Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study

Ghaedi, Mehrorang; Ghaedi, A.M.; Hossainpour, M.; Ansari, Amin; Habibi, Mohammad Hossein; Asghari, Alireza

doi:10.1016/j.jiec.2013.08.011

Cited by 142 publications

(48 citation statements)

References 46 publications

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“…The reason for choosing these models for comparison was that the current models used for constructing relationships in sorption studies are mainly based on these methods. The LSSVM-related model indicated a higher predictive capability than the linear method for the sorption of methylene blue [44]. RF proved to be more powerful than multiple linear regression to predict the sorption of bromophenol blue using activated carbon sorbents [45].…”

Section: Sca Treesmentioning

confidence: 92%

A Stepwise-Cluster Inference Model for Phenanthrene Immobilization at the Aqueous/Modified Palygorskite Interface

Zhao

Huang

Cheng

et al. 2017

Water

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A stepwise-cluster inference (SI) model was established through introducing stepwise-cluster analysis (SCA) into the phenanthrene immobilization process at the aqueous/modified palygorskite interface. SCA has the advantages of tackling the nonlinear relationships among environmental factors and the phenanthrene sorption amount in the immobilization process. The essence of SCA is to form a tree-based classification on a series of cutting or mergence procedures under given statistical criteria. The results indicated that SI could help develop a statistical relationship between environmental variables and the phenanthrene sorption amount, where discrete and nonlinear complexities exist. During the experiment, data were randomly sampled 10 times for model calibration and verification. The R 2 (close to one) and root mean squared error (RMSE) (close to zero) values guaranteed the prediction accuracy of the model. Compared to other statistical methods, the calculation of R 2 and RMSEs showed that SI was more straightforward for describing the nonlinear relationships and precisely fitting and predicting the immobilization of phenanthrene. Through the calculation of the input effects on the output in the SI model, the influence of environmental factors on phenanthrene immobilization were ranged in descending order as: initial phenanthrene concentration, ionic strength, pH, added humic acid dose, and temperature. It is revealed that SCA can be used to map the nonlinear and discrete relationships and elucidate the transport patterns of phenanthrene at the aqueous/modified palygorskite interface.

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Section: Sca Treesmentioning

confidence: 92%

A Stepwise-Cluster Inference Model for Phenanthrene Immobilization at the Aqueous/Modified Palygorskite Interface

Zhao

Huang

Cheng

et al. 2017

Water

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“…This results is explained by the fact that following adsorption of first layer of charged dyes on adsorbent and formation of semi-capacitor, hinder form more layer adsorption via repulsive force among layer [43] The effect of isotherm shape has been discussed with a view to predict whether an adsorption system is favorable or unfavorable which in Langmuir isotherm represent by R L value to separation factor or equilibrium parameter. [44] The i.e., whether the process is unfavorable (R L > 1), or linear (R L = 1), or favorable (0 < R L < 1), or irreversible (R L = 0) and as results show R L values were in the range of 0 < R L < 1 (Table 4) which support high efficiency of this model for [48] Activated Carbon (walnut shells) MB 315.0 1440 [49] CuO-Nanoparticles Loaded on Activated Carbon MB 10.55 15 [50] PDA microspheres MB 90.70 100 [51] MWCNTs filled with Fe 2 O 3 particles MB 42.90 60 [52] Ag-Nanoparticles Loaded on Activated Carbon MB 71.43 15 [21] Humic Acid-coated Fe 3 O 4 -NP (HA-Fe 3 O 4 ) MB 93.08 27 [53] Activated Carbon of Thespesia Populnea Pods OG 9.120 180 [54] Bagasse Fly Ash OG 18.79 240 [10] explanation of data in single (MB and OG) and binary system (MB-OG) on the SnO 2 /(NH 4 ) 2 -SnCl 6 -NCs-AC. Freundlich parameters (K F and n) indicate whether the nature of sorption is either favorable or unfavorable and accordingly 1 < n < 10 imply favorable sorption and its value closer to 1 suggest more uniform adsorbent.…”

Section: Adsorption Isotherm Modelingmentioning

confidence: 66%

Synthesis and characterization of SnO₂/(NH₄)₂‐SnCl₆ nanocomposites loaded on activated carbon and its application for adsorption of methylene Blue and Orange G

