Specification and prediction of nickel mobilization using artificial intelligence methods
Groundwater and soil pollution from pyrite oxidation, acid mine drainage generation, and release and transport of toxic metals are common environmental problems associated with the mining industry. Nickel is one toxic metal considered to be a key pollutant in some mining setting; to date, its format...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/10308 |
| _version_ | 1848746197000388608 |
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| author | Gholami, Raoof Ziaii, M. Doulati Ardejani, F. Maleki, S. |
| author_facet | Gholami, Raoof Ziaii, M. Doulati Ardejani, F. Maleki, S. |
| author_sort | Gholami, Raoof |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Groundwater and soil pollution from pyrite oxidation, acid mine drainage generation, and release and transport of toxic metals are common environmental problems associated with the mining industry. Nickel is one toxic metal considered to be a key pollutant in some mining setting; to date, its formation mechanism has not yet been fully evaluated. The goals of this study are 1) to describe the process of nickel mobilization in waste dumps by introducing a novel conceptual model, and 2) to predict nickel concentration using two algorithms, namely the support vector machine (SVM) and the general regression neural network (GRNN). The results obtained from this study have shown that considerable amount of nickel concentration can be arrived into the water flow system during the oxidation of pyrite and subsequent Acid Drainage (AMD) generation. It was concluded that pyrite, water, and oxygen are the most important factors for nickel pollution generation while pH condition, SO 4, HCO3, TDS, EC, Mg, Fe, Zn, and Cu are measured quantities playing significant role in nickel mobilization. SVM and GRNN have predicted nickel concentration with a high degree of accuracy. Hence, SVM and GRNN can be considered as appropriate tools for environmental risk assessment. © Versita Sp. z o.o. |
| first_indexed | 2025-11-14T06:29:25Z |
| format | Journal Article |
| id | curtin-20.500.11937-10308 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:29:25Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-103082017-09-13T14:51:30Z Specification and prediction of nickel mobilization using artificial intelligence methods Gholami, Raoof Ziaii, M. Doulati Ardejani, F. Maleki, S. Groundwater and soil pollution from pyrite oxidation, acid mine drainage generation, and release and transport of toxic metals are common environmental problems associated with the mining industry. Nickel is one toxic metal considered to be a key pollutant in some mining setting; to date, its formation mechanism has not yet been fully evaluated. The goals of this study are 1) to describe the process of nickel mobilization in waste dumps by introducing a novel conceptual model, and 2) to predict nickel concentration using two algorithms, namely the support vector machine (SVM) and the general regression neural network (GRNN). The results obtained from this study have shown that considerable amount of nickel concentration can be arrived into the water flow system during the oxidation of pyrite and subsequent Acid Drainage (AMD) generation. It was concluded that pyrite, water, and oxygen are the most important factors for nickel pollution generation while pH condition, SO 4, HCO3, TDS, EC, Mg, Fe, Zn, and Cu are measured quantities playing significant role in nickel mobilization. SVM and GRNN have predicted nickel concentration with a high degree of accuracy. Hence, SVM and GRNN can be considered as appropriate tools for environmental risk assessment. © Versita Sp. z o.o. 2011 Journal Article http://hdl.handle.net/20.500.11937/10308 10.2478/s13533-011-0039-x unknown |
| spellingShingle | Gholami, Raoof Ziaii, M. Doulati Ardejani, F. Maleki, S. Specification and prediction of nickel mobilization using artificial intelligence methods |
| title | Specification and prediction of nickel mobilization using artificial intelligence methods |
| title_full | Specification and prediction of nickel mobilization using artificial intelligence methods |
| title_fullStr | Specification and prediction of nickel mobilization using artificial intelligence methods |
| title_full_unstemmed | Specification and prediction of nickel mobilization using artificial intelligence methods |
| title_short | Specification and prediction of nickel mobilization using artificial intelligence methods |
| title_sort | specification and prediction of nickel mobilization using artificial intelligence methods |
| url | http://hdl.handle.net/20.500.11937/10308 |