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Journal ArticleDOI

Artificial neural networks modeling for the prediction of Pb(II) adsorption

01 Sep 2019-International Journal of Environmental Science and Technology (Springer Berlin Heidelberg)-Vol. 16, Iss: 9, pp 5079-5086
TL;DR: In this article, an artificial neural network (ANN) model was used to predict the efficiency of Pb(II) adsorption on polyamine-polyurea polymer modified with pyromellitic dianhydride.
Abstract: The work presents an artificial neural network (ANN) model predicting the efficiency of Pb(II) adsorption on polyamine-polyurea polymer modified with pyromellitic dianhydride. Adsorption percentages of Pb(II) ions, as calculated using the results of batch experiments, are used as data inputs for the ANN model. In the developed model, the contact time (5–240 min.), pH (1–7), the initial Pb(II) concentration (50–300 mg/L), amount of adsorbent (20–75 mg) and temperature (25–55 °C) values constitute the input layer, while adsorption percentage values constitute the output layer. Simulation-based development of ANN models was carried out with eight values for neurons in the hidden layer (2, 3, 5, 10, 20, 30, 50 and 100). The best results were obtained with 10 neurons. The prediction data of ANN models were statistically compared to experimental data. With the developed model’s trial period and cost savings, the adsorption ratio was estimated with an error rate of about 2%. The results show that the multilayer perception ANN model (R2 = 0.9858) justified the prediction of adsorption percentage.
Citations
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Journal ArticleDOI
TL;DR: In this paper, the authors have applied artificial neural network (ANN) and multiple linear regression (MLR) techniques to predict the fitness of groundwater quality for drinking from Shivganga River basin, located on the eastern slopes of the Western Ghat region of India.
Abstract: The present study has applied artificial neural network (ANN) and multiple linear regression (MLR) techniques to predict the fitness of groundwater quality for drinking from Shivganga River basin, located on the eastern slopes of the Western Ghat region of India. In view of this, thirty-four (34) representative groundwater samples have been collected and analyzed for major cations and anions during pre- and post-monsoon seasons of 2015. The physicochemical parameters such as pH, EC, TDS, TH, Ca, Mg, Na, K, Cl, HCO3, SO4, NO3 and PO4 were considered for computing water quality index (WQI). Analytical results confirmed that all the parameters are within acceptable range; however, EC, TDS, TH, Ca and Mg are exceeding the desirable limit of the WHO drinking standards. The groundwater suitability for drinking was ascertained by WQI method. The WQI value ranges from 25.75 to 129.07 and from 37.54 to 91.38 in pre- and post-monsoon seasons, respectively. Only one sample (DW5) shows 129.07 WQI value indicating poor quality for drinking due to input of domestic and agricultural waste. In the view of generating consistent and precise model for prediction of WQI-based groundwater quality, a Levenberg–Marquardt three-layer back propagation algorithm was used in ANN architecture. Further, MLR model is used to check the efficiency of ANN prediction. The results corroborated that predictions of ANN model are satisfactory and confirms consistently acceptable performance for both the seasons. The proposed ANN model may be useful in similar studies of groundwater quality prediction for drinking purpose.

131 citations

Journal ArticleDOI
TL;DR: The development of computer aid models for heavy metals (HMs) simulation has been remarkably advanced over the past two decades as mentioned in this paper, and several machine learning (ML) models have been developed for modeling HMs with outstanding progress.

128 citations

Journal ArticleDOI
TL;DR: Artificial Neural Network (ANN) was used as a Machine Learning tool for prediction of PCM adsorption efficiency on chemically modified orange peel (CMOP) and the results obtained showed that ANN is efficient in predicting the adsorptive efficiency ofPCM on CMOP.

