# Quality Evaluation of Wasteless Mining in Dongguashan Based on Intuitionistic Fuzzy Set and VIKOR
## 4 Case Study
### Evaluation of Wasteless Mining in Dongguashan Based on Intuitionistic Fuzzy Set and VIKOR
The AHP method is now used to calculate the weight values of each index as shown in Table 11.  
Based on the above study, the index weights of the waste rock utilization, tailing sand utilization, wastewater exceedance, waste gas, soil quality and resource recovery in the intuitive fuzzy set are obtained as follows:  
\(W=(0.062,0.0.062,0.195,0.195,0.114,0.372)^{\mathrm{T}}\)  
(3) VIKOR method sorting  
The first five rows of the weighted intuitionistic fuzzy sets of the evaluation criteria, and the sixth row is the weighted intuitionistic fuzzy set of the Dongguanhan Copper Mine. After that, the VIKOR method is used to rank the effectiveness of the wasteless mining, the ideal effectiveness and the worst effectiveness of the comprehensive evaluation matrix are as follows:  
\(A^{+}=(<1.00,0.00><1.00,0.00><1.00,0.00><1.00,0.00><1.00,0.00><1.00,0.00>)\)  
\(A^{-}=(<0.00,1.00><0.00,1.00><0.00,1.00><0.00,1.00><0.00,1.00>)\)  
To balance the importance of the group utility value and the individual regret value in the quality evaluation of wasteless mining, Equation (3) and Equations (7)-(9) are combined, and \(\upgamma=0.5\) is used to calculate the values of S, R and Q. The calculation results are shown in Table 12.  
The above results show that the relative proximity intervals of the wasteless mining evaluation criteria are as follows:  
Class I wasteless mining:  
\(0.0\leq\mathrm{S}_{1}\leq 0.2\), \(0.000\leq\mathrm{R}_{1}\leq 0.074\) and \(0.000\leq\mathrm{Q}_{1}\leq 0.199\)  
Class II wasteless mining:  
\(0.2\leq\mathrm{S}_{1}\leq 0.4\), \(0.074\leq\mathrm{R}_{1}\leq 0.149\) and \(0.199\leq\mathrm{Q}_{1}\leq 0.398\)  
Class III wasteless mining:  
\(0.4\leq\mathrm{S}_{1}\leq 0.6\), \(0.149\leq\mathrm{R}_{1}\leq 0.223\) and \(0.398\leq\mathrm{Q}_{1}\leq 0.598\)  
Class IV wasteless mining:  
\(0.6\leq\mathrm{S}_{1}\leq 1.0\), \(0.223\leq\mathrm{R}_{1}\leq 0.372\) and \(0.598\leq\mathrm{Q}_{1}\leq 0.996\) \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{O_{j}}\) & \(\mathbf{O_{1}}\) & \(\mathbf{O_{2}}\) & \(\mathbf{O_{3}}\) & \(\mathbf{O_{4}}\) & \(\mathbf{O_{5}}\) & \(\mathbf{O_{6}}\) & \(\mathbf{w_{j}}\) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **Waste Rock** \\ **Utilization** \\ \end{tabular} } & **Tailings** & **Wastewater** & \multirow{4}{*}{**Exheast Gas**} & **Quality** & **Recovery** & \\  & **Tailings** & **Utilization** & & **Standard** & & **of Soil** & **of Resource** & \\ \hline \(\mathbf{O_{1}}\) & 1 & 1 & 1/3 & 1/3 & 1/2 & 1/6 & 0.062 \\ \(\mathbf{O_{2}}\) & 1 & 1 & 1/3 & 1/3 & 1/2 & 1/6 & 0.062 \\ \(\mathbf{O_{3}}\) & 3 & 3 & 1 & 1 & 2 & 1/2 & 0.195 \\ \(\mathbf{O_{4}}\) & 3 & 3 & 1 & 1 & 2 & 1/2 & 0.195 \\ \(\mathbf{O_{5}}\) & 2 & 2 & 1/2 & 1/2 & 1 & 1/3 & 0.114 \\ \(\mathbf{O_{6}}\) & 6 & 6 & 2 & 2 & 3 & 1 & 0.372 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: AHP method to calculate index weights. \begin{table}
\begin{tabular}{c c c} \hline \hline  & \(\mathbf{S}\) & \(\mathbf{R}\) & \(\mathbf{Q}\) \\ \hline I* & 0 & 0 & 0 \\ I & 0.2 & 0.0744 & 0.1992 \\ II & 0.4 & 0.1488 & 0.3984 \\ III & 0.6 & 0.2232 & 0.5976 \\ IV & 1 & 0.372 & 0.996 \\ Dongguanhan Copper Mine & 0.194348715 & 0.091613261 & 0.219325372 \\ Affiliation Level & I & II & II \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Evaluation results of wasteless mining quality. Through the calculation, the group utility value is 0.1943 \(\in\) (0.0.2), belonging to level I; the individual regret value is 0.0916 \(\in\) (0.074, 0.149), belonging to level II; the compromise value is 0.2193 \(\in\) (0.199, 0.398), belonging to level II. The S, R and Q indicators of Dongguashan are ranked according to the above numerical data, and the level of wasteless mining at Dongguashan Copper Mine is evaluated finally as Grade II on a comprehensive basis. (4) Sensitivity Analysis  
The value of the decision mechanism coefficient \(\
u\) has a great influence on the decision outcome. In this study, \(\
u\) = 0.5 is set to combine the maximum grouping benefits and the minimum individual regret.