scholarly journals Integration of Response Surface Methodology (RSM) and Principal Component Analysis (PCA) as an Optimization Tool for Polymer Inclusion Membrane Based-Optodes Designed for Hg(II), Cd(II), and Pb(II)

Membranes ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 288
Author(s):  
Jeniffer García-Beleño ◽  
Eduardo Rodríguez de San Miguel
Keyword(s):  
Principal Component Analysis ◽  
Metal Ion ◽  
Color Change ◽  
Principal Component ◽  
Optimization Method ◽  
Component Analysis ◽  
Full Spectrum ◽  
Inclusion Membrane ◽  
Polymer Inclusion Membrane ◽  
Desirability Functions

An optimization of the composition of polymer inclusion membrane (PIM)-based optodes, and their exposure times to metal ion solutions (Hg(II), Cd(II), and Pb(II)) was performed using two different chromophores, diphenylthiocarbazone (dithizone) and 1-(2-pyridylazo)-2-naphthol (PAN). Four factors were evaluated (chromophore (0.06–1 mg), cellulose triacetate (25–100 mg) and plasticizer amounts (25–100 mg), and exposure time (20–80 min)). Derringer’s desirability functions values were employed as response variables to perform the optimization obtained from the results of three different processes of spectral data treatment: two full-spectrum methods (M1 and M3) and one band-based method (M2). The three different methods were compared using a heatmap of the coefficients and dendrograms of the Principal Component Analysis (PCA)reductions of their desirability functions. The final recommended M3 processing method, i.e., using the scores values of the first two principal components in PCA after subtraction of the normalized spectra of the membranes before and after complexation, gave more discernable differences between the PIMs in the Design of Experiments (DoE), as the nodes among samples appeared at longer distances and varyingly distributed in the dendrogram analysis. The optimal values were time of 35–65 min, 0.53 mg–1.0 mg of chromophores, plasticizers 34.4–71.9 of chromophores, and 62.5–100 mg of CTA, depending on the metal ion. In addition, the method yielded the best outcomes in terms of interpretability and an easily discernable color change so that it is recommended as a novel optimization method for this kind of PIM optode.

Robust sparse principal component analysis by DC programming algorithm

2020 ◽  
Vol 39 (3) ◽  
pp. 3183-3193
Author(s):  
Jieya Li ◽  
Liming Yang
Keyword(s):  
Principal Component Analysis ◽  
Continuous Optimization ◽  
Principal Component ◽  
Optimization Method ◽  
Component Analysis ◽  
Programming Algorithm ◽  
Data Sets ◽  
Sparse Principal Component Analysis ◽  
Data Types ◽  
L2 Norm

The classical principal component analysis (PCA) is not sparse enough since it is based on the L2-norm that is also prone to be adversely affected by the presence of outliers and noises. In order to address the problem, a sparse robust PCA framework is proposed based on the min of zero-norm regularization and the max of Lp-norm (0 < p ≤ 2) PCA. Furthermore, we developed a continuous optimization method, DC (difference of convex functions) programming algorithm (DCA), to solve the proposed problem. The resulting algorithm (called DC-LpZSPCA) is convergent linearly. In addition, when choosing different p values, the model can keep robust and is applicable to different data types. Numerical simulations are simulated in artificial data sets and Yale face data sets. Experiment results show that the proposed method can maintain good sparsity and anti-outlier ability.

Research on Operation Optimization of Industrial Boiler by Principal Component Analysis

2014 ◽  
Vol 672-674 ◽  
pp. 1501-1505
Author(s):  
Wen Biao Wang ◽  
Lan Chen ◽  
Xu Dong Wang ◽  
Ji Bin Pei
Keyword(s):  
Principal Component Analysis ◽  
Thermal Efficiency ◽  
Control Method ◽  
Principal Component ◽  
Optimization Method ◽  
Component Analysis ◽  
Efficient Operation ◽  
Operation Optimization ◽  
High Efficient ◽  
Industrial Boilers

Abstract. Thermal efficiency is an economical operation index of industrial boilers. There are many factors influencing thermal efficiency. It is difficult to keep the boiler in high efficient operation just using single automatic control method when environment has been changed. Therefore, the control of combustion systems is usually depended on artificial experience. To improve this situation, an operation optimization method is proposed. An identification model which can reflect the thermal efficiency is established by using principal component analysis based on historical data. When the boiler’s operation efficiency decreases, the parameters of influencing boiler efficiency can be directly got by contribution plot method, which can guide operators in real-time to adjust these parameters maintaining boiler efficient operation. Abstract. Thermal efficiency is an economical operation index of industrial boilers. There are many factors influencing thermal efficiency. It is difficult to keep the boiler in high efficient operation just using single automatic control method when environment has been changed. Therefore, the control of combustion systems is usually depended on artificial experience. To improve this situation, an operation optimization method is proposed. An identification model which can reflect the thermal efficiency is established by using principal component analysis based on historical data. When the boiler’s operation efficiency decreases, the parameters of influencing boiler efficiency can be directly got by contribution plot method, which can guide operators in real-time to adjust these parameters maintaining boiler efficient operation.

Design Optimization of Cutting Parameters in Turning 45Cr Steel Using Cermets Tool

2010 ◽  
Vol 443 ◽  
pp. 238-243 ◽  
Author(s):  
Zhi Lin Han ◽  
Bin Lin ◽  
Bao Xing Zhang ◽  
Lei Zhang
Keyword(s):  
Principal Component Analysis ◽  
Optimal Parameter ◽  
Principal Component ◽  
Cutting Parameters ◽  
Optimization Method ◽  
Component Analysis ◽  
Process Efficiency ◽  
Machining Parameters ◽  
Optimization Of Cutting Parameters

In this paper, the optimization of cutting parameters in turning thin-walled 45Cr steel workpieces with cermets tool is researched. A new integrated optimization method is proposed, in which the parameter design of the Taguchi method, principal component analysis method and response surface method (RSM) are applied to obtain the optimal parameter for a hard turning process using mixed cermets tools. The orthogonal array experiment is conducted to economically obtain the response measurements. The Principal Component Analysis (PCA) is applied to transform the original evaluation variables into new, uncorrelated comprehensive variables, which includes most information of original variables, then the output response can be calculated by the principal components. At last, the RSM is used to build the relationship between the input parameters and output responses, and obtain the desired machining parameters. The quality of workpieces and the process efficiency are improved obviously.

scholarly journals A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Yunfeng Dong ◽  
Xiaona Wei ◽  
Lu Tian ◽  
Fengrui Liu ◽  
Guangde Xu
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Components ◽  
Principal Component ◽  
Optimization Method ◽  
Component Analysis ◽  
Earth Observation ◽  
Weighted Sum ◽  
Orbital Elements ◽  
Double Cluster

The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM), which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM) is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM). In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.

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