42 citations

Journal ArticleDOI
TL;DR: Adsorption of methylene blue dye in residual agricultural biomass was modelled using the machine learning algorithm Random Forest and compared with the traditional Artificial Neural Networks approach, finding that RF stood out due to its capacity to better capture data variation.
Abstract: In the present study, adsorption of methylene blue dye in residual agricultural biomass (orange bagasse) was modelled using o machine learning algorithm Random Forest (RF) and compared with the traditional Artificial Neural Networks (ANN) approach. The Machine Learning was performed using Python, a free and open source programming language. The models were built and validated with a combination of 202 independent experiments aimed at separately predicting the final concentration of methylene blue (Cf), adsorption capacity (Q) and adsorbate percentage removal (R%), having as input variables: Temperature, pH, adsorbent dosage, contact time, salinity, initial methylene blue concentration and rotation. The validation process of the models was carried out using the Coefficient of Determination (R2) and the Mean Squared Error (MSE). According to the obtained results, both RF and ANN models exhibited similar performances, as shown by their respective R2 values of 0.9739 and 0.9734 for Cf; 0.9932 and 0.9919, for Q; 0.9318 and 0.9257 for R%, as well as their respective MSE values of 0.0012 and 0.0016 for Cf; 0.0005 and 0.0007 for Q; 0.0015 and 0.0019 for R%. However, RF stood out due to its capacity to better capture data variation. Finally, it was possible to point out that both methods resulted in models able to satisfactorily predict all three response variables, thereby allowing less experimental effort.

23 citations

Journal ArticleDOI
TL;DR: In this paper , three feature selection algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia.

11 citations

References
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Journal ArticleDOI
TL;DR: It is shown that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to L p (μ) performance criteria, for arbitrary finite input environment measures μ.

5,593 citations


Additional excerpts

  • ...The mathematical theory of ANN states that every continuous function that maps intervals of actual numbers to some output interval of actual numbers can be randomly approximated nearly by a feed forward ANN with just one hidden layer (Hornik 1991)....

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Book ChapterDOI
01 Jan 1978

4,100 citations

Journal ArticleDOI
TL;DR: It is highlighted that both adults and children consuming food crops grown in wastewater-irrigated soils ingest significant amount of the metals studied, however, health risk index values of less than 1 indicate a relative absence of health risks associated with the ingestion of contaminated vegetables.

1,951 citations


"Artificial neural networks modeling..." refers background in this paper

  • ...Heavy metals tend to accumulate in the vital organs of living things (such as the brain and kidney system) and can severely damage the normal functioning of these organs, dangerously affecting health even in small amounts (Khan et al. 2008)....

    [...]

Journal ArticleDOI
TL;DR: This technique for syntheses of the crosslinked chitosans with epichlorohydrin via the homogeneous reaction in aqueous acetic acid solution showed that the adsorptions of three metal ions in aQueous solution were followed the monolayer coverage of the adsorbents through physical adsorption phenomena.

442 citations


Additional excerpts

  • ...The most common method is adsorption (Ozdes et al. 2011; Imamoglu et al. 2015; Zhao et al. 2013; Bereket et al. 1997; Kadirvelu et al. 2000; Rafatullah et al. 2009; Chen et al. 2008)....

    [...]

Journal ArticleDOI
29 Sep 2000-Langmuir
TL;DR: In this paper, the adsorption of three metal ions, Cu(II), Ni(II) and Pb (II), is performed by activated carbon cloths (ACC), and two adsorbents, CS 1501 and RS 1301, are studied.
Abstract: The adsorption of three metal ions, Cu(II), Ni(II), and Pb(II), is performed by activated carbon cloths (ACC). Two adsorbents, CS 1501 (with more than 96% of micropore volume) and RS 1301 (with 32% of mesopore volume), are studied. Batch experiments are carried out to assess kinetic and equilibrium parameters. They allow kinetic data, transfer coefficients, and maximum adsorption capacities to be computed. These parameters show the fast external film transfer of metal ions on fibers, because of their low diameter (10 μm). Intraparticular diffusion coefficients are lower than those obtained with a granular activated carbon, but maximum adsorption capacities agree with literature values for GAC. They show the dependency of adsorption on metal ion size and ACC porosity, the largest cation Pb(II) being more adsorbed by the mesoporous cloth. The pH effect is studied, and pH adsorption edges are determined. They are short, only 2 pH units, and located below the precipitation edges. A decrease of equilibrium pH ...

405 citations


Additional excerpts

  • ...The most common method is adsorption (Ozdes et al. 2011; Imamoglu et al. 2015; Zhao et al. 2013; Bereket et al. 1997; Kadirvelu et al. 2000; Rafatullah et al. 2009; Chen et al. 2008)....

    [...]