Design of Cyclone Separator Critical Diameter Model Based on Machine Learning and CFD

Park, Dong-Geun; Go, Jeung Sang

doi:10.3390/pr8111521

Cited by 17 publications

(15 citation statements)

References 21 publications

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“…It is difficult to estimate the exposure to the user's fast needs. To address these compute cost problems, machine learning modeling method based on simulation data are used in the various industries [20][21][22]. Therefore, in future research, we will develop a machine learning model with spatial information as input variable and spatial concentration as output variable to predict concentration according to various spatial structure and characteristics.…”

Section: Resultsmentioning

confidence: 99%

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Feasibility Evaluation of CFD Approach for Inhalation Exposure Assessment: Case Study for Biocide Spray

Park¹,

Lee²

2021

Preprint

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Consumer products contain the chemical substances that threaten human health. The modeling methods and experimental methods have been used to estimate the inhalation exposure concentration by the consumer products. The model and measurement methods have the spatial property problem and time/cost consuming problem, respectively. For solving the problems due to the conventional methodology, this study performed the feasibility of applying CFD for evaluation of inhalation exposure by comparing the experiment results and the zero-dimensional results with CFD results. To calculate the aerosol concentration, the CFD was performed by combined the 3D Reynolds averaged Navier Stoke’s equation and discrete phased model using ANSYS FLUENT. As a result of comparing the three methodologies performed under the same simulation/experimental conditions, we found the zero-dimensional spray model shows approximately 5 times underestimated inhalation exposure concentration when compared with the CFD results and measurement results in near field. Also, the results of the measured concentration of aerosols at five locations and the CFD results at the same location were compared to show the possibility of evaluating inhalation exposure at various locations using CFD instead of experimental method. The CFD results according to measurement positions can predict rationally the measurement results with low error. In conclusion, in the field of exposure science, a guideline for exposure evaluation using CFD was found that complements the shortcomings of the conventional methodology, the zero-dimensional spray model and measurement method.

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Section: Resultsmentioning

confidence: 99%

“…The sprayed biocide aerosol has the micro-size diameter. The micro-size aerosol is dependent on the flow field [20]. In the indoor air flow, a complex three-dimensional turbulent flow is generated.…”

Section: Modeling the Flow Fieldmentioning

confidence: 99%

Feasibility Evaluation of CFD Approach for Inhalation Exposure Assessment: Case Study for Biocide Spray

Park¹,

Lee²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…To address these computational cost problems, machine learning modeling methods based on simulation data are used in various industries [29][30][31]. Therefore, in future research, we will develop a machine learning model with spatial information as an input variable and spatial concentration as an output variable to predict concentration according to various spatial structures and characteristics.…”

Section: Discussionmentioning

confidence: 99%

Feasibility Evaluation of Computational Fluid Dynamics Approach for Inhalation Exposure Assessment: Case Study for Biocide Spray

Park

Lee

2021

Applied Sciences

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Consumer products contain chemical substances that threaten human health. The zero-dimensional modeling methods and experimental methods have been used to estimate the inhalation exposure concentration of consumer products. The model and measurement methods have a spatial property problem and time/cost-consuming problem, respectively. For solving the problems due to the conventional methodology, this study investigated the feasibility of applying computational fluid dynamics (CFD) for the evaluation of inhalation exposure by comparing the experiment results and the zero-dimensional results with CFD results. To calculate the aerosol concentration, the CFD was performed by combined the 3D Reynolds averaged Navier–Stokes equations and a discrete phased model using ANSYS FLUENT. As a result of comparing the three methodologies performed under the same simulation/experimental conditions, we found that the zero-dimensional spray model shows an approximately five times underestimated inhalation exposure concentration when compared with the CFD results and measurement results in near field. Additionally, the results of the measured concentration of aerosols at five locations and the CFD results at the same location were compared to show the possibility of evaluating inhalation exposure at various locations using CFD instead of the experimental method. The CFD results according to measurement positions can rationally predict the measurement results with low error. In conclusion, in the field of exposure science, a guideline for exposure evaluation using CFD, was found that complements the shortcomings of the conventional methodology, the zero-dimensional spray model and measurement method.

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“…The results feed the DNN to get the complex non-linearity between input and target. Since the theory for the timedependent of LSTM [15,16] and the process of calculation of complex nonlinearity between input and output of DNN [17,18] have been well described in many studies, in this study, the description of theory is omitted in order to avoid unnecessary repetition.…”

Section: Machine Learning Algorithmmentioning

confidence: 99%

Assessment and Calibration on Low-cost PM2.5 Sensor using Machin Learning (Hybrid-LSTM Neural Network): Feasibility Study to Build Air Quality Monitoring System

Park¹,

Yoo²,

Park³

et al. 2021

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Although commercially-available low-cost air quality sensors have low accuracy, the sensor system are being used to collect the data for the regulation of PM2.5 emission caused by industrial activities or to estimate the personal exposure for PM2.5. In this work, to solve the accuracy problem of low-cost PM sensor, we developed a new PM2.5 calibration model by combining the deep neural network (DNN) optimized in calibration problem and a LSTM optimized in time-dependent characteristics. First, two datasets were generated to test the accuracy performance and generalization performance of the PM2.5 calibration machine learning (ML) model. The PM2.5 concentrations, temperature and humidity by low-cost sensor and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmark (multiple linear regression model) and low-cost sensor results. For root mean square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41-60% of error compared to the raw data of low-cost sensor, and reduced 30-51% of error compared to the benchmark model. R2 of ML model, MLR and raw data were 93, 80 and 59 %. Also, the developed model still showed consistent calibration performance when calibrated with new sensors in different locations. Low-cost sensors combined with ML model not only can improve the calibration performance of benchmark, but also can be applied to the sensor monitoring systems for various epidemiologic investigations and regulatory decisions.

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Design of Cyclone Separator Critical Diameter Model Based on Machine Learning and CFD

Cited by 17 publications

References 21 publications

Feasibility Evaluation of CFD Approach for Inhalation Exposure Assessment: Case Study for Biocide Spray

Feasibility Evaluation of CFD Approach for Inhalation Exposure Assessment: Case Study for Biocide Spray

Feasibility Evaluation of Computational Fluid Dynamics Approach for Inhalation Exposure Assessment: Case Study for Biocide Spray

Assessment and Calibration on Low-cost PM2.5 Sensor using Machin Learning (Hybrid-LSTM Neural Network): Feasibility Study to Build Air Quality Monitoring System

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