanca, Morocco
Duration: 18 Sep 201920 Sep 2019
Conference number: 4
http://www.systol.org/systol19/

Publication series

NameConference on Control and Fault Tolerant Systems (SysTol)
PublisherIEEE
ISSN (Print)2162-1195
ISSN (Electronic)2162-1209

Conference

ConferenceInternational Conference on Control and Fault-Tolerant Systems
Abbreviated titleSysTol
CountryMorocco
CityCasabalanca
Period18/09/201920/09/2019
Internet address

    Research areas

  • 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

ID: 39374039