Abstract
The removal of contaminants from process effluent streams is very important, as the demand for water continue to increase. The contaminants in the effluent also pose an environmental pollution challenge and result in affecting human health. The objective of this study was to investigate the reduction of nickel in the presence of hydrazine (reducing agent) and ethylene maleic anhydride (EMA) as an additive. Reduction crystallisation was conducted at 0.5 g/L of Ni metal solution, 1 M hydrazine in the presence of EMA and at temperature of 60oC and pH of 10.7-11. Various concentrations of EMA were investigated and varied from 2 mg/L to 10 mg/L. The effluent before and after the reduction crystallization process was analyzed to study the removal efficiency. The seeding material was also analyzed to measure the elemental composition, so as to understand the reduction mechanism and the particle size distribution of the nickel seed. Electron Microscopy. The results obtained showed that hydrazine in the presence of an additive can reduce nickel to its elemental state. When the concentration of EMA was increased the percentage removal of Ni2+ in Ni- solution also increased from ~0-115%. The highest Ni % removal was obtained when 5, 7 and 10mg/L of EMA was used with percentage removal above 80-115%. The XRF results also confirmed that as EMA concentration was increased, the relative proportion of nickel increased from 98.9% to 99.7% with the highest concentration of EMA (C=10mg/l). The presence of EMA contributed to the transformation the morphology of the nickel powder.
Original language | English |
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Pages (from-to) | 649-656 |
Number of pages | 8 |
Journal | Procedia Manufacturing |
Volume | 35 |
DOIs | |
Publication status | Published - 2019 |
Event | 2nd International Conference on Sustainable Materials Processing and Manufacturing, SMPM 2019 - Sun City, South Africa Duration: 8 Mar 2019 → 10 Mar 2019 |
Keywords
- Eduction crystallization
- Electroless plating
- Hydrazine
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Artificial Intelligence