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. 2023 May 25;23(11):5060.
doi: 10.3390/s23115060.

A Distributed IoT Air Quality Measurement System for High-Risk Workplace Safety Enhancement

Lorenzo Parri  1 , Marco Tani  1 , David Baldo  1 , Stefano Parrino  1 , Elia Landi  1 , Marco Mugnaini  1 , Ada Fort  1
Affiliations

A Distributed IoT Air Quality Measurement System for High-Risk Workplace Safety Enhancement

Lorenzo Parri et al. Sensors (Basel). .

Abstract

The safety of an operator working in a hazardous environment is a recurring topic in the technical literature of recent years, especially for high-risk environments such as oil and gas plants, refineries, gas depots, or chemical industries. One of the highest risk factors is constituted by the presence of gaseous substances such as toxic compounds such as carbon monoxide and nitric oxides, particulate matter or indoors, in closed spaces, low oxygen concentration atmospheres, and high concentrations of CO2 that can represent a risk for human health. In this context, there exist many monitoring systems for lots of specific applications where gas detection is required. In this paper, the authors present a distributed sensing system based on commercial sensors aimed at monitoring the presence of toxic compounds generated by a melting furnace with the aim of reliably detecting the insurgence of dangerous conditions for workers. The system is composed of two different sensor nodes and a gas analyzer, and it exploits commercial low-cost commercially available sensors.

Keywords: air quality; distributed measurement system; gas sensors; sensor network; workplace safety.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
A scheme of the developed system composed of internal nodes I, the gas analyzer A, and the LoRaWAN gateway (inside the building where the furnace is hosted). Outside the building, there are the external nodes, O, that differently from the other system is battery powered.
Figure 2
Figure 2
A block diagram of the two sensor nodes, the internal sensor (left) does not use battery power and particulate sensor module while the external sensor (right) does not use NDIR sensor and is powered by battery source.
Figure 3
Figure 3
A block diagram of the gas analyzer.
Figure 4
Figure 4
Carbon monoxide concentration transient measured by the protected sensor (Co_Lo) and by the protection sensor (Co_Hi). T1 and T2 are the threshold values used to activate the proper working condition.
Figure 5
Figure 5
(Left): Psychometric chart at 1 atm showing the dehumidification process in the chiller. (Right): Structure of the chiller.
Figure 6
Figure 6
(A): The gas analyzer installed close to furnace exhaust gas pipeline. (B): A picture of the gas analyzer without the case cover. (C,F): Internal sensor node pictures. (D,E): External battery-powered sensor node pictures.
Figure 7
Figure 7
Grafana-based dashboard of the front-end showing gas concentration trends.
Figure 8
Figure 8
Gas concentrations measured by the gas analyzer at the melting furnace exhaust.
Figure 9
Figure 9
Gas concentrations measured by the external and internal nodes.
Figure 10
Figure 10
UML state machine diagram of the safe decision algorithm implemented on the server.
See this image and copyright information in PMC

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