Artificial Neural Network (ANN) for Modelling Adsorption of Lead (Pb (II)) from Aqueous Solution

Khan, Taimur; Mustafa, Muhammad Raza Ul; Isa, Mohamed Hasnain; Manan, Teh Sabariah Binti Abd; Ho, Yeek‐Chia; Lim, Jun-Wei; Yusof, Nur Zulaikha

doi:10.1007/s11270-017-3613-0

Cited by 57 publications

(17 citation statements)

References 47 publications

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“…Isotherm models such as the Langmuir Equation (5), Freundlich Equation (6), and Sips (combined Langmuir–Freundlich expression) Equation (7) models show the degree of adsorbate accumulation on any adsorbent surface at constant temperature [ 45 , 46 ]. The Langmuir, Freundlich, and Sips [ 47 ] adsorption isotherms are expressed in the following equations:

where Q o is the sum of the adsorbed solute on the surface of the adsorbent that forms a monolayer (monolayer adsorption capacity) and b is the energy of adsorption.…”

Section: Resultsmentioning

confidence: 99%

Modeling of Cu(II) Adsorption from an Aqueous Solution Using an Artificial Neural Network (ANN)

et al. 2020

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This research optimized the adsorption performance of rice husk char (RHC4) for copper (Cu(II)) from an aqueous solution. Various physicochemical analyses such as Fourier transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FESEM), carbon, hydrogen, nitrogen, and sulfur (CHNS) analysis, Brunauer–Emmett–Teller (BET) surface area analysis, bulk density (g/mL), ash content (%), pH, and pHZPC were performed to determine the characteristics of RHC4. The effects of operating variables such as the influences of aqueous pH, contact time, Cu(II) concentration, and doses of RHC4 on adsorption were studied. The maximum adsorption was achieved at 120 min of contact time, pH 6, and at 8 g/L of RHC4 dose. The prediction of percentage Cu(II) adsorption was investigated via an artificial neural network (ANN). The Fletcher–Reeves conjugate gradient backpropagation (BP) algorithm was the best fit among all of the tested algorithms (mean squared error (MSE) of 3.84 and R2 of 0.989). The pseudo-second-order kinetic model fitted well with the experimental data, thus indicating chemical adsorption. The intraparticle analysis showed that the adsorption process proceeded by boundary layer adsorption initially and by intraparticle diffusion at the later stage. The Langmuir and Freundlich isotherm models interpreted well the adsorption capacity and intensity. The thermodynamic parameters indicated that the adsorption of Cu(II) by RHC4 was spontaneous. The RHC4 adsorption capacity is comparable to other agricultural material-based adsorbents, making RHC4 competent for Cu(II) removal from wastewater.

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where Q o is the sum of the adsorbed solute on the surface of the adsorbent that forms a monolayer (monolayer adsorption capacity) and b is the energy of adsorption.…”

Section: Resultsmentioning

confidence: 99%

Modeling of Cu(II) Adsorption from an Aqueous Solution Using an Artificial Neural Network (ANN)

et al. 2020

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“…The ranges of process variables and code factors are shown in Table S1. The values of the coded factors (i.e., low (−1) and high (+1) coded factors) in Table 2 were calculated based on Equation (5). The range is the difference between highest and lowest values (Equation ( 6)).…”

Section: Model Equation For Ecological Risk Indicatorsmentioning

confidence: 99%

“…However, concrete mixes containing coal ash as a binder may pose risk of leaching heavy metals to the soil leading to adverse environmental and health impacts. The biohazards of heavy metals are due to their bioproperties, such as high toxicity with possibilities of acute and chronic toxicity effects [3,[5][6][7]. Heavy metals are non-biodegradable metal ions that bioaccumulate in living organisms [8,9].…”

Section: Introductionmentioning

confidence: 99%

Ecological Risk Indicators for Leached Heavy Metals from Coal Ash Generated at a Malaysian Power Plant

et al. 2021

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The application of coal ash (CA) in construction industries has grown rapidly, posing risk to the environment due to heavy metals leaching from the material. This research presents a simulation of ecological risk assessment and model risk indicators (ERI) of leached heavy metals (lead (Pb), copper (Cu), zinc (Zn) and arsenic (As)) from CA (FA: fly ash and BA: bottom ash) via response surface methodology (RSM). The ERI values were based on quantified leached heavy metals from the toxicity characteristic leaching procedure (TCLP-1311) and synthetic precipitation leaching procedure (SPLP-1312). The ecological risk index (RI ) values for TCLP were 10.27 × 100 (FA), 9.91 × 100 (BA) and 12.58 × 100 (FA + BA); whereas RI for SPLP were 10.34 × 100 (FA), 9.90 × 100 (BA) and 12.61 × 100 (FA + BA). Twenty-nine combinations of operations were evaluated based on Box-Behnken design with ERI as the response variable. The established model risk indicator (i.e., coded and actual factors) of Pb, Cu, Zn and ‘As’ showed significant model terms that describe their relationship very well, perfectly fit to the corresponding ERI (sum of squares = 0.4160, F value = 682,375.55) with probability of 0.01% for an F-value could occur due to noise. The optimized models were validated with error percentage of less than 5%. The established ERI models showed significant model terms and will be useful for ecological monitoring of CA application in construction industries.

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“…Adsorption mechanism is of complex nature and conventional mathematical modelling cannot be used to fully model and simulate adsorption data. This is because of the interaction of many adsorption process parameters, and thus, the resulting relationships are strongly nonlinear [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Application of multilayer perceptron network and random forest models for modelling the adsorption of chlorobenzene on a modified bentonite by intercalation with hexadecyltrimethyl ammonium (HDTMA)

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Belhocine

et al. 2021

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Prediction of adsorption capacity, one of the most important properties of any adsorbent-adsorbate system, is crucial for adsorption studies. In this investigation, two approaches such as multilayer perceptron (MLP) and random forest (RF) were used to predict the adsorption capacity of hexadecyltrimethyl ammonium modified bentonite to remove chlorobenzene (CB) from aqueous solution. The adsorption study was conducted in batch mode at different adsorption parameters. The results show that the adsorption processes were best described by Freundlich isotherm model, while the adsorption mechanism followed the pseudo-second order kinetics. It was observed that the structure of MLP model that give the best prediction consisted of three layers: input layer with four neurons, output layer with one neuron, and four neurons at hidden layer. The three important parameters for RF model were n tree = 500, m try = 1, and node size = 1. According to the results obtained, the MLP model provided slightly higher levels of accuracy with a consistently high coefficient of determination (R 2 = 0.996) and low root mean square error (RMSE = 0.00101) compared to RF model. (R 2 = 0.969, RMSE = 0.03008). Therefore, the initial concentration of CB with 35.29%, appeared to be the most influential parameter in the adsorption of CB on the modified bentonite.

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Artificial Neural Network (ANN) for Modelling Adsorption of Lead (Pb (II)) from Aqueous Solution

Cited by 57 publications

References 47 publications

Modeling of Cu(II) Adsorption from an Aqueous Solution Using an Artificial Neural Network (ANN)

Modeling of Cu(II) Adsorption from an Aqueous Solution Using an Artificial Neural Network (ANN)

Ecological Risk Indicators for Leached Heavy Metals from Coal Ash Generated at a Malaysian Power Plant

Application of multilayer perceptron network and random forest models for modelling the adsorption of chlorobenzene on a modified bentonite by intercalation with hexadecyltrimethyl ammonium (HDTMA)

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