Enhanced Heat-Powered Batteryless IIoT Architecture with NB-IoT for Predictive Maintenance in the Oil and Gas Industry
The carbon footprint associated with human activity, particularly from energy-intensive industries such as iron and steel, aluminium, cement, oil and gas, and petrochemicals, contributes significantly to global warming. These industries face unique challenges in achieving Industry 4.0 goals due to t...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:311407 |
| Acceso en línea: | https://ddd.uab.cat/record/311407 https://dx.doi.org/urn:doi:10.3390/s25082590 |
| Access Level: | acceso abierto |
| Palabra clave: | Energy harvesting Thermoelectricity LCA Carbon footprint NB-IoT Edge computing NETZERO Energy-intensive industry |
| Sumario: | The carbon footprint associated with human activity, particularly from energy-intensive industries such as iron and steel, aluminium, cement, oil and gas, and petrochemicals, contributes significantly to global warming. These industries face unique challenges in achieving Industry 4.0 goals due to the widespread adoption of industrial Internet of Things (IIoT) technologies, which require reliable and efficient power solutions. Conventional wireless devices powered by lithium batteries have limitations, including a reduced lifespan in high-temperature environments, incompatibility with explosive atmospheres, and high maintenance costs. This paper proposes a novel approach to address these challenges by leveraging residual heat to power IIoT devices, eliminating the need for batteries and enabling autonomous operation. Based on the Seebeck effect, thermoelectric energy harvesters transduce waste heat from industrial surfaces, such as pipes or chimneys, into sufficient electrical energy to power IoT nodes for applications like the condition monitoring and predictive maintenance of rotating machinery. The methodology presented standardises the modelling and simulation of Waste Heat Recovery Systems (IoT-WHRSs), demonstrating their feasibility through statistical analysis of IoT-WHRS architectures. Furthermore, this technology has been successfully implemented in a petroleum refinery, where it benefits from the NB-IoT standard for long-range, robust, and secure communications, ensuring reliable data transmission in harsh industrial environments. The results highlight the potential of this solution to reduce costs, improve safety, and enhance efficiency in demanding industrial applications, making it a valuable tool for the energy transition. |
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