Removal of Mercury (Hg(II)) from Seaweed Extracts by Electrodialysis and Process Optimization Using Response Surface Methodology

Abstract

In this work, response surface methodology (RSM) was employed to model and optimize electrodialysis process for mercury (Hg(II)) removal from seaweed extracts. Box-Behnken design (BBD) was utilized to evaluate the effects and the interaction of influential variables such as operating voltage, influent flow rate, initial concentration of Hg(II) on the removal rate of Hg(II). The developed regression model for removal rate response was validated by analysis of variance, and presented a good agreement of the experimental data with the quadratic equation with high value coefficient of determination value ( R ² = 0.9913, R _Adj ² = 0.9678). The optimum operating parameters were determined as 7.17 V operating voltage, 72.54 L h ⁻¹ influent flow rate and 5.04 mg L ⁻¹ initial concentration of mercury. Hg(II) removal rate of 76.45% was acquired under the optimum conditions, which showed good agreement with model-predicted (75.81%) result. The results revealed that electrodialysis can be considered as a promising strategy for removal of Hg(II) from seaweed extracts.

Journal

Journal ID (publisher-id): JOUC

Title: Journal of Ocean University of China

Publisher: Science Press and Springer (China )

ISSN (Electronic): 1672-5182

Publication date (Electronic): 20 December 2019

Publication date (Print): 01 February 2020

Volume: 19

Issue: 1

Pages: 135-142

Affiliations

¹College of Chemical and Environmental Engineering, Shandong University of Science and Technology, Qingdao 266590, China

²Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education; College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China

Author notes

*Corresponding author: MA Zhun, E-mail: mzyxy199@ 123456163.com

Article

Publisher ID: s11802-020-4069-1

DOI: 10.1007/s11802-020-4069-1

Copyright statement: Copyright © Ocean University of China, Science Press and Springer-Verlag GmbH Germany 2020.

License:

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Product

Self URI (journal-page): https://www.springer.com/journal/11802