# A Method of Ore Blending Based on the Quality of Benefication and Its Application in a Concentrator
## 2 Materials and Methods
### Optimization Model  
According to the neural network results and the scene's actual situation, the optimization model can be established. Before ore blending, each outlet's ore properties should be obtained as the ore blending model's input data. Input the raw ore properties into the neural network model trained above to calculate the concentrate recovery rate's predicted value. The absolute value of the difference between the predicted value and the target value is used as the optimization model's objective function. According to the field experience, determine the ore blending indicators that make the sequence work normally and take this as the constraint conditions. In addition, the difference between the ore blending plan of this stope and other studies is that the unit amount of the mining volume of this stope is not \"1 ton\", but \"1 vehicle.\" It means the full tonnage of a truck. Therefore, the decision variable is changed from mining volume to the number of mining vehicles. Then the optimization problem becomes a pure integer programming (PIP) problem. According to this, the first objective function of the optimization problem can be obtained: Formula (12). In order to further ensure the quality of beneficiation products, when optimizing ore blending, the improvement of resource utilization must be another goal of ore blending. In the mining production process, the resource utilization index is an important index  
Figure 3: Text of neural network. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Mean** & **SD** \\ \hline ABC-BP & 76.0725 & 0.0309 \\ PSO-BP & 77.1132 & 0.0421 \\ NA-BP & 75.8654 & 0.0674 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison with other methods. reflecting the loss of mineral resources mining and the recovery level of valuable metals. The resource utilization index can be expressed as:  
\[Z_{l}=\frac{W_{I}}{M_{d}\times\theta_{d}\times\frac{1-\gamma_{h}}{1-\gamma_{p}} \times 100\%} \tag{9}\]  
where \(Z_{l}\) is the resource utilization rate; \(W_{I}\) is the amount of concentrate metal; \(M_{d}\) is the geological reserves; \(\theta_{d}\) is the geological grade; \(\gamma_{s}\) is the dilution rate; and \(\gamma_{p}\) is the loss rate. Dilution rate: In the process of ore mining, due to the mixing of waste rock and gangue, loss of high-grade ore, dissolution or loss of some useful components, etc., the grade of the mined ore is lower than the geological grade of the ore in the industrial reserves calculated before mining. This phenomenon is called ore dilution. Mining dilution rate is the ratio of the difference between the original ore's geological grade and the grade of the mined ore to the original ore's geological grade, and the percentage of industrial reserves lost to reimbursed industrial reserves. It is one of the important factors to determine the degree of utilization of mineral resources. The formula for calculating the dilution rate is:  
\[\gamma_{s}=\frac{\theta_{d}-\theta_{c}}{\theta_{d}}\times 100\% \tag{10}\]  
where \(\theta_{c}\) is the grade of the mined ore. When the ore dilution rate is high, the selected ore grade is low, the quality of the concentrate product decreases, and the enterprise's economic efficiency decreases. Loss rate: In the process of mining and production, industrial ores cannot be fully mined, released, and transported. The loss rate is the degree of industrial ore production loss expressed as a percentage. The calculation formula for the loss rate is:  
\[\gamma_{p}=\frac{M_{l}}{M_{c}}\times 100\% \tag{11}\]  
where \(M_{l}\) is the amount of ore lost, and \(M_{c}\) is the amount of ore mined. The amount of ore mined is the sum of the minerals mined in each mining area in the ore blending plan. In summary, a multi-objective optimization problem can be obtained. 1. _Performance Index:_ \[\min(\Big{|}J_{f}(X_{i})-\varepsilon\Big{|}),i=1,2,\cdots,m\] (12) \[\max Z_{l}(X_{i}),i=1,2,\cdots,m\] (13) where \(\varepsilon\) is the target value of concentrate recovery. 2. _Constraints:_ 1.