Response surface methodology and support vector machine for the optimization of separation in linear gradient elution

2008 ◽  
Vol 31 (22) ◽  
pp. 3864-3870 ◽  
Author(s):  
Mohammad Reza Hadjmohammadi ◽  
Kamyar Kamel
Keyword(s):  
Support Vector Machine ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Gradient Elution ◽  
Support Vector ◽  
Linear Gradient ◽  
Optimization Of Separation

scholarly journals Modelling of cutting forces and surface roughness evolutions during straight turning of Stellite 6 material based on response surface methodology, artificial neural networks and support vector machine approaches

2021 ◽  
Vol 15 (4) ◽  
pp. 8540-8554
Author(s):  
Brahim Ben Fathallah ◽  
R. Saidi ◽  
S. Belhadi ◽  
M.A. Yallese ◽  
T. Mabrouki
Keyword(s):  
Support Vector Machine ◽  
Response Surface Methodology ◽  
Cutting Force ◽  
Response Surface ◽  
Feed Rate ◽  
Research Work ◽  
Operating Conditions ◽  
Support Vector ◽  
Stellite 6 ◽  
Artificial Neural

The present research work proposes an experimental investigation helping to comprehend fundamental impacts of operating conditions during the cutting of cobalt alloys (Stellite 6). Thus, an experimental design was adopted to allow to build predicted mathematical models for the outputs, which are the average peak-to-valley profile roughness (Rz) and the tangential cutting force (Ft). Artificial neural network (ANN), support vector machine (SVM) and response surface methodology (RSM) were exploited to model the pre-cited outputs according to operation parameters. As a result, it has been highlighted that both feed rate and cutting depth, considerably, affect tangential cutting force evolution. Moreover, results show that both the insert feed rate and nose radius, are higher. This means the average peak-to-valley profile roughness is higher. In order to put out the effect of operating parameters on cutting outputs, Analysis of variance (ANOVA) method has been employed. This has allowed the detection of significant cutting conditions affecting average peak-to-valley profile roughness and tangential cutting force. In fact, to highlight the performance of adopted mathematical approaches, a comparison between RSM, ANN, and SVM has been also established in this study.

Application of SVM to Reliability Analysis of Foundation Excavations System

2013 ◽  
Vol 859 ◽  
pp. 315-321 ◽  
Author(s):  
Jing Cao ◽  
Chang Ning Sun ◽  
Hai Ming Liu
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
Response Surface ◽  
Correlation Coefficient ◽  
Failure Modes ◽  
Pearson Correlation ◽  
Support Vector ◽  
Random Parameters ◽  
Pearson Correlation Analysis ◽  
Instance Analysis

The correlation of failure modes needs to be considered in the reliability analysis of foundation excavations system. Because it is difficult to calculate the correlation coefficient of failure modes, the computational efficiency of traditional method is low. In this paper, the response surface (RS) is established by using the uniform test and support vector machine (SVM). On this basis, in order to obtain the index of each failure mode, the random parameters generated by Monte Carlo simulation are predicted. Combined with the Pearson correlation analysis, the correlation coefficient of failure modes is obtained. And then, the Breadth Border Method, Narrow Bounds Method and PNET method are used to calculate system failure probability of foundation excavations. The reliability analysis method of the foundation excavations system based on the response surface of the support vector machine (RSSVM) is put forward. The instance analysis shows that the method is simple in calculation, and provides a convenient way for the system reliability theory of foundation excavations.

Reliability Analysis of Foundation Pit Based on Response Surface of Support Vector Machine

2013 ◽  
Vol 671-674 ◽  
pp. 240-244
Author(s):  
Chang Ning Sun ◽  
Jing Cao ◽  
Hai Ming Liu ◽  
Hui Min Zhao
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
Response Surface ◽  
Safety Factor ◽  
Lateral Displacement ◽  
Support Vector ◽  
Probability Density Distribution ◽  
Analysis Method ◽  
Foundation Pit ◽  
Random Samples

Traditional analysis methods of reliability in the foundation pit engineering have larger error and larger amount of calculation. Therefore, the response surface method has attracted much attention because it can effectively use the finite element analysis method (FEAM) and reduce the number of the numerical simulation. This paper combines uniform design (UD) with support vector machine (SVM). On this base, a reliability analysis method of the foundation pit is put forward based on the response surface of support vector machine (RSSVM). The UD structures random samples and the FEAM is used to obtain corresponding response parameters including the lateral displacement of wall, settlement of ground, safety factor of overall stability and safety factor of against overturning. Then, SVM trains the above random samples and corresponding response parameters to get response surface (RS) respectively. The probability density distribution of each response parameter is obtained by combining the Monte Carlo method with RSSVM. The instance analysis shows that the method has high computing efficiency and less amount of calculation, and the result is reasonable. It provides an effective way for the reliability analysis of the foundation pit engineering.

Optimization of separation phases of activated carbon by hydrocyclone process on using response surface methodology

2021 ◽  
Vol 233 ◽  
pp. 170-181
Author(s):  
Nizar Barrak ◽  
Wafa Miled ◽  
Rabeb Mannai ◽  
Mounir Zaag ◽  
Ahmed Noureddine Helal
Keyword(s):  
Response Surface Methodology ◽  
Activated Carbon ◽  
Response Surface ◽  
Optimization Of Separation

Nonlinear Dynamic Probabilistic Analysis for Turbine Casing Radial Deformation Using Extremum Response Surface Method Based on Support Vector Machine

2013 ◽  
Vol 8 (4) ◽  
Author(s):  
Chengwei Fei ◽  
Guangchen Bai
Keyword(s):  
Support Vector Machine ◽  
Response Surface ◽  
Response Surface Method ◽  
Probabilistic Analysis ◽  
Nonlinear Dynamic ◽  
Nonlinear Material ◽  
Support Vector ◽  
Radial Deformation ◽  
Surface Method ◽  
Turbine Casing

To improve the computational efficiency of nonlinear dynamic probabilistic analysis for aeroengine typical components, an extremum response surface method based on the support vector machine (SVM ERSM) was proposed in this paper. The basic principle was introduced and the mathematical model was established for the SVM ERSM. The probabilistic analysis of turbine casing radial deformation was taken as an example to validate the SVM ERSM considering the influences of nonlinear material property and dynamic heat loads. The results of probabilistic analysis imply that the distribution features of random parameters and the major factors are gained for more accurate the design of casing radial deformation. The SVM ERSM offers a feasible and valid method, which possesses high efficiency and high precision in the nonlinear dynamic probabilistic analysis. Moreover, the SVM ERSM is promising to provide an useful insight for casing dynamic optimal design and the blade-tip clearance control of aeroengine high pressure turbine.

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