Application of fuzzy logic and data mining techniques as tools for qualitative interpretation of acid mine drainage processes

2007 ◽  
Vol 53 (1) ◽  
pp. 135-145 ◽  
Author(s):  
J. Aroba ◽  
J. A. Grande ◽  
J. M. Andújar ◽  
M. L. de la Torre ◽  
J. C. Riquelme
2020 ◽  
Vol 231 (4) ◽  
Author(s):  
María J. Rivera ◽  
María Santisteban ◽  
Javier Aroba ◽  
José Antonio Grande ◽  
José Miguel Dávila ◽  
...  

2009 ◽  
Vol 11 (2) ◽  
pp. 147-153 ◽  
Author(s):  
A. Jiménez ◽  
J. Aroba ◽  
M. L. de la Torre ◽  
J. M. Andujar ◽  
J. A. Grande

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.


Author(s):  
Jose M. Davila ◽  
Aguasanta M. Sarmiento ◽  
Javier Aroba ◽  
Juan C. Fortes ◽  
Jose A. Grande ◽  
...  

The Odiel River Basin, located in the Iberian Pyrite Belt (IPB), is heavily affected by acid mine drainage (AMD), which occurs when pyritic minerals from sulfide mining areas are exposed to atmospheric, hydrological or biological weathering. This paper presents a hydrochemical characterization of parameters in the Odiel River Basin by means of Fuzzy Logic and data mining methodologies to determine the seasonal influence of AMD in polluted waters that have not been used before for a basin in this environmental area. This technique was proven to be effective, providing results that could not be achieved by using classic statistics, because it allows us to characterize the different parameters separately and also their relationships in waters affected by AMD in a qualitative manner based on the antecedents and according to the conditions (rules) imposed by the consequents (in this case, the Fe(II) and accumulated rainfall over 30 days). Thus, it was possible to confirm that hydrochemistry is greatly affected by seasonal changes, with a higher pH in the wet season (up to 8.59) compared to 2.12, the minimum pH value reached in the dry season. Accordingly, higher concentrations of most of the metals were observed in the dry season (e.g., up to 4000 mg/L of Fe (II)), with the exception of the values found after the first rains that occur in the early fall. With the use of the Fuzzy Logic technique, it was observed that, during the wet season, lixiviates with a higher Fe content have higher metal concentrations, and in the dry season, the behavior is the opposite.


2017 ◽  
Author(s):  
D. Kirk Nordstrom ◽  
◽  
Charles N. Alpers ◽  
Kate M. Campbell

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