ANN modeling in Pb(II) removal from water by clay‐polymer composites fabricated via the melt‐blending

Dlamini, Derrick S.; Mishra, Ajay Kumar; Mamba, Bhekie B.

doi:10.1002/app.39656

Cited by 13 publications

(3 citation statements)

References 28 publications

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“…The MLR and BPNN models were adopted for determining the association of optimal crop spectral variables (IRECI, CVI, NGRDI, REP, and TGI) and the SFI using 90 training samples. For the BPNN algorithm, referred to relevant literature [48][49][50] and through numerous experiments, the number of neuron nodes of the hidden layer was eventually set to 11, the number of iterations was set to 5000, and the learning rate and learning objective were set to 0.01. Figure 8 indicates that the BPNN model provides more accurate estimates than the MLR because the values in the scatter plot approach the 1:1 line.…”

Section: Model Construction and Accuracy Evaluationmentioning

confidence: 99%

A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network

et al. 2022

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Soil fertility affects crop yield and quality. A quick, accurate evaluation of soil fertility is crucial for agricultural production. Few satellite image-based evaluation studies have quantified soil fertility during the crop growth period. Therefore, this study proposes a new approach to the quantitative evaluation of soil fertility. Firstly, the optimal crop spectral variables were selected using the integration of an extreme gradient boosting (XGBoost) algorithm with variance inflation factor (VIF). Then, based on the optimal crop spectral variables where the red-edge indices were introduced for the first time, the estimation models were developed using the backpropagation neural network (BPNN) algorithm to assess soil fertility. The model was finally adopted to map the soil fertility using Sentinel-2 imagery. This study was performed in the Conghua District of Guangzhou, Guangdong Province, China. The results of our research are as follows: (1) five crop spectral variables (inverted red-edge chlorophyll index (IRECI), chlorophyll vegetation index (CVI), normalized green-red difference index (NGRDI), red-edge position (REP), and triangular greenness index (TGI)) were the optimal variables. (2) The BPNN model established with optimal variables provided reliable estimates of soil fertility, with the determination coefficient (R2) of 0.66 and a root mean square error (RMSE) of 0.17. A nonlinear relation was found between soil fertility and the optimal crop spectral variables. (3) The BPNN model provides the potential for soil fertility mapping using Sentinel-2 images, with an R2 of 0.62 and an RMSE of 0.09 for the measured and estimated results. This study suggests that the proposed method is suitable for the estimation of soil fertility in paddy fields.

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Section: Model Construction and Accuracy Evaluationmentioning

confidence: 99%

A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network

et al. 2022

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“…Each link, like synapses in the human brain, can transmit a signal to other neurons. 29 An artificial neuron receives a signal, evaluates it, and then transmits it to the neurons to which it is linked. 30,31 A nonlinear function of the sum of a neuron's inputs generates its output, and the signal at a connection is represented by a real number (weights).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network prediction of thermal and mechanical properties forBi₂O₃‐polybenzoxazinenanocomposites

Hassan

Derradji

Ali

et al. 2022

J of Applied Polymer Sci

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Polybenzoxazine (BA-a) based nanocomposites with varying amounts of Bi 2 O 3 particles reinforcement (10, 20, and 30 wt%) were produced. The structures of the Bi 2 O 3 before and after surface silane treatment, as well as the structures of the BA-a matrix and its nanocomposites, were all evaluated using Fourier transform infrared spectroscopy (FTIR). The thermal stability of the samples was evaluated by thermogravimetric analysis (TGA) and the resistance to bending was studied using the three-point bending test. Then, an artificial neural network approach was used to estimate the thermal degradation and flexural strengths of the polybenzoxazine matrix and its nanocomposites. The results of the TGA analysis showed that when Bi 2 O 3 content increased, the char yield (Y c ) of the nanocomposites also increased from 27.98% to 43.53% for the experimental data and from 27.95% to 43 0.57% for the prediction, with all SDs less than 0.02. Moreover, both the experimental and predicted values of the flexural strengths for the unfilled BA-a displayed 100.40 and 102.96 MPa, respectively and these values increased to 119.65 and 114.95 MPa when 10 wt % of fillers were incorporated into the matrix. However, for higher filler contents, 20 and 30 wt%, these values decreased to 112.94 and 108.01 MPa for the experiments, and 106.35 and 103.20 MPa for the prediction, respectively. The very small deviations (<0.05) between the experimental and predicted values demonstrated that the prediction approach using the artificial neural network is reliable and accurate.

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“…Application of different clay-polymer composites and nanocomposites for water treatment is well documented [2][3][4][5]. A large number of the studies report use of clay minerals like, montmorillonite, Bentonite etc., with different polymers for the syntheses of claypolymer nanocomposites for water treatment applications [4][5][6][7][8]. However, limited studies report the use of layered anionic clays for making clay-polymer composites for removing various toxic materials from water [9,10].…”

Section: Introductionmentioning

confidence: 99%

Layered Clay-Alginate Composites for the Adsorption of Anionic Dyes: A Biocompatible Solution for Water/Wastewater Treatment

Sebastian¹,

Mayadevi²,

Beevi³

et al. 2014

JWARP

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Adsorption properties of layered anionic clay-composites for the removal of anionic dyes from water were studied. The dye adsorption capacities of the composites were higher than the pristine clay. The clay-alginate composite with 5.9% alginate showed highest adsorption for both the dyes. The maximum adsorption capacity of the composite was enhanced by 51% for Acid Green 25 and 160% for Acid Green 27, compared to the pristine layered clay sample. The adsorption isotherm data were fitted to the Langmuir, Freundlich and Temkin isotherm models. The isotherm data could be explained well using the Freundlich isotherm model. Adsorption kinetics was analyzed using normal first order and Lagergren first order kinetic models.

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ANN modeling in Pb(II) removal from water by clay‐polymer composites fabricated via the melt‐blending

Cited by 13 publications

References 28 publications

A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network

A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network

Artificial neural network prediction of thermal and mechanical properties forBi₂O₃‐polybenzoxazinenanocomposites

Layered Clay-Alginate Composites for the Adsorption of Anionic Dyes: A Biocompatible Solution for Water/Wastewater Treatment

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ANN modeling in Pb(II) removal from water by clay‐polymer composites fabricated via the melt‐blending

Cited by 13 publications

References 28 publications

A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network

A New Method for Estimating Soil Fertility Using Extreme Gradient Boosting and a Backpropagation Neural Network

Artificial neural network prediction of thermal and mechanical properties forBi2O3‐polybenzoxazinenanocomposites

Layered Clay-Alginate Composites for the Adsorption of Anionic Dyes: A Biocompatible Solution for Water/Wastewater Treatment

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Product

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Artificial neural network prediction of thermal and mechanical properties forBi₂O₃‐polybenzoxazinenanocomposites