Battery-Less Industrial Wireless Monitoring and Control System for Improved Operational Efficiency

An industrial wireless monitoring and control system, capable of supporting energy-harvesting devices through smart sensing and network management, designed for improving electro-refinery performance by applying predictive maintenance, is presented. The system is self-powered from bus bars, and feat...

Full description

Saved in:
Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 23; no. 5; p. 2517
Main Authors Hidalgo-Fort, Eduardo, Gómez-Galán, Juan Antonio, González-Carvajal, Ramón, Sánchez-Cárdenas, Pedro, Clemente-Maya, Carlos
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 24.02.2023
MDPI
Subjects
battery-less
Control systems
Current efficiency
Efficiency
Electrolytes
Energy consumption
Energy harvesting
industrial wireless monitoring
Internet of Things
LoRaWAN
Mobile communication systems
Monitoring
Neural networks
Power supply
Predictive maintenance
Production management
Refineries
Sensors
Short circuits
Technology application
Wireless communication systems
Wireless communications
wireless sensor network
Spain
energy harvesting
LoRaWAN
industrial wireless monitoring
wireless sensor network
battery-less
Online AccessGet full text

Cover

More Information
Summary:An industrial wireless monitoring and control system, capable of supporting energy-harvesting devices through smart sensing and network management, designed for improving electro-refinery performance by applying predictive maintenance, is presented. The system is self-powered from bus bars, and features wireless communication and easy-to-access information and alarms. With cell voltage and electrolyte temperature measurements, the system enables real-time cell performance discovery and early reaction to critical production or quality disturbances such as short-circuiting, flow blockages, or electrolyte temperature excursions. Field validation shows an increase in operational performance of 30% (reaching 97%) in the detection of short circuits, which, thanks to a neural network deployed, are detected, on average, 10.5 h earlier compared to the traditional methodology. The developed system is a sustainable IoT solution, being easy to maintain after its deployment, and providing benefits of improved control and operation, increased current efficiency, and decreased maintenance costs.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s23052517