Modeling of fixed-bed adsorption of fluoride on bone char using a hybrid neural network approach

Tovar-Gómez, Rigoberto; Moreno-Virgen, Ma. del Rosario; Dena-Aguilar, José Alonso; Hernández‐Montoya, Virginia; Bonilla-Petriciolet, Adrián; Montes-Morán, Miguel A.

doi:10.1016/j.cej.2013.05.080

Cited by 119 publications

(56 citation statements)

References 51 publications

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“…The mono-component approach is useful in binary systems [74]; nevertheless, this approach is lengthy and cumbersome for multi-metal systems. A better alternative could be the use of new tools, like artificial neural networking (ANN) [120,137], but a lot still remains to be done for its application.…”

Section: Multi-metal Systemmentioning

confidence: 99%

Metal sorption by algal biomass: From batch to continuous system

2016

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Section: Multi-metal Systemmentioning

confidence: 99%

Metal sorption by algal biomass: From batch to continuous system

2016

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“…Electrostatic attraction of dissolved F´is also known to occur in parallel with F´adsorbed via physisorption. The dominating mechanism of fluoride removal has therefore not been fully elucidated [59,62,63]; however, it is certain that the kinetics of hydoxyapatite dissolution limit the extent of ion exchange. BC has been used as a versatile adsorbent for a wide variety of other pollutants, including arsenate, dyes and heavy metals [59].…”

Section: Bone Char-based Filters For F´removalmentioning

confidence: 99%

Technologies for Decentralized Fluoride Removal: Testing Metallic Iron-based Filters

Ndé-Tchoupé

Crane

Mwakabona

et al. 2015

Water

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Since the realization in the 1930s that elevated fluoride concentrations in drinking water can have detrimental effects on human health, new methods have been progressively developed in order to reduce fluoride to acceptable levels. In the developing world the necessity for filtration media that are both low-cost and sourced from locally available materials has resulted in the widespread use of bone char. Since the early 1990s metallic iron (Fe 0 ) has received widespread use as both an adsorbent and a reducing agent for the removal of a wide range of contaminant species from water. The ion-selectivity of Fe 0 is dictated by the positively charged surface of iron (hydr)oxides at circumneutral pH. This suggests that Fe 0 could potentially be applied as suitable filter media for the negatively charged fluoride ion. This communication seeks to demonstrate from a theoretical basis and using empirical data from the literature the suitability of Fe 0 filters for fluoride removal. The work concludes that Fe 0 -bearing materials, such as steel wool, hold good promise as low-cost, readily available and highly effective decentralized fluoride treatment materials.

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“…Therefore, the utilization of ANNs in the field of adsorption processes by using biomass has recently gained interest, given the difficulty that can be encountered to fully characterize all the functionalities found in common biomass (Shojaeimehr et al, 2014). For example, ANNs have been successfully used to model the biosorption of Pb(II) using black cumin (Bingol et al, 2012), the removal of fluoride by bone char (Tovar Gomez et al, 2013), and the removal of Cu(II) using sunflower shells (Oguz and Ersoy, 2010). This approach has also been used for the study of adsorption of dyes and organic compounds (Witek-Krowiak et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models

Hamid

Jenidi

Thielemans

et al. 2016

Industrial Crops and Products

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This study observed the influence of temperature, initial Cu(II) ion concentration, and sorbent dosage on the Cu(II) removal from the water matrix using surface-oxidized cellulose nanowhiskers (CNWs) bearing carboxylate functionalities. In addition, this study focused on the actual conditions in a wastewater treatment plant. Conductometric titration of CNWs suspensions showed a surface charge of 54 and 410 mmol/kg for the unmodified and modified CNWs, respectively, which indicated that the modified CNWs provide a relatively high surface area per unit mass than the unmodified CNWs. In addition, the stability of the modified CNWs was tested under different conditions and proved that the functional groups were permanent and not degraded. Response surface methodology (RSM) and artificial neural network (ANN) models were employed in order to optimize the system and to create a predictive model to evaluate the Cu(II) removal performance of the modified CNWs. The performance of the ANN and RSM models were statistically evaluated in terms of the coefficient of determination (R 2 ), 2 absolute average deviation (AAD), and the root mean squared error (RMSE) on predicted experiment outcomes. Moreover, to confirm the model suitability, unseen experiments were conducted for 14 new trials not belonging to the training data set and located both inside and outside of the training set boundaries. Result showed that the ANN model (R 2 =0.9925, AAD = 1.15%, RMSE = 1.66) outperformed the RSM model (R 2 =0.9541, AAD = 7.07%, RMSE = 3.99) in terms of the R 2 , AAD, and RMSE when predicting the Cu (II) removal and is thus more reliable. The Langmuir and Freundlich isotherm models were applied to the equilibrium data and the results revealed that Langmuir isotherm (R 2 = 0.9998) had better correlation than the Freundlich isotherm (R 2 = 0.9461). Experimental data were also tested in terms of kinetics studies using pseudo-first order and pseudo-second order kinetic models. The results showed that the pseudo-second-order model accurately described the kinetics of adsorption.

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Modeling of fixed-bed adsorption of fluoride on bone char using a hybrid neural network approach

Cited by 119 publications

References 51 publications

Metal sorption by algal biomass: From batch to continuous system

Metal sorption by algal biomass: From batch to continuous system

Technologies for Decentralized Fluoride Removal: Testing Metallic Iron-based Filters

Predicting the capability of carboxylated cellulose nanowhiskers for the remediation of copper from water using response surface methodology (RSM) and artificial neural network (ANN) models

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