The release and distribution of uranium from these sites was studied. Correlation analysis showed a strong link between different variables as a result of AMD ...
APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR THE CLASSIFICATION OF URANIUM DISTRIBUTION IN THE CENTRAL RAND GOLDFIELD, SOUTH AFRICA H.Tutu1, E. M. Cukrowska1, V. Dohnal2 and J. Havel2 1.
School of Chemistry, University of the Witwatersrand, P. Bag 3 WITS, 2050 Johannesburg, South Africa 2.
Department of Analytical Chemistry, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic
INTRODUCTION Mine tailings generate significant environmental impacts and contribute to water and soil pollution. The Central Rand goldfield of the Witwatersrand basin, South Africa (Fig.1) has many gold mine tailings which have contributed significantly to water and soil pollution as a result of acid mine drainage (AMD). The release and distribution of uranium from these sites was studied. Correlation analysis showed a strong link between different variables as a result of AMD produced. Principal component analysis (PCA) was used to identify influential variables which account for the pollution trends. Artificial neural networks (ANN) using the Kohonen algorithm were applied to visualise these trends and patterns in the distribution of uranium. High concentrations of this radionuclide were detected in streams in the vicinity of the tailings dumps, active slimes and reprocessing areas. The concentrations are reduced drastically in dams and wetlands as a result of precipitation and dilution effects. • Mining started in 1886 and ended in the 70s. Reprocessing of old dumps to recover gold is currently being done. • U is a byproduct of gold mining in this area. • The geochemical speciation of U influences its mobility and bioavailability in the environment. In oxidising conditions U exists as a uranyl ion, UO22+, which forms strong complexes with inorganic and organic ligands. • U is both chemotoxic and radiotoxic. It can cause kidney damage. • The World Health Organisation (WHO) recommends a limit of 140ngmL-1 of U in drinking water. Figure 1. The Witwatersrand basin
x-w
x w(new)
ANN THEORY w
Neural networks can derive meaning from complicated or imprecise data and can be used to extract patterns or detect trends The Kohonen algorithm is an unsupervised learning method. The network consists of a grid of output units and N input units. • When the neurons receive outside signals they each compute the local field: yi=Σiwijxj • The effect of the learning is to move the vector w closer to x as shown in Fig. 2
Component 1
Component 3
-0.54
0.20
0.24
-0.02
-0.92
0.32
-0.01
Salinity
NO3-
-0.83
0.04
-0.30
0.16
0.02
0.77
-
-0.29
0.56
-0.29
U
-0.89
-0.32
-0.04
Ca
-0.86
0.09
0.02
Na
-0.37
0.87
-0.01
Cl
0.87
0.11
Mg
-0.44
0.34
-0.03
Ni
K
-0.81
-0.39
0.17
Zn
-0.26
-0.81
-0.41
Al
-0.88
-0.40
0.02
Mn
-0.64
0.48
0.19
Fe
SND SND
SND SND SND SND
SND,SND
SND
STREAMS SND S S
SND
S S D, D
SND SND
S
D,D,D
SND
D,D, D
DAMS
SND S D,D,D
pH
Sulphate
0.15
0.50
0.04
Cu
-0.58
-0.20
-0.57
Co
-0.72
-0.32
0.05
Cr
0.14
-0.59
-0.20
54.17
75.92
83.53
% Cumulative variance
-0.48
W
SO4
2-
-0.20
W
-0.21
-0.31 -0.93
• The neural network consists of 2 layers: the input layer is connected to a vector of the input data set (20 sample descriptors, 20 neurons in input layer); the second (output) layer forms a map, a rectangular grid with 36 neurons in the output layer.
SND SND SND
SND SND
SND SND SND SND
WETLANDS
0.73
Eh EC
Kohonen Self-Organising Maps
W
pH
Component 2
Uranium
Key map SND SND SND
RESULTS AND DISCUSSION Table 1 Component-variable correlations
Figure 2. The learning process drags the weight vector w towards the input vector x
Figure 3. The Kohonen self-organising maps (20:6x6) Figure 4. The Kohonen maps for U, sulphate and pH distribution (Trajan Neural Network Simulator 4.0, U.K)
SND – stream near tailings dump or footprint S – stream not in the vicinity of tailings dumps W – wetland D - dam
Correlation analysis • A high positive correlation of U with other metals – Ni, Ca, Zn, Al, Mn, Cu and Co was observed. • A positive correlation with sulphates indicated AMD. Geochemical modelling results pointed to uranium existing mainly as the uranyl sulphate. PCA • The variables were reduced and represented by 3 components (Table 1).
0.07400 0.1600 0.2460 0.3320 0.4180 0.5040 0.5900 0.6760 0.7620
• Component 1 explains 54.17% of the variability in the data and is distributed by pH, conductivity, sulphates, U, Cu, Co, Zn, Mn, Al, Ni, Ca. This indicates solution chemistry in the vicinity of the tailings as a result of AMD. • Component 2 accounts for 75.92% of the cumulative variance. It is accounted for by Fe, Cr, chlorides, K and Na. There is a strong indication of redox chemistry involving Fe and Cr. The presence of an Fe redox buffer controls the water chemistry in streams in the distal from tailings. Na and K could be attributed to be from external sources particularly the addition of KCN and NaCN during reprocessing of tailings. Cyanide degrades quickly and thus poses no environmental threat in these tailings. • Component 3 accounts for 83.53% of the cumulative variance. It has a high contribution from nitrates and Cu. This is expected in wetlands where there is a high content of organic matter. • The application of KSOM shows that samples can be classified. The 3 classes were found to be streams, dams and wetlands. • The pH of the water flowing near dumps was found to be lower than in other samples. Tailings footprints displayed the lowest pH values and highest sulphate contents due to the effects of AMD. • Sulphate values were low in dams and wetlands owing to precipitation as pH levels increase and also due to sulphate reduction.
CONCLUSIONS • Correlation analysis showed the interdependence of variables. The correlation of U, sulphates, pH and other metals showed the impact of AMD. • PCA gave insight to the most important variables determining water chemistry. • Using ANN, the origin and distribution trends of U were investigated. • Gold tailings dumps are sources of mobile U to the biosphere as a result of reprocessing activities that lead to the release of AMD and the subsequent leaching of heavy metals. As such, elevated levels of U were observed in water bodies in the vicinity of the dumps. • The ANN results can provide a useful platform for planning for rehabilitation and monitoring of tailings activities. ACKNOWLEDGEMENTS This work was supported by National Research Foundation of South Africa and the Grant Agency of Czech Republic, grant no. GAČR 203/02/1103. REFERENCES • http://www.willamette.edu/~gorr/classes/cs449/Unsupervised • Tutu, H., Cukrowska, E. M., McCarthy, T. S., Mphephu, N. F., Hart, R. (2003). Determination and modelling of geochemical speciation of uranium in gold mine polluted land in South Africa. In: Proceedings of the International Congress on Mine Water and the Environment, Johannesburg, South Africa, 137-155. • Viljoen, M.J. and Reimold, W.U. (1999). An introduction to SA’s geological and mining heritage, Geological Society of South Africa and Mintek, 37-39. •. Patočka, J. Kassa, R. Štětina, G. Šafr, and J. Havel, Toxicological Aspects of Depleted Uranium, J. Applied Biomed., 2: 37–42, 2004, ISSN 1214-0287.
