Low-cost sensor system for monitoring the oil mist concentration in a workshop

Zhang, Hongsheng; Zhang, Siyi; Pan, Wuxuan; Long, Zhengwei

doi:10.1007/s11356-020-11709-9

Cited by 6 publications

(2 citation statements)

References 37 publications

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“…During the measurement, the ambient temperature was set at 24 • C and the relative humidity was 20%. The instruments and calibration methods used were consistent with those of Zhang et al [43]. Sensor 1 was used as a reference to measure the deviation degree of other sensors relative to Sensor 1.…”

Section: Experimental Procedures and Setupmentioning

confidence: 99%

Numerical Study of Indoor Oil Mist Particle Concentration Distribution in an Industrial Factory Using the Eulerian–Eulerian and Eulerian–Lagrangian Methods

Wang,

Sun,

Zhao

et al. 2023

Fluids

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The transport and prediction of the concentration of particles in confined spaces are crucial for human well-being; this has become particularly evident during the current worldwide pandemic. Computational fluid dynamics (CFD) has been widely used for such predictions, relying on Eulerian–Eulerian (EE) and Eulerian–Lagrangian (EL) models to study particle flow. However, there is a lack of research on industrial factories. In this study, a scaled laboratory in an industrial factory was established for oil mist particles in a machining factory, and oil mist dispersion experiments were conducted under roof exhaust and mixed ventilation conditions. After that, the oil mist concentration distribution in the factory under the same working conditions was calculated by Eulerian and Lagrangian methods, and the corresponding calculation errors and resource consumption were compared. It was found that the simulation results of both methods are acceptable for mixed ventilation and roof exhaust ventilation systems. When there are more vortices in the factory, the Lagrangian method increases the computation time by more than 53% to satisfy the computational accuracy, and the computational error between the Eulerian and Lagrangian methods becomes about 10% larger. For oil mist particles with an aerodynamic diameter of 0.5 μm, both Eulerian and Lagrangian methods have reliable accuracy. Based on the same flow field, the Lagrangian method consumes more than 400 times more computational resources than the Eulerian method. This study can provide a reference for the simulation of indoor particulate transport in industrial factories.

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Section: Experimental Procedures and Setupmentioning

confidence: 99%

Numerical Study of Indoor Oil Mist Particle Concentration Distribution in an Industrial Factory Using the Eulerian–Eulerian and Eulerian–Lagrangian Methods

Wang,

Sun,

Zhao

et al. 2023

Fluids

Self Cite

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show abstract

“…However, large-scale deployment and limited budget usually restrict the choice of sensor hardware, which usually reduces the accuracy and reliability (Barcelo-Ordinas et al 2019;Pineiro Di Blasi et al 2015). In the process of pollutant concentration monitoring in the industrial field, outliers caused by low-cost sensors (Ottosen and Kumar 2019;Zhang et al 2020), which are widely used to reduce deployment costs, must be identified and eliminated to ensure data quality (Malings et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Sensor Outlier Detection Framework For on-Line Monitoring of Particle Pollutants in Multiple Scenarios

Long

Pan

et al. 2021

Preprint

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Monitoring the concentration of particle pollutants is very important for industrial production control and workers' health protection. Low-cost sensors are widely used to reduce deployment costs. The outliers in the observed data of pollutant concentration can be eliminated by outlier detection algorithms. However, it is difficult to meet the actual needs of changing working conditions or scene migration in factories by building a single algorithm for specific scenarios. It is a feasible scheme to identify the changing characteristics of data and adaptively adjust the outlier detection algorithm. From the point of view of data characteristics, we creatively match typical data types with high performance algorithms. The framework proposed in this paper provides a general process including five basic tasks, and uses a modular structure to complete the outlier detection target. The actual pollutant data of the workshops are used to evaluate the performance of our framework. At last, we compare eight different strategies under this framework, and analyze the contribution of each step to outlier detection from the perspective of algorithm principle. The results show that low-cost sensors following the framework can meet the outlier detection requirements in the field of pollutant monitoring, thus greatly reducing the cost of algorithm selection and data adaptation.

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Air pollution forecasting based on wireless communications: review

Fadhil,

Gharghan,

Saeed

2023

Environ Monit Assess

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Low-cost sensor system for monitoring the oil mist concentration in a workshop

Cited by 6 publications

References 37 publications

Numerical Study of Indoor Oil Mist Particle Concentration Distribution in an Industrial Factory Using the Eulerian–Eulerian and Eulerian–Lagrangian Methods

Numerical Study of Indoor Oil Mist Particle Concentration Distribution in an Industrial Factory Using the Eulerian–Eulerian and Eulerian–Lagrangian Methods

Low-Cost Sensor Outlier Detection Framework For on-Line Monitoring of Particle Pollutants in Multiple Scenarios

Air pollution forecasting based on wireless communications: review

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Low-cost sensor system for monitoring the oil mist concentration in a workshop

Cited by 6 publications

References 37 publications

Numerical Study of Indoor Oil Mist Particle Concentration Distribution in an Industrial Factory Using the Eulerian–Eulerian and Eulerian–Lagrangian Methods

Numerical Study of Indoor Oil Mist Particle Concentration Distribution in an Industrial Factory Using the Eulerian–Eulerian and Eulerian–Lagrangian Methods

Low-Cost Sensor Outlier Detection Framework For on-Line Monitoring of Particle&nbsp;Pollutants in Multiple Scenarios

Air pollution forecasting based on wireless communications: review

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Low-Cost Sensor Outlier Detection Framework For on-Line Monitoring of Particle Pollutants in Multiple Scenarios