Back-propagation neural network: Box–Behnken design modelling for optimization of copper adsorption on orange zest biochar
نویسندگان
چکیده
Heavy metals adsorption by adsorbents prepared from natural materials is a low-cost effective method for their removal aqueous environments. This study aims to assess the applicability of orange zest biochar adsorb divalent copper (cupric chloride) its solution maximizing capacity using feed-forward back-propagation neural network (FFBPNN)–Box–Behnken design (BBD) modelling. BBD modelling predicted maximum 99.61% at an initial concentration copper, adsorbent dosage and temperature 100 ppm, 192.5 mg per mL feed 38 °C. The results showed best fit between experimental, FFBPNN values. Langmuir isotherm fitted well with experimental data than Freundlich model, was found be 116.28 mg/g. Also, kinetic followed Lagergren’s pseudo-first-order model. Thus, obtained conclude that one potential solution.
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ژورنال
عنوان ژورنال: International Journal of Environmental Science and Technology
سال: 2021
ISSN: ['1735-1472', '1735-2630']
DOI: https://doi.org/10.1007/s13762-021-03411-1