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

Abstract

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.

Investigating a Serious Challenge in the Sustainable Development Process: Analysis of Confirmed cases of COVID-19 (New Type of Coronavirus) Through a Binary Classification Using Artificial Intelligence and Regression Analysis

“…As mentioned before, to deal with unpredicted and uncertain problems, the GMDH algorithm can be applied as a powerful tool. Hence, a binary classification analysis was done by the GMDH algorithm in the present study [67][68][69].…”

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

“…In order to solve the local optimization problem and computing cost problem, the method combining CFD and surrogate modeling has been applied for the relationship between cyclone shapes and separation performance [11][12][13][14][15][16]. For example, the developed surrogate models such as ANN, RSM and GMDH algorithm showed reasonable predictive performance, and optimum design was performed by applying optimization algorithms such as genetic algorithms to the surrogate model [13]. However, the most optimization studies omitted the analysis of the cause of the optimal separation performance.…”

“…However, the most optimization studies omitted the analysis of the cause of the optimal separation performance. For analyzing the optimization results, the tangential velocity distribution of the shape before and after optimization was compared based on the particle separation theory from Euler's point of view [13,14]. However, since the force acting on the particles differs according to the rotational trajectory, it is more appropriate to analyze it according to the trajectory position rather than to analyze it from the Euler perspective.…”

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

“…In order to solve the local optimization problem and computing cost problem, the method combining CFD and surrogate modeling has been applied for the relationship between cyclone shapes and separation performance [11][12][13][14][15][16]. For example, the surrogate models such as artificial neural network (ANN), response surface methodology (RSM) and group method of data handling (GMDH) algorithm showed the reasonable predictive performance, and optimum design was performed by applying optimization algorithms such as genetic algorithms (GA) [13]. However, the most optimization studies omitted the analysis of the cause of the optimal separation performance.…”

“…However, the most optimization studies omitted the analysis of the cause of the optimal separation performance. For analyzing the optimization results, the tangential velocity contour, and the velocity distribution before and after optimization was compared based on the particle separation theory from Euler's point of view [13,14]. However, since the force acting on the particles differs according to the rotational trajectory, it is more appropriate to analyze it according to the trajectory position rather than to analyze it from the Euler perspective.…”