Industry 4.0 based process data analytics platform: A waste-to-energy plant case study

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Industry 4.0 based process data analytics platform : A waste-to-energy plant case study. / Kabugo, James Clovis; Jämsä-Jounela, Sirkka Liisa; Schiemann, Robert; Binder, Christian.

In: International Journal of Electrical Power and Energy Systems, Vol. 115, 105508, 01.02.2020.

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@article{42e046c029994d56a731143236d4d222,
title = "Industry 4.0 based process data analytics platform: A waste-to-energy plant case study",
abstract = "Industry 4.0 and Industrial Internet of Things (IIoT) technologies are rapidly fueling data and software solutions driven digitalization in many fields notably in industrial automation and manufacturing systems. Among the several benefits offered by these technologies, is the infrastructure for harnessing big-data, machine learning (ML) and cloud computing software tools, for instance in designing advanced data analytics platforms. Although, this is an area of increased interest, the information concerning the implementation of data analytics in the context of Industry 4.0 is scarcely available in scientific literature. Therefore, this work presents a process data analytics platform built around the concept of industry 4.0. The platform utilizes the state-of-the-art IIoT platforms, ML algorithms and big-data software tools. The platform emphasizes the use of ML methods for process data analytics while leveraging big-data processing tools and taking advantage of the currently available industrial grade cloud computing platforms. 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. Among the studied data-driven methods, the neural network-based NARX model demonstrated better performance in the prediction of both syngas heating value and flue gas temperature. The modeling results showed that, in cases where process knowledge about the process phenomena at hand is limited, data-driven soft sensors are useful tools for predictive data analytics.",
keywords = "Data analytics platform, Industrial internet of things platform, Machine learning, Soft sensor, Waste-to-energy",
author = "Kabugo, {James Clovis} and J{\"a}ms{\"a}-Jounela, {Sirkka Liisa} and Robert Schiemann and Christian Binder",
year = "2020",
month = "2",
day = "1",
doi = "10.1016/j.ijepes.2019.105508",
language = "English",
volume = "115",
journal = "International Journal of Electrical Power and Energy Systems",
issn = "0142-0615",
publisher = "Elsevier Limited",

}

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TY - JOUR

T1 - Industry 4.0 based process data analytics platform

T2 - A waste-to-energy plant case study

AU - Kabugo, James Clovis

AU - Jämsä-Jounela, Sirkka Liisa

AU - Schiemann, Robert

AU - Binder, Christian

PY - 2020/2/1

Y1 - 2020/2/1

N2 - Industry 4.0 and Industrial Internet of Things (IIoT) technologies are rapidly fueling data and software solutions driven digitalization in many fields notably in industrial automation and manufacturing systems. Among the several benefits offered by these technologies, is the infrastructure for harnessing big-data, machine learning (ML) and cloud computing software tools, for instance in designing advanced data analytics platforms. Although, this is an area of increased interest, the information concerning the implementation of data analytics in the context of Industry 4.0 is scarcely available in scientific literature. Therefore, this work presents a process data analytics platform built around the concept of industry 4.0. The platform utilizes the state-of-the-art IIoT platforms, ML algorithms and big-data software tools. The platform emphasizes the use of ML methods for process data analytics while leveraging big-data processing tools and taking advantage of the currently available industrial grade cloud computing platforms. 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. Among the studied data-driven methods, the neural network-based NARX model demonstrated better performance in the prediction of both syngas heating value and flue gas temperature. The modeling results showed that, in cases where process knowledge about the process phenomena at hand is limited, data-driven soft sensors are useful tools for predictive data analytics.

AB - Industry 4.0 and Industrial Internet of Things (IIoT) technologies are rapidly fueling data and software solutions driven digitalization in many fields notably in industrial automation and manufacturing systems. Among the several benefits offered by these technologies, is the infrastructure for harnessing big-data, machine learning (ML) and cloud computing software tools, for instance in designing advanced data analytics platforms. Although, this is an area of increased interest, the information concerning the implementation of data analytics in the context of Industry 4.0 is scarcely available in scientific literature. Therefore, this work presents a process data analytics platform built around the concept of industry 4.0. The platform utilizes the state-of-the-art IIoT platforms, ML algorithms and big-data software tools. The platform emphasizes the use of ML methods for process data analytics while leveraging big-data processing tools and taking advantage of the currently available industrial grade cloud computing platforms. 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. Among the studied data-driven methods, the neural network-based NARX model demonstrated better performance in the prediction of both syngas heating value and flue gas temperature. The modeling results showed that, in cases where process knowledge about the process phenomena at hand is limited, data-driven soft sensors are useful tools for predictive data analytics.

KW - Data analytics platform

KW - Industrial internet of things platform

KW - Machine learning

KW - Soft sensor

KW - Waste-to-energy

UR - http://www.scopus.com/inward/record.url?scp=85071276679&partnerID=8YFLogxK

U2 - 10.1016/j.ijepes.2019.105508

DO - 10.1016/j.ijepes.2019.105508

M3 - Article

VL - 115

JO - International Journal of Electrical Power and Energy Systems

JF - International Journal of Electrical Power and Energy Systems

SN - 0142-0615

M1 - 105508

ER -

ID: 36595752