Framework for digitalizing different industrial sectors via the Internet of Things
Ullah, Mehar (2023-12-18)
Väitöskirja
Ullah, Mehar
18.12.2023
Lappeenranta-Lahti University of Technology LUT
Acta Universitatis Lappeenrantaensis
School of Energy Systems
School of Energy Systems, Sähkötekniikka
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In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Lappeenranta-Lahti University of Technology LUT's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_ standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.
Julkaisun pysyvä osoite on
https://urn.fi/URN:ISBN:978-952-412-031-9
https://urn.fi/URN:ISBN:978-952-412-031-9
Tiivistelmä
The Industrial Revolution 4.0 (IR4.0) refers to the current era of technological advancements that are transforming the different sectors of productive activities. It brings what is called Industry 4.0 characterized by the convergence of physical and digital systems that support the automation of several tasks. In this context, the Internet of Things (IoT) is enabling companies to make more informed decisions and operate more efficiently by providing a data network of physical objects that are connected to one another through dedicated applications. The industrial IoT has emerged as a key component of Industry 4.0, as it enables the collection of vast amounts of industrial data that can be used to optimize processes and improve decision-making. Data platforms have been developed to build and run different IoT applications, i.e., to process the huge amount of data generated by the IoT devices that are as diverse as the different industrial sectors. Handling such a massive amount of data is a challenge, leading to an approach usually called big data.
This doctoral dissertation explores different aspects of the IoT and explains how different parts of the IoT can work together to support a given application, especially in the industry. The focus is on the IoT data platforms—systems that enable the deployment and management of IoT devices. They are essential for data collection, analysis, and visualization by enabling a set of tools and services for device management, data analytics, and application development, as well as for support of various communication protocols and standards. Despite the generality of these processes related to the IoT, each industrial sector has specific requirements.
To enable companies to implement their specific IoT applications, an IoT platform is needed. The market offers a multitude of IoT platforms, each sharing similar functionalities but differing in their implementation and underlying technologies. These technological advancements present numerous difficulties for businesses and government entities, especially when dealing with the IoT infrastructure and platforms, which may be unfamiliar to many players in the field. Choosing an appropriate IoT platform from the available choices is a complex undertaking because this decision must consider not only current requirements but also potential future demands. This dissertation aims to answer the question: “Is it possible to create a unified framework for the digitalization of industrial sectors based on the IoT in integration with technologies like big data and analytics and edge computing?” The study also aims to answer the following subquestions: 1) How would a unified framework look like that can be used for the selection of an IoT platform based on companies’ business requirements? 2) What happens when such a unified framework is applied to a specific domain of Industrial Energy Management? and 3) Can such an approach be deployed in different industrial sectors?
This dissertation offers a unified approach to solve practical deployment issues when digitalizing operations, taking into consideration particular applications. The main contributions of the dissertation can be summarized as follows. First, 21 key factors of an IoT platform required for the selection of a suitable IoT platform are identified for different applications considering the indications provided by the management of the industry, following a five-stage procedure. Second, a theoretical framework for an efficient cyber-physical system design is proposed by covering processes from data collection to end-user decision-making in order to build an industrial energy management system (IEnMS). Third, four different solutions based on the proposed approach are constructed for a diverse set of industrial applications, namely digitalization of a power-to-X plant, a cyber-physical pyrolysis process to recycle carbon fiber-reinforced polymer composite wastes, IoT platform selection for an IEnMS, and the data processing architecture of smart grids.
This doctoral dissertation explores different aspects of the IoT and explains how different parts of the IoT can work together to support a given application, especially in the industry. The focus is on the IoT data platforms—systems that enable the deployment and management of IoT devices. They are essential for data collection, analysis, and visualization by enabling a set of tools and services for device management, data analytics, and application development, as well as for support of various communication protocols and standards. Despite the generality of these processes related to the IoT, each industrial sector has specific requirements.
To enable companies to implement their specific IoT applications, an IoT platform is needed. The market offers a multitude of IoT platforms, each sharing similar functionalities but differing in their implementation and underlying technologies. These technological advancements present numerous difficulties for businesses and government entities, especially when dealing with the IoT infrastructure and platforms, which may be unfamiliar to many players in the field. Choosing an appropriate IoT platform from the available choices is a complex undertaking because this decision must consider not only current requirements but also potential future demands. This dissertation aims to answer the question: “Is it possible to create a unified framework for the digitalization of industrial sectors based on the IoT in integration with technologies like big data and analytics and edge computing?” The study also aims to answer the following subquestions: 1) How would a unified framework look like that can be used for the selection of an IoT platform based on companies’ business requirements? 2) What happens when such a unified framework is applied to a specific domain of Industrial Energy Management? and 3) Can such an approach be deployed in different industrial sectors?
This dissertation offers a unified approach to solve practical deployment issues when digitalizing operations, taking into consideration particular applications. The main contributions of the dissertation can be summarized as follows. First, 21 key factors of an IoT platform required for the selection of a suitable IoT platform are identified for different applications considering the indications provided by the management of the industry, following a five-stage procedure. Second, a theoretical framework for an efficient cyber-physical system design is proposed by covering processes from data collection to end-user decision-making in order to build an industrial energy management system (IEnMS). Third, four different solutions based on the proposed approach are constructed for a diverse set of industrial applications, namely digitalization of a power-to-X plant, a cyber-physical pyrolysis process to recycle carbon fiber-reinforced polymer composite wastes, IoT platform selection for an IEnMS, and the data processing architecture of smart grids.
Kokoelmat
- Väitöskirjat [1038]