Jalali

Rahimi

Ghaedi

et al. 2017

Applied Organom Chemis

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The present study deals with the synthesis and characterization (FE-SEM, particle size distribution, XRD and point of zero charge) SnO 2 /(NH 4 ) 2 -SnCl 6 nanocomposites loaded on activated carbon (SnO 2 /(NH 4 ) 2 -SnCl 6 -NCs-AC) and its subsequent application for the simultaneous removal of Methylene Blue (MB) and Orange G (OG) from aqueous solution. Response surface methodology (RSM) based on central composite design (CCD) give trend of influencing responses with respect to five parameters such as contact time (X 1 ), OG concentration (X 2 ), MB concentration (X 3 ), adsorbent mass (X 4 ) and pH (X 5 ). In later stage following recognition of significant variables and interaction, quadratic model generated which are able to predict the dyes removal in different conditions. Justification and selection of significant terms was conducted based on analysis of variance and Fisher's F-test Optimal value of contact time, OG concentration, MB concentration, adsorbent mass and pH were set at 4.0 min, 10 mg l −1 , 20 mg L −1 , 0.015 g and 6.0, respectively, which lead to achievement of best experiment removal percentage of 97.0 and 99.5% OG and MB respectively, from their binary solutions. The whole experimental data follow pseudo-first-order and pseudo-second-order rate equations. The fitting experimental data to more available conventional model like Langmuir, Freundlich, Temkin and Dubinin-Radushkevich isotherm models revel more ability of Langmuir model (with R 2 > 0.997) for explanation of system in equilibrium. The adsorption efficiency remained high even after the five cycle of reuse (99.76% and 95.56% for MB and OG, respectively).KEYWORDS adsorption isotherm and kinetic, experimental design, methylene blue, orange G, SnO 2 /(NH 4 ) 2 -SnCl 6 -NCs-AC, ultrasound

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“…SVM solves the pattern recognition problem through nonlinear mapping the samples into a high-dimensional (even infinite dimensional) feature space, which usually achieves good classification accuracy. Furthermore, the Least Square Support Vector Machine (LS-SVM) [17] solves a set of linear equations instead of a convex quadratic programming (QP) problem in the classical SVMs, which can reduce the complexity of the problem. Hence, a LS-SVM classifier is adopted in this work.…”

Section: Classifier Constructionmentioning

confidence: 99%

Toward Automated Quality Classification via Statistical Modeling of Grain Images for Rice Processing Monitoring

Liu¹,

Tang²,

Chen³

et al. 2016

IJCIS

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Computer vision-based rice quality inspection has recently attracted increasing interest in both academic and industrial communities because it is a low-cost tool for fast, non-contact, nondestructive, accurate and objective process monitoring. However, current computer-vision system is far from effective in intelligent perception of complex grainy images, comprised of a large number of local homogeneous particles or fragmentations without obvious foreground and background. We introduce a well known statistical modeling theory of size distribution in comminution processes, sequential fragmentation theory, for the visual analysis of the spatial structure of the complex grainy images. A kind of omnidirectional multi-scale Gaussian derivative filter-based image statistical modeling method is presented to attain omnidirectional structural features of grain images under different observation scales. A modified LS-SVM classifier is subsequently established to automatically identify the processing rice quality. Extensive confirmative and comparative tests indicate the effectiveness and outperformance of the proposed method.

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Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study

Cited by 142 publications

References 46 publications

A Stepwise-Cluster Inference Model for Phenanthrene Immobilization at the Aqueous/Modified Palygorskite Interface

A Stepwise-Cluster Inference Model for Phenanthrene Immobilization at the Aqueous/Modified Palygorskite Interface

Synthesis and characterization of SnO₂/(NH₄)₂‐SnCl₆ nanocomposites loaded on activated carbon and its application for adsorption of methylene Blue and Orange G

Toward Automated Quality Classification via Statistical Modeling of Grain Images for Rice Processing Monitoring

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Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon: Kinetic and isotherm study

Cited by 142 publications

References 46 publications

A Stepwise-Cluster Inference Model for Phenanthrene Immobilization at the Aqueous/Modified Palygorskite Interface

A Stepwise-Cluster Inference Model for Phenanthrene Immobilization at the Aqueous/Modified Palygorskite Interface

Synthesis and characterization of SnO2/(NH4)2‐SnCl6 nanocomposites loaded on activated carbon and its application for adsorption of methylene Blue and Orange G

Toward Automated Quality Classification via Statistical Modeling of Grain Images for Rice Processing Monitoring

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Synthesis and characterization of SnO₂/(NH₄)₂‐SnCl₆ nanocomposites loaded on activated carbon and its application for adsorption of methylene Blue and Orange G