# A Method of Ore Blending Based on the Quality of Benefication and Its Application in a Concentrator
## 4 Conclusions  
The current common iron ore blending methods only refer to the mixed ore indicators and the original ore's nature, but ignore the impact of the mixed ore properties on the beneficiation production. They often regard ore blending and beneficiation as two separate processes, rather than using the former as an auxiliary link to the latter to ensure the quality of the beneficiation products. This has led to a disconnect in the whole process of beneficiation production. This paper proposes a method of ore blending based on the quality of beneficiation.  
This method uses the ABC-BP neural network to fit the blended product's properties and the total concentrate recovery rate and establishes a prediction model. The results show that the improved BP neural network based on the ABC algorithm has a good application effect. This research also proposes not only to increase the recovery rate of beneficiation as the goal, but also to focus on long-term development, and to improve the resource utilization rate of the mine as another goal. In this way, the mining farm and the concentrator obtained the linkage through the ore grade, which can achieve a win-win effect. Based on this, a multi-objective optimized ore blending model was established, and a reasonable ore blending plan could obtained after calculation. Finally, a set of ore blending scheme generation system was developed, and it operated stably in a concentrator.  
Scholars should continue to collect data on mixed ore and beneficiation concentrates and establish a more comprehensive concentrate quality model in further research. This is because the accuracy of the objective function in the multi-objective optimization ore blending model determines the ore blending scheme's reliability. Moreover, the constraint conditions for optimizing ore blending should keep pace with the times. Taking this research as an example, when the ODI calculation rules are changed, the operator must actively change the constraint conditions for ore blending. In future research, a system linked to an economic benefit calculation should be developed to further reduce manual intervention.  
Conceptualization, B.L. and D.Z.; methodology, D.Z.; software, D.Z.; validation, B.L., D.Z. and X.G.; formal analysis, B.L. and D.Z.; investigation, B.L. and D.Z.; resources, X.G.; data curation, D.Z.; writing--original draft preparation, D.Z.; writing--review and editing, B.L. and D.Z.; visualization, B.L. and D.Z.; supervision, B.L. and D.Z.; project administration, X.G. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & **Mining** & **Mining** & **Mining** & **Mining** & **Mining** & **Mining** & **Mining** & **Mining** & **Mining** & **Mixed Ore** & **Profit** \\  & **No.1** & **No.2** & **No.3** & **No.4** & **No.5** & **No.6** & **No.7** & **No.8** & **No.9** & **Grade (\%)** & **(CNY)** \\ \hline Method 1 & 45 & 20 & 0 & 0 & 15 & 6 & 14 & 2 & 5 & 54.40 & 56.41 \\ Method 2 & 45 & 0 & 20 & 0 & 15 & 6 & 14 & 2 & 5 & 54.63 & 56.09 \\ Method 3 & 45 & 20 & 0 & 15 & 0 & 6 & 15 & 2 & 5 & 53.83 & 69.35 \\ Method 4 & 45 & 0 & 20 & 15 & 0 & 6 & 15 & 2 & 5 & 54.04 & 68.92 \\ This method & 35 & 27 & 1 & 13 & 5 & 6 & 13 & 2 & 4 & 54.21 & 80.27 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Ore blending scheme in September.  
Funding: This work was supported by the National Natural Science Foundation of China (No. 61573088). Conflicts of Interest: The authors declare no conflict of interest.