Comprehensive study on interactive effects of operational parameters by using response surface method for sodium sulfate removal from magnesium stearate aqueous slurry via electrodialysis process. Issue 30 (26th June 2016)

• Record Type: Journal Article
• Authors: Masigol, Mohammad Ali; Moheb, Ahmad; Mehrabani-Zeinabad, Arjomand
• Is Part Of: Desalination and water treatment. Volume 57:Issue 30(2016)
• Page Start: 14145
• Page End: 14157
• Publication Date: 2016-06-26
• Subjects: Response surface method -- Magnesium stearate -- Sodium sulfate -- Batch electrodialysis -- Sodium ion removal; Saline water conversion -- Periodicals; Saline water conversion; Water -- Purification; Periodicals; 628.167
• DOI: 10.1080/19443994.2015.1062426
• Languages: English
• ISSNs: 1944-3994

Abstract

Abstract: Detailed results of a comprehensive study on sodium sulfate removal from magnesium stearate (MgSt) aqueous slurry by using batch electrodialysis (ED) process are presented in this paper. Lowered operating costs and water consumption and avoiding from salty waste discharge to the environment were major advantageous of applying ED process in comparison with the conventional methods. The single and interactive effects of operating parameters including applied voltage, initial MgSt concentration, stirring speed, and process time on ED performance were studied by using response surface methodology method. Na recovery, specific power consumption, and sodium ion current efficiency were the objective parameters used to evaluate the ED process performance. The results showed that interaction terms of "voltage-MgSt concentration" and "stirrer speed-voltage" had the most noticeable effects on Na recovery and energy consumption, respectively. Confirmation experiments were performed at the optimum condition predicted by the model to find the response values to judge the reliability of the models. Under the optimum condition, the values obtained for Na recovery, energy consumption, and current efficiency were 73.14%, 0.523 kW h/mol, and 98.57%, respectively, which were in good agreement with the predicted values by the model.