Effects of the gas outlet duct length and shape on the performance of cyclone separators

Souza, Francisco José de; Salvo, Ricardo de Vasconcelos; Martins, Diego de Moro

doi:10.1016/j.seppur.2014.12.008

Cited by 65 publications

(18 citation statements)

References 50 publications

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“…With the rapid development of computing speed and numerical analysis techniques, many studies were conducted to predict cyclone flow by solving RANS equations based on commercial computational fluid dynamic (CFD) codes. The CFD studies have investigated the effect on separation performance by independently setting several cyclone geometric design variables [4][5][6][7][8][9][10]. For example, the separation efficiency and pressure drop were evaluated according to the inlet shape by using CFD [6], and the cyclone performance investigated according to the relationship between the outlet shape and the shape of the dust container [8,9].…”

Section: Figure 1 Geometry Of the Cyclone Separatormentioning

confidence: 99%

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

Park¹,

Go²

2020

Preprint

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This paper deals with the characteristics of the cyclone separator from the Lagrangian perspective to design important dependent variables, develops a neural network model for predicting the separation performance parameter, and compares the predictive performance between the traditional surrogate model and the neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based CFD methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learning and multiple regression methods, Unsteady-RANS analyzes according to shape dimensions were performed. The input design variables for predicting the critical diameter were selected as four geometry parameters that affect the turbulent flow inside the cyclone. As a result of comparing the model prediction performances, the ML model showed the 32.5 % of improvement rate of R2 compared to the traditional MLR considering the nonlinear relationship between the cyclone design variable and the critical diameter. The proposed techniques have proven to be fast and practical tools for cyclone design.

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Section: Figure 1 Geometry Of the Cyclone Separatormentioning

confidence: 99%

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

Park¹,

Go²

2020

Preprint

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“…Therefore, many CFD studies have been performed for optimizing p and η depending on the geometrical parameters. The effects of pressure drop and collection efficiency on the changes of the cyclone vortex finder shape were investigated using CFD (De Souza, Salvo, & Martins, 2015;Raoufi, Shams, Farzaneh, & Ebrahimi, 2008). The CFD simulation of cyclone flow was performed according to changing the cyclone inlet shape (Bernardo, Mori, Peres, & Dionísio, 2006;Elsayed & Lacor, 2011;Yang, Sun, & Gao, 2013).…”

Section: Research Backgroundmentioning

confidence: 99%

Multi-objective optimization and comparison of surrogate models for separation performances of cyclone separator based on CFD, RSM, GMDH-neural network, back propagation-ANN and genetic algorithm

Park

Cha

Kim

et al. 2019

Engineering Applications of Computational Fluid Mechanics

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Pressure drop (p) and collection efficiency (η) are used to evaluate the separation performance of the cyclone separator. In this study, we conducted comparative study of cyclone models using response surface methodology (RSM), back propagation neural network (BPNN), and group method of data handling (GMDH) networks to develop optimal predictive cyclone models. Also, we conducted multi-objective optimization for maximizing model and minimizing model using genetic algorithm (GA). CFD was performed instead of experimental method to get the estimated values for modeling of p and η. The validation results of CFD showed 0.5% and 2% errors for p and η, respectively, compared with the experimental data. Second, design of experiment (DOE) analysis for 10 cyclone geometrical parameters was executed to obtain the significant geometrical parameters. Vortex finder diameter D x , inlet width a, inlet height b and cone height H co have a significant effect on η and p. However, interaction effects between the geometrical parameters have small effects. The cyclone models by RSM, BPNN and GMDH based on 25 CFD training set were developed. The predictive performance results by the cyclone models were compared by 25 CFD test set. The GMDH method achieved the best prediction for p (R 2 = 99.7, RMSE = 0.102) R 2 adjusted = 98.99, RMSE = 0.0119) than the RSM, BPNN cyclone models. Additionally, uncertainty analysis was performed to estimate the quantitative performance of cyclone models. The results show that the uncertainty width of GMDH models achieved the best prediction (η: ±0.0065, p: ±0.0188). Finally, GA was applied to optimize the GMDH models simultaneously. GA generated 70 non-dominant solutions. Reproducibility of five optimal points was validated by using CFD. The trade-off optimal point showed improvement by 24.31%, 18% and 8.79% for p d 50 and η, respectively.

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“…With the rapid development of computing speed and numerical analysis techniques, many studies were conducted to predict cyclone flow by solving Reynolds averaged Navier-Stokes equations based on commercial computational fluid dynamic (CFD) codes. The CFD studies have investigated the effect on separation performance by independently setting several cyclone geometric design variables [4][5][6][7][8][9][10]. For example, the separation efficiency and pressure drop were evaluated according to the inlet shape without changing other shapes by using CFD [6].…”

Section: Introductionmentioning

confidence: 99%

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

Park

2020

Processes

View full text Add to dashboard Cite

In this paper, the characteristics of the cyclone separator was analyzed from the Lagrangian perspective for designing the important dependent variables. The neural network network model was developed for predicting the separation performance parameter. Further, the predictive performances were compared between the traditional surrogate model and the developed neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based computational fluid dynamics (CFD) methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learning and multiple regression methods, unsteady-Reynolds averaged Navier-Stokes analyzes according to shape dimensions were performed. The input design variables for predicting the critical diameter were selected as four geometry parameters that affect the turbulent flow inside the cyclone. As a result of comparing the model prediction performances, the machine learning (ML) model, which takes into account the critical diameter and the nonlinear relationship of cyclone design variables, showed a 32.5% improvement in R-square compared to multi linear regression (MLR). The proposed techniques have proven to be fast and practical tools for cyclone design.

show abstract

Effects of the gas outlet duct length and shape on the performance of cyclone separators

Cited by 65 publications

References 50 publications

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

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

Multi-objective optimization and comparison of surrogate models for separation performances of cyclone separator based on CFD, RSM, GMDH-neural network, back propagation-ANN and genetic algorithm

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

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