Optimizing Low-Concentration Mercury Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Fe3O4 Composites with the Aid of an Artificial Neural Network and Genetic Algorithm

Cao, Rensheng; Fan, Mingyi; Hu, Jiwei; Ruan, Wenqian; Xiong, Kangning; Wei, Xionghui

doi:10.3390/ma10111279

Cited by 30 publications

(12 citation statements)

References 66 publications

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“…The acceptable range of the discrepancy between the adjusted R 2 value and the predicted R 2 value should be less than 0.20; if the numerical difference is above 2, the model is regarded as not being appropriate to fit the data [56]. In addition, the appropriate value of adequate precision should be higher than 4 [28]. In our work, the adjusted R 2 , predicted R 2 , and adequate precision were 0.9789, 0.9331, and 34.747 respectively.…”

Section: Optimal Adsorption and Interactivementioning

confidence: 99%

“…RSM can quantitatively evaluate interactive effects of multiple variables and effectively reduce the number of experimental variables. RSM has been applied to various wastewater treatments, such as the Fenton process, electrocoagulation, photocatalysis, membranes, and adsorption operations for optimization [24][25][26][27][28]. In addition, regarding recycled agricultural materials, rice bran, also known as miller's bran, is a common agricultural byproduct in Taiwan and can be obtained during the milling of rice.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation and Optimization of Dyes Removal using Rice Bran-Based Magnetic Composite Adsorbent

Ma¹,

Hong

Wang

2020

Materials

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Although several studies have explored green adsorbent synthesized from many types of agriculture waste, this study represents the first attempt to prepare an environmentally friendly rice bran/SnO2/Fe3O4-based absorbent with economic viability and material reusability, for the promotion of sustainable development. Here, rice bran/SnO2/Fe3O4 composites were successfully synthesized and applied for adsorption of reactive blue 4 (RB4) and crystal violet (CV) dyes in aqueous solutions. The adsorption data were well-fitted by the Langmuir isotherm model and the pseudo-second-order kinetic model. The maximum adsorption capacities of the RB4 and CV dyes as indicated by the Langmuir isotherm model were 218.82 and 159.24 mg/g, respectively. As results of response surface methodology (RSM) showed, the quadratic model was appropriate to predict the performance of RB4 dye removal. The findings exhibited that an optimum removal rate of 98% was achieved at 60 °C for pH 2.93 and adsorption time of 360 min. Comparative evaluation of different agricultural wastes indicated that the rice bran/SnO2/Fe3O4 composite appeared to be a highly promising material in terms of regeneration and reusability, and showed that the composite is a potential adsorbent for dye removal from aqueous solutions. Overall, the study results clearly suggest that an adsorbent synthesized from rice bran/SnO2/Fe3O4 magnetic particle composites provides encouraging adsorption capacity for practical applications for environmental prevention.

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Section: Optimal Adsorption and Interactivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Evaluation and Optimization of Dyes Removal using Rice Bran-Based Magnetic Composite Adsorbent

Ma¹,

Hong

Wang

2020

Materials

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“…Therefore, artificial neural network (ANN) modeling appears as an alternative technique for optimization and control of MEUF process [12]. ANN networks are capable to store and process the information with distributed memory without empirical studies of the process and after learning they can make decision by commenting on similar events as literature studies showed their applications in various environmental engineering systems [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Experimental and neural network modeling of micellar enhanced ultrafiltration for arsenic removal from aqueous solution

Yaqub

Lee

2020

Environmental Engineering Research

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The optimization of micellar-enhanced ultrafiltration (MEUF) of arsenic (As) contaminated aqueous solution using cetylpyridinium chloride (CPC) as surfactant was studied through experimental and artificial neural network (ANN) modeling. Experimental studies were carried out by varying operational conditions such as time, pressure, molar ratio of CPC to As, concentration of As and pH of feed solution. Root mean square error (RMSE) and coefficient of determination (R<sup>2</sup>) were considered as performance criterion to evaluate the predicted results of ANN model. The experimental studies provided optimum operating parameters such as pressure 1.8 bar, molar ratio of CPC to As was 5:1, As concentration 1 mM and pH 8.0 of feed solution. ANN model presented reliable results with RMSE values 0.259, 0.553 and 0.623 for training, validation and testing datasets, respectively, while R<sup>2</sup> values for training, validation and testing dataset were noted as 0.962, 0.942 and 0.932, respectively. The proposed ANN model traced input-output relationship to predict As removal efficiency (RE) of MEUF process. Therefore, ANN model can be considered as a competitive, powerful and fast alternate because of its high computational speed, accuracy and economics in MEUF process optimization without doing laborious experimental work.

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“…In addition, Cao et al. 23 used ANN and GA to optimize low-concentration mercury removal from aqueous solutions by reduced graphene oxide-supported Fe 3 O 4 composites.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network and genetic algorithm to predict and optimize load and torque in T-section profile ring rolling

Parvizi

Raftar

2019

Proceedings of the Institution of Mechanical Engineers, Part C:

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Artificial neural network is implemented to predict the required load and torque in T-section profile ring rolling process for the first time in this study. Moreover, an optimal condition of T-section profile ring rolling process for specific limit of input factor is acquired using genetic algorithm technique. Various three-dimensional finite element simulations are carried out for different collections of process variables to obtain initial data for training and validation of the neural network. Besides, the finite element model is verified via comparison with the experimental results of the other investigators. The back-propagation algorithm is utilized to develop Levenberg–Marquardt feed-forward network and the optimum architecture is achieved by estimating the performance considering different number of hidden layers and neurons. It is concluded that results of artificial neural network predictions have an appropriate conformity with those ones from simulation and experiments. Moreover, a reasonable accuracy is obtained from the implemented model by which the prediction of ring rolling load and torque in different conditions can be achieved.

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Optimizing Low-Concentration Mercury Removal from Aqueous Solutions by Reduced Graphene Oxide-Supported Fe3O4 Composites with the Aid of an Artificial Neural Network and Genetic Algorithm

Cited by 30 publications

References 66 publications

Performance Evaluation and Optimization of Dyes Removal using Rice Bran-Based Magnetic Composite Adsorbent

Performance Evaluation and Optimization of Dyes Removal using Rice Bran-Based Magnetic Composite Adsorbent

Experimental and neural network modeling of micellar enhanced ultrafiltration for arsenic removal from aqueous solution

Application of artificial neural network and genetic algorithm to predict and optimize load and torque in T-section profile ring rolling

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