4321ea1638cc1fa1c2ff964e4d371055 information1500330v3.pdf 149794e3ae16b2c5f5bacec47da548eb477a6012 information1500330v3.pdf 31d37295b48d1d1a35a6a3dc8fce7cba2b9aaaa184794e084b8bbb1b01680c92 information1500330v3.pdf Title: Model and Implementation of a Novel Heat-Powered Battery-Less IIoT Architecture for Predictive Industrial Maintenance Subject: The research and management of Industry 4.0 increasingly relies on accurate real-time quality data to apply efficient algorithms for predictive maintenance. Currently, Low-Power Wide-Area Networks (LPWANs) offer potential advantages in monitoring tasks for predictive maintenance. However, their applicability requires improvements in aspects such as energy consumption, transmission range, data rate and constant quality of service. Commonly used battery-operated IIoT devices have several limitations in their adoption in large facilities or heat-intensive industries (iron and steel, cement, etc.). In these cases, the self-heating nodes together with the appropriate low-power processing platform and industrial sensors are aligned with the requirements and real-time criteria required for industrial monitoring. From an environmental point of view, the carbon footprint associated with human activity leads to a steady rise in global average temperature. Most of the gases emitted into the atmosphere are due to these heat-intensive industries. In fact, much of the energy consumed by industries is dissipated in the form of waste heat. With this scenario, it makes sense to build heat transformation collection systems as guarantors of battery-free self-powered IIoT devices. Thermal energy harvesters work on the physical basis of the Seebeck effect. In this way, this paper gathers the methodology that standardizes the modelling and simulation of waste heat recovery systems for IoT nodes, gathering energy from any hot surface, such as a pipe or chimney. The statistical analysis is carried out with the data obtained from two different IoT architectures showing a good correlation between model simulation and prototype behaviour. Additionally, the selected model will be coupled to a low-power processing platform with LoRaWAN connectivity to demonstrate its effectiveness and self-powering ability in a real industrial environment. Keywords: energy harvesting; thermoelectricity; LCA; carbon footprint; LoRaWAN; edge computing; NETZERO; energy intensive industry Author: Raúl Aragonés, Joan Oliver, Roger Malet, Maria Oliver-Parera and Carles Ferrer Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Tue Jun 25 13:07:20 2024 CEST ModDate: Wed Jun 26 06:19:10 2024 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 20 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 7592819 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- MQFYIK+VnURWPalladioL-Bold Type 1 Custom yes yes yes 10 0 RRWVIL+URWPalladioL-Bold Type 1 Custom yes yes yes 16 0 OMWJDQ+URWPalladioL-Roma Type 1 Custom yes yes yes 22 0 BPSWAP+URWPalladioL-Ital Type 1 Custom yes yes yes 27 0 ONRKVR+VnURWPalladioL Type 1 Custom yes yes 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2024-10-23 13:59:30 CEST Size: 7592819 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 1012 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Children: Item: Title: Waste Heat Recovery Systems (WHRS) Destination: subsection.1.1 Item: Title: WHRSs in IoT and Harsh Environments Destination: subsection.1.2 Item: Title: WHRS Modelling Destination: subsection.1.3 Item: Title: WHRS Architectures for IoT Destination: section.2 Item: Title: IoT-WHRS Model Parametrization Destination: section.3 Children: Item: Title: IoT-WHRS RC Model Destination: subsection.3.1 Item: Title: Heat Propagation Contribution of the TEG Channel Destination: subsection.3.2 Item: Title: Contribution of the Air Gap Channel to the Heat Propagation Destination: subsection.3.3 Item: Title: TEG Efficient Parameters Model Destination: section.4 Item: Title: IoT-WHRS Finite Difference Model Destination: section.5 Item: Title: Simulation Results Destination: section.6 Item: Title: Low-Power Autonomous Hardware Architecture Destination: section.7 Item: Title: Experimental Results Destination: section.8 Children: Item: Title: TEG Module Experiments Destination: subsection.8.1 Item: Title: Experimental Data Analysis Destination: subsection.8.2 Item: Title: IoT Module in the Facility Destination: subsection.8.3 Item: Title: Hardware Integration in a Real Pilot Destination: subsection.8.4 Item: Title: Conclusions Destination: section.9 Item: Title: References Destination: section.10 Info: Title: Model and Implementation of a Novel Heat-Powered Battery-Less IIoT Architecture for Predictive Industrial Maintenance Author: Raúl Aragonés, Joan Oliver, Roger Malet, Maria Oliver-Parera and Carles Ferrer Subject: The research and management of Industry 4.0 increasingly relies on accurate real-time quality data to apply efficient algorithms for predictive maintenance. Currently, Low-Power Wide-Area Networks (LPWANs) offer potential advantages in monitoring tasks for predictive maintenance. However, their applicability requires improvements in aspects such as energy consumption, transmission range, data rate and constant quality of service. Commonly used battery-operated IIoT devices have several limitations in their adoption in large facilities or heat-intensive industries (iron and steel, cement, etc.). In these cases, the self-heating nodes together with the appropriate low-power processing platform and industrial sensors are aligned with the requirements and real-time criteria required for industrial monitoring. From an environmental point of view, the carbon footprint associated with human activity leads to a steady rise in global average temperature. Most of the gases emitted into the atmosphere are due to these heat-intensive industries. In fact, much of the energy consumed by industries is dissipated in the form of waste heat. With this scenario, it makes sense to build heat transformation collection systems as guarantors of battery-free self-powered IIoT devices. Thermal energy harvesters work on the physical basis of the Seebeck effect. In this way, this paper gathers the methodology that standardizes the modelling and simulation of waste heat recovery systems for IoT nodes, gathering energy from any hot surface, such as a pipe or chimney. The statistical analysis is carried out with the data obtained from two different IoT architectures showing a good correlation between model simulation and prototype behaviour. Additionally, the selected model will be coupled to a low-power processing platform with LoRaWAN connectivity to demonstrate its effectiveness and self-powering ability in a real industrial environment. 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