Wheat and ryegrass biomass ashes as effective sorbents for metallic and organic pollutants from contaminated water in lab-engineered cartridge filtration system

Guérin, Théo; Ghinet, Alina; Hossart, Marc; Waterlot, Christophe

doi:10.1016/j.biortech.2020.124044

Cited by 7 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many researchers have found that by using this new method, in which different categories of biomass can be used as biosorbents, toxic pollutants can be selectively removed from aqueous solutions at desired residual concentration levels [18,39,[41][42][43][44]. As a broader spectrum of properties suitable for biosorbent requirements has been discovered for biomass, the concept of biosorption has become increasingly attractive in various fields.…”

Section: Introductionmentioning

confidence: 99%

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

Ferţu

Bulgariu

2022

Processes

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Ferţu

Bulgariu

2022

Processes

View full text Add to dashboard Cite

show abstract

“…Biomass waste from industry and agriculture is proving to be a viable alternative for removing heavy metals from polluted effluents because they have functional groups on their porous surface capable of retaining metals by predominantly ionic bonds, but also hydrogen bonding and the coordination bond in metal ion complexation [11][12][13]. Some studies have used agricultural waste (fruit kernels, wood waste, plant waste, shells or shells from some vegetables or seeds) due to low procurement costs and their abundance, even if they have a lower biosorption capacity than some synthetic adsorbents [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Biosorption Process of Heavy Metal Ions on Soybean-Based Low-Cost Biosorbents Using Artificial Neural Networks

et al. 2022

View full text Add to dashboard Cite

Pollution of the environment with heavy metals requires finding solutions to eliminate them from aqueous flows. The current trends aim at exploiting the advantages of the adsorption operation, by using some low-cost sorbents from agricultural waste biomass, and with good retention capacity of some heavy metal ions. In this context, it is important to provide tools that allow the modeling and optimization of the process, in order to transpose the process to a higher operating scale of the biosorption process. This paper capitalizes on the results of previous research on the biosorption of heavy metal ions, namely Pb(II), Cd(II), and Zn(II) on soybean biomass and soybean waste biomass resulting from biofuels extraction process. The data were processed by applying a methodology based on artificial neural networks (ANNs) and evolutionary algorithms (EAs) capable of evolving ANN parameters. EAs are represented in this paper by the differential evolution (DE) algorithm, and a simultaneous training and determination of the topology is performed. The resulting hybrid algorithm, hSADE-NN was applied to obtain optimal models for the biosorption process. The expected response of the system addresses biosorption capacity of the biosorbent (q, mg/g), the biosorption efficiency (E, %), as functions of input parameters: pH, biosorbent dose (DS, mg/g), the initial concentration of metal in the solution (c0, mg/L), contact time (tc, h), and temperature (T, °C). Models were developed for the two output variables, for each metal ion, finding a high degree of accuracy. Furthermore, the combinations of input parameters were found which can lead to an optimal output in terms of biosorption capacity and biosorption efficiency.

show abstract

Using Wheat Straw for Treatment of Urban Surface Water Run-Offs

Samodolova

Samodolov

Ulrikh

et al. 2023

Lecture Notes in Civil Engineering

View full text Add to dashboard Cite

Wheat and ryegrass biomass ashes as effective sorbents for metallic and organic pollutants from contaminated water in lab-engineered cartridge filtration system

Cited by 7 publications

References 47 publications

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

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

Modeling the Biosorption Process of Heavy Metal Ions on Soybean-Based Low-Cost Biosorbents Using Artificial Neural Networks

Using Wheat Straw for Treatment of Urban Surface Water Run-Offs

Contact Info

Product

Resources

About