Preparation of a new adsorbent for the removal of arsenic and its simulation with artificial neural network-based adsorption models

Rodríguez-Romero, J.A.; Mendoza-Castillo, Didilia I.; Reynel-Ávila, Hilda Elizabeth; Rodríguez, Luis Mario González; Bonilla-Petriciolet, Adrián; Durán‐Valle, Carlos J.; Camacho-Aguilar, K.I.

doi:10.1016/j.jece.2020.103928

Cited by 46 publications

(7 citation statements)

References 45 publications

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“…In this regard, Rodríguez-Romero et al , 2020 hybridized the ANN with classical isotherm and kinetic equations to improve the arsenic adsorption capacity of carbon-enriched biomaterial. 211 The authors obtained the ANN-Langmuir model from the classical Langmuir functionality using initial metal concentration, pH and temperature as input parameters with sigmoid activation functions. Likewise, the other hybridized models were also obtained.…”

Section: Ann Framework For Hybridizing Classical Adsorption Modelsmentioning

confidence: 99%

Application of neural network in metal adsorption using biomaterials (BMs): a review

Nighojkar

Zimmermann

Ateia

et al. 2023

Environ. Sci.: Adv.

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Section: Ann Framework For Hybridizing Classical Adsorption Modelsmentioning

confidence: 99%

Application of neural network in metal adsorption using biomaterials (BMs): a review

Nighojkar

Zimmermann

Ateia

et al. 2023

Environ. Sci.: Adv.

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“…Te parameters of this adsorption model were calculated from the correlation of adsorption isotherms of the raw and regenerated bone char samples. A simultaneous nonlinear regression of all experimental adsorption isotherms was performed using an artifcial neural network to calculate the best model parameters following the procedure reported by Rodríguez-Romero et al [33]. A feed-forward artifcial neural network with one hidden layer and one hidden neuron was employed in data modeling, where the input variables were the fuoride equilibrium concentrations and the number of regeneration cycles, while the output variables were the parameters of this statistical physics model, as shown in Figure 1(a).…”

Section: Calculation Of Physicochemical Parameters Of Fluoride Adsorp...mentioning

confidence: 99%

Regeneration Analysis of Bone Char Used in Water Defluoridation: Chemical Desorption Route, Surface Chemistry Analysis and Modeling

González-Ponce

Mendoza-Castillo

Bonilla-Petriciolet

et al. 2023

International Journal of Chemical Engineering

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High concentrations of fluoride (F−) in drinking water represent a public health threat, and consequently, effective and sustainable methods are required to improve the water quality, mainly in developing and low-income countries. This study focused on the thermodynamics of fluoride adsorption on bone char regenerated with NaOH for water defluoridation. A detailed analysis of the number of fluoride adsorption/desorption cycles, their impact on the performance and surface chemistry of bone char using different NaOH concentrations, and modeling of the adsorption mechanism using statistical physics theory was carried out. The results showed that 0.075 mol/L NaOH was effective in recuperating the defluoridation properties of bone char with a regeneration efficiency higher than 90% during five adsorption/desorption cycles. Bone char regeneration efficiency decreased up to 64% after ten adsorption/desorption cycles with a maximum fluoride adsorption capacity of 0.18 mmol/g. NaOH restored the bone char surface properties for ligand exchange of the fluoride anions via the hydroxyapatite functionalities contained in this adsorbent. It was calculated that around 0.25–0.46 mmol/g hydroxyapatite ligand exchange sites of regenerated bone char samples could be involved in the fluoride adsorption, which was also expected to be a mono-ligand mechanism. The reduction in defluoridation properties of bone char during the regeneration cycles was attributed to the decrease in the ligand exchange capacity as well as the deactivation and blocking of some functional groups of hydroxyapatite, which limited their participation in consecutive adsorption processes. This study contributes to the optimization of the recycling and reuse of bone char for fluoride removal from water to reduce the operating defluoridation costs, thus enhancing the application of this technology in low-income areas where fluorinated water represents a threat to public health.

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“…However, it is convenient to remark that ANN can be considered as black-box (i.e., empirical) models that are effective for correlation and prediction but without providing an additional theoretical understanding of the system under analysis. This drawback of ANN can be partially resolved via its hybridization with theoretical adsorption models [105,106]. Mathematically, the performance of an adsorption system is a nonlinear function depended on the adsorbent properties, chemistry of adsorbate(s), operating conditions, fluid properties, and equipment configuration.…”

Section: Applications Of Anns To Model the Adsorption Of Water Pollut...mentioning

confidence: 99%

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

Reynel-Ávila

Aguayo‐Villarreal

Díaz-Muñoz

et al. 2022

Adsorption Science & Technology

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The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

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Preparation of a new adsorbent for the removal of arsenic and its simulation with artificial neural network-based adsorption models

Cited by 46 publications

References 45 publications

Application of neural network in metal adsorption using biomaterials (BMs): a review

Application of neural network in metal adsorption using biomaterials (BMs): a review

Regeneration Analysis of Bone Char Used in Water Defluoridation: Chemical Desorption Route, Surface Chemistry Analysis and Modeling

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

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