Acid Mine Drainage is a water pollution type characterized by several topics such as high acidity, sulfate and heavy metal concentrations. One of the chemical characteristics is the absence of correlation between pH and conductivity, as could be expected. This last parameter is well correlated with other variables such as sulfate concentration and can be used as a field assessment. The absence of pH/conductivity correlation is largely discussed by several authors. In this work, the use of fuzzy logic algorithms in a large temporal database (over 20,000 records) has allowed us to study the “hidden” relation between them. This work finds this correlation, with some conditions such as the range of pH where it happens. Maybe the study of the usual range of pH values in previous studies has disturbed the correlation because of other chemical processes.
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Research Article|
March 01 2009
Model of behaviour of conductivity versus pH in acid mine drainage water, based on fuzzy logic and data mining techniques
A. Jiménez;
1Grupo de Geologia Costera y Recursos Hídricos, Universidad de Huelva, E-21819, Palos de la Frontera, Huelva, Spain
Tel.: +34 959 217343 Fax: +34 959 217304; E-mail: [email protected]
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J. Aroba;
J. Aroba
2Grupo de Control y Robótica, Universidad de Huelva, E-21819, Palos de la Frontera, Huelva, Spain
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M. L. de la Torre;
M. L. de la Torre
1Grupo de Geologia Costera y Recursos Hídricos, Universidad de Huelva, E-21819, Palos de la Frontera, Huelva, Spain
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J. M. Andujar;
J. M. Andujar
2Grupo de Control y Robótica, Universidad de Huelva, E-21819, Palos de la Frontera, Huelva, Spain
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J. A. Grande
J. A. Grande
1Grupo de Geologia Costera y Recursos Hídricos, Universidad de Huelva, E-21819, Palos de la Frontera, Huelva, Spain
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Journal of Hydroinformatics (2009) 11 (2): 147–153.
Article history
Received:
February 13 2008
Accepted:
September 16 2008
Citation
A. Jiménez, J. Aroba, M. L. de la Torre, J. M. Andujar, J. A. Grande; Model of behaviour of conductivity versus pH in acid mine drainage water, based on fuzzy logic and data mining techniques. Journal of Hydroinformatics 1 March 2009; 11 (2): 147–153. doi: https://doi.org/10.2166/hydro.2009.015
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