Insights into the estimation of heavy metals ions sorption from aqueous environment onto natural zeolite

Dashti, Amir; Amirkhani, Farid; Jokar, Mojtaba; Mohammadi, Amir H.; Chau, Kwok Wing

doi:10.1007/s13762-020-02912-9

Cited by 13 publications

(13 citation statements)

References 83 publications

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“…ANN model showed excellent capability for removal efficiency prediction 73 LSSVR, ANN, GP, ANFISPSO, PNR MSE, R 2 , AARD Electronegativity, first ionization energy, initial pH and equilibrium pH Ionic species sorbed Adsorption of Zn, Ni, Cd, Pb heavy metal ions on natural zeolite. All the used models confirmed perfection in the simulation of adsorption process over testing and training trials 74 ANN MSE, R 2 Adsorbent weight, dye content, and ultrasonic Adsorption capacity Adsorption of Cd and Co on ZnO-NRs-AC. The obtained results showed that the ANN model were able to predict and model the adsorption process 75 ANN RMSE, R 2 Removal duration, C 0 , dosage Separation efficiency Adsorption of Pb(II) and Cu(II) ions on nanocomposites of rice straw and Fe 3 O 4 nanoparticles.…”

Section: Resultsmentioning

confidence: 63%

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

Parsaei

Roudbari

Piri

et al. 2022

Sci Rep

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We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model’s training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.

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Section: Resultsmentioning

confidence: 63%

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

Parsaei

Roudbari

Piri

et al. 2022

Sci Rep

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“…In this work, we choose the RBF out of existing kernel functions, which is defined as follows 47 in which σ 2 suggests the tunable variable of the LSSVM model. These two variables σ 2 and μ can be tuned/adjusted by minimizing the cost function, which is the mean squared error (MSE) of the LSSVM model and actual values as follows 48 where y pred denotes the values provided by the LSSVM and y exp represents the experimental data values. In the current study, we used the CSA method introduced by Xavier-de-Souza et al ( 49 ) to adjust the value of these parameters.…”

Section: Model Developmentmentioning

confidence: 99%

“…In this work, we choose the RBF out of existing kernel functions, which is defined as follows 47 (11) in which σ 2 suggests the tunable variable of the LSSVM model. These two variables σ 2 and μ can be tuned/adjusted by minimizing the cost function, which is the mean squared error (MSE) of the LSSVM model and actual values as follows 48…”

Section: Model Developmentmentioning

confidence: 99%

Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

et al. 2021

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Amino acid salt (AAs) aqueous solutions have recently exhibited a great potential in CO 2 absorption from various gas mixtures. In this work, four hybrid machine learning methods were developed to evaluate 626 CO 2 and AAs equilibrium data for different aqueous solutions of AAs (potassium sarcosinate, potassium l -asparaginate, potassium l -glutaminate, sodium l -phenylalanine, sodium glycinate, and potassium lysinate) gathered from reliable references. The models are the hybrids of the least squares support vector machine and coupled simulated annealing optimization algorithm, radial basis function neural network (RBF-NN), particle swarm optimization–adaptive neuro-fuzzy inference system, and hybrid adaptive neuro-fuzzy inference system. The inputs of the models are the CO 2 partial pressure, temperature, mass concentration in the aqueous solution, molecular weight of AAs, hydrogen bond donor count, hydrogen bond acceptor count, rotatable bond count, heavy atom count, and complexity, and the CO 2 loading capacity of AAs aqueous solution is considered as the output of the models. The accuracies of the models’ results were verified through graphical and statistical analyses. RBF-NN performance is promising and surpassed that of other models in estimating the CO 2 loading capacities of AAs aqueous solutions.

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“…Clays and zeolites also make viable the elimination of contaminants existent in water by ion exchange mechanism between iron and manganese and the available cations existent in the minerals structure (Dashti et al 2021). The negative charged surface of zeolite and clay can attract the positive charge of metal particles from water (Givehchi et al 2015) which is an additional mechanism of metal ions removal.…”

Section: Removal Of Fe 2+ and Mn 2+ In Rainwatermentioning

confidence: 99%

Rainwater treatment: an approach for drinking water provision to indigenous people in Ecuadorian Amazon

Pineda

Guaya

Rivera

et al. 2021

Int. J. Environ. Sci. Technol.

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This study is about the use of naturally occurring filtering materials for rainwater treatment for drinking water proposal. Crushed gravel, ceramic spheres from natural clays, silica sand and natural zeolite were used as filtering materials. The mineralogical composition of filtering materials was determined, being the illite and mordenite the major components of ceramic spheres and natural zeolite, respectively. Naturally occurring materials were simultaneous evaluated on two configuration of pilot plant systems (biofilters) for rainwater treatment. Three columns were arranged in series with unstratified flooded beds. The first stage was packed using crushed gravel. The second stage was packed using ceramic spheres. The third stage was packed with silica sand for the first plant and a natural zeolite was used for the second pilot plant system. Finally, a last stage of ultraviolet disinfection was incorporated. The trial period was 90 days, and it was evaluated the removal of Fe+2 and Mn+2, total coliforms, faecal coliforms and Escherichia col (E. coli). The rainwater treatment system using natural zeolite provided better results than the one using silica sand at third stage. The concentration of Fe+2 and Mn+2 was below the maximum permissible limits within 45 days. The efficiency of the treatment systems was optimal within 45 days, after the efficiency decreased progressively. Then, it is an attractive proposal for rural areas in developing countries for single-family water treatment systems.

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Insights into the estimation of heavy metals ions sorption from aqueous environment onto natural zeolite

Cited by 13 publications

References 83 publications

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Rainwater treatment: an approach for drinking water provision to indigenous people in Ecuadorian Amazon

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Insights into the estimation of heavy metals ions sorption from aqueous environment onto natural zeolite

Cited by 13 publications

References 83 publications

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment

Evaluation of CO2 Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods

Rainwater treatment: an approach for drinking water provision to indigenous people in Ecuadorian Amazon

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Evaluation of CO₂ Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods