Sugar beet lignocellulose waste as biosorbents: surface functionality, equilibrium studies and artificial neural network modeling

To meet sustainable development criteria, this paper deals with the possible utilization of solid waste materials generated from single and multiple successive processing of sugar beet (i.e., from the production of sugar, bioethanol and pectin) in wastewater treatment. Waste lignocellulose materials...

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Bibliographic Details
Published in:International journal of environmental science and technology (Tehran) 2023-03, Vol.20 (3), p.2503-2516
Main Authors: Kukić, D., Šćiban, M., Brdar, M., Vasić, V., Takači, A., Antov, M., Prodanović, J.
Format: Article
Language:English
Subjects:
Aquatic Pollution
Earth and Environmental Science
Ecotoxicology
Environment
Environmental Chemistry
Environmental Science and Engineering
Original Paper
Soil Science & Conservation
Waste Water Technology
Water Management
Water Pollution Control
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Summary:To meet sustainable development criteria, this paper deals with the possible utilization of solid waste materials generated from single and multiple successive processing of sugar beet (i.e., from the production of sugar, bioethanol and pectin) in wastewater treatment. Waste lignocellulose materials after extraction of sucrose, successive extractions of sucrose and pectin, as well as successive extractions of sucrose and pectin followed by enzymatic hydrolysis of cellulose were investigated as biosorbents for heavy metal removal. Surface characterization was performed by Fourier transform infrared spectroscopy and Boehm’s titration which showed heterogeneity regarding functional groups and the acidic surface of adsorbents. Also, a possible involvement of certain functional groups (hydroxyl, phenolic, carbonyl, amino) in the adsorption process was discussed. Equilibrium studies showed that these materials had greater adsorption capacity for Cu 2+ compared to capacity for Cr 6+ ions and that the adsorption process by various adsorbents could not be described by the same isotherm model. Adsorption mechanism study implied that ion exchange was not the only mechanism of Cu 2+ binding onto investigated biosorbents. Also, the Cu 2+ removal performance of waste materials was successfully predicted by applying a three-layer neural network with 6 neurons in the hidden layer.
ISSN:1735-1472
1735-2630
DOI:10.1007/s13762-022-04140-9