Modelling and efficiency evaluation of the continuous biosorption of Cu(II) and Cr(VI) from water by agricultural waste materials

Blagojev, Nevena; Vasić, Vesna; Kukić, Dragana V.; Šćiban, Marina; Prodanović, Jelena M.; Bera, Oskar

doi:10.1016/j.jenvman.2020.111876

Cited by 19 publications

(16 citation statements)

References 35 publications

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“…ANN is a powerful modeling method for determining the nonlinear relationship between variables 30 . This study was performed by feed-forward backpropagation and Levenberg–Marquardt using MATLAB 2018 31 , 32 . In the neural network, there are several inputs, hidden and output layers.…”

Section: Methodsmentioning

confidence: 99%

Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method

Morovati

Abbasi²,

Samaei³

et al. 2022

Sci Rep

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Diesel oil is known to be one of the major petroleum products that can pollute water and soil. Soil pollution caused by petroleum hydrocarbons has substantially impacted the environment, especially in the Middle East. In this study, modeling and optimization of hexadecane removal from soil was performed using two pure cultures of Acinetobacter and Acromobacter and consortium culture of both bacterial species using artificial neural network (ANN) method. Then the best ANN structure was proposed based on mean square error (MSE) as well as correlation coefficient (R) for pure cultures of Acinetobacter and Acromobacter as well as their consortium. The results showed that the correlations between the actual data and the data predicted by ANN (R2) in Acromobacter, Acinetobacter and consortium of both cultures were 0.50, 0.47 and 0.63, respectively. Despite the low correlation between the experimental data and the data predicted by the ANN, the correlation coefficient and the precision of ANN for the consortium was higher. As a result, ANN had desirable precision to predict hexadecan removal by the cobsertium culture of Ochromobater and Acintobacter.

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Section: Methodsmentioning

confidence: 99%

Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method

Morovati

Abbasi²,

Samaei³

et al. 2022

Sci Rep

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“…The biosorption capacity of copper by dried Chlorella pyrenoidosa was modelled by Rezende Moreira et al using Response Surface Methodology (RSM) built based on a Box-Behnken design and artificial neural networks (ANN) [53]. A parallel sigmoidal (PS) model was applied by Blagojev et al to describe the biosorption process and confirm its applicability to different types of biomass and various types of heavy metal ion [54]. Selva Filho et al developed a two-parameter model based on a central composite rotatable design to find the optimal oil-water separation efficiency, involving floating macrophytes of the species Eichhornia crassipes in constructed wetlands [55].…”

Section: Introductionmentioning

confidence: 99%

Modeling and Optimization of Heavy Metals Biosorption by Low-Cost Sorbents Using Response Surface Methodology

Ferţu

Bulgariu

2022

Processes

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This paper exploits, through modeling and optimization, the experimental laboratory data on the biosorption of heavy metal ions Pb(II), Cd(II), and Zn(II) from aqueous media using soybean and soybean waste biomasses. The biosorption modeling was performed using the Response Surface Methodology, followed by optimization based on numerical methods. The aim of the modeling was to establish the most probable mathematical relationship between the dependent variables (the biosorption efficiency of the biosorbents when adsorbing metal ions, R(%), and the biosorption capacity of sorbents, q(mg/g)) and the process parameters (pH; sorbent dose, DS (g/L); initial metal ion concentration in solution, c0 (mg/L); contact time, tc (min); temperature, T (°C)), validated by methodologies specific to the multiple regression analysis. Afterward, sets of solutions were obtained through optimization that correlate various values of the process parameters to maximize the objective function. These solutions also confirmed the performance of soybean waste biomass in the removal of heavy metal ions from polluted aqueous effluents. The results were validated experimentally.

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“…In earlier times, Laszlo reported an anion sorbent with amine groups prepared from sugarcane bagasse powders (Laszlo 1996). Subsequently, rice hull, soy beans, corn stalk, wheat straw, and peanut shell were also selected for preparing anion biosorbents after modification with triethylamine, trimethylamine, or quaternary ammonium salts, i.e., trimethyl dihydroxypropyl ammonium chloride (Marshall and Wartelle 2004;Anirudhan et al 2006;Blagojev et al 2021). These low-cost anion biosorbents exhibit good potential for removing anionic pollutants, such as nitrate, sulfate, phosphate, arsenate, and chromate from wastewater.…”

Section: Introductionmentioning

confidence: 99%

Preparation and characterization of spherical lignocellulose-based anion exchanger from sugarcane bagasse

Cao

Hong

et al. 2022

BioRes

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Biosorption is considered a promising technique for removing heavy metals from water. However, a biosorbent is usually prepared in the form of biomass powder that has drawbacks in stability and uniformity. Herein, a spherical lignocellulose-based anion exchanger (LCB-AE) was prepared from sugarcane bagasse through the method of dissolution-regeneration of biomass followed by quaternary ammonium modification. Dissolution-regeneration conditions of raw biomass were optimized, and the prepared materials were characterized by element composition analysis, pore-structure analysis (mercury intrusion porosimetry), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and carbon-13 nuclear magnetic resonance (13C-NMR) analyses. The LCB-AE has a macro-porous structure and a rough surface occupied mainly by quaternary ammonium and hydroxyl groups. Adsorption selectivity of LCB-AE follows the order of CrO42- > PO43- > SO42- > NO3-, and adsorption isotherms agree well with the Langmuir model, which suggests the experimental exchange abilities are approximately 0.8 to 0.9 mEq/g. These results show that LCB-AE as a new spherical biosorbent has the potential to be used for anions removal from water.

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Modelling and efficiency evaluation of the continuous biosorption of Cu(II) and Cr(VI) from water by agricultural waste materials

Cited by 19 publications

References 35 publications

Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method

Modelling of n-Hexadecane bioremediation from soil by slurry bioreactors using artificial neural network method

Modeling and Optimization of Heavy Metals Biosorption by Low-Cost Sorbents Using Response Surface Methodology

Preparation and characterization of spherical lignocellulose-based anion exchanger from sugarcane bagasse

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