Optimization of Cu (II) biosorption onto sea urchin test using response surface methodology and artificial neural networks

Optimization of Cu (II) biosorption onto sea urchin test using response surface methodology and... Copper biosorption potential of the biomass prepared from shells of sea urchin from aqueous solutions at optimum process conditions was studied. Response surface methodology and artificial neural network combined with central composite design were used for modeling and optimization of biosorption and to study interaction effects of process variables. A two-level three-factor face-centered central composite design was used for the experimental design. The influence of pH, initial copper concentration and biosorbent dosage on biosorption of copper was investigated. Prediction capacities of both models were compared and found that response surface methodology showed better prediction performance than artificial neural networks. Kinetic data were well fitted to second-order rate equation showing maximum biosorption capacity of 15.625 mg/g for 100 mg/l metal solution concentration. It was further confirmed by fitting the data to Elovich model. Biosorption mechanism was investigated using intra-particle diffusion and Boyd models. The optimum copper removal efficiency of the biosorbent was found as 89.09%. Keywords Artificial Neural Networks · Biosorption · Copper · Kinetics · Response Surface Methodology · Sea urchin test Introduction pharmaceutical industries, plastic and paint, mining and metallurgical processing, paper and pulp, petrochemical and To provide amenities and facilities to unceasing global battery manufacturing industries (Jafari et al. 2015). Reduc- population, rapid industrialization is http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Environmental Science and Technology Springer Journals

Optimization of Cu (II) biosorption onto sea urchin test using response surface methodology and artificial neural networks

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Islamic Azad University (IAU)
Subject
Environment; Environment, general; Environmental Science and Engineering; Environmental Chemistry; Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution; Soil Science & Conservation; Ecotoxicology
ISSN
1735-1472
eISSN
1735-2630
D.O.I.
10.1007/s13762-018-1747-2
Publisher site
See Article on Publisher Site

Abstract

Copper biosorption potential of the biomass prepared from shells of sea urchin from aqueous solutions at optimum process conditions was studied. Response surface methodology and artificial neural network combined with central composite design were used for modeling and optimization of biosorption and to study interaction effects of process variables. A two-level three-factor face-centered central composite design was used for the experimental design. The influence of pH, initial copper concentration and biosorbent dosage on biosorption of copper was investigated. Prediction capacities of both models were compared and found that response surface methodology showed better prediction performance than artificial neural networks. Kinetic data were well fitted to second-order rate equation showing maximum biosorption capacity of 15.625 mg/g for 100 mg/l metal solution concentration. It was further confirmed by fitting the data to Elovich model. Biosorption mechanism was investigated using intra-particle diffusion and Boyd models. The optimum copper removal efficiency of the biosorbent was found as 89.09%. Keywords Artificial Neural Networks · Biosorption · Copper · Kinetics · Response Surface Methodology · Sea urchin test Introduction pharmaceutical industries, plastic and paint, mining and metallurgical processing, paper and pulp, petrochemical and To provide amenities and facilities to unceasing global battery manufacturing industries (Jafari et al. 2015). Reduc- population, rapid industrialization is

Journal

International Journal of Environmental Science and TechnologySpringer Journals

Published: Jun 4, 2018

References

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