# A Method of Ore Blending Based on the Quality of Benefication and Its Application in a Concentrator
## 2 Materials and Methods
### Constraints on Ore Blending  
In actual production, mining and mixing of minerals are restricted by the nature of mine products and production capacity [21]. Therefore, various indicators must be comprehensively considered when generating an ore blending plan. There are usually two classification methods for ore blending indicators, which are divided according to the work order and the ore's nature. According to the working order, the ore blending conditions can be divided into raw ore, ore blending indicators, and ore dressing products. According to the ore natures, it can be divided into ore's physical properties, ore's chemical properties, and quantity of ore. The first method is adopted in this research. Raw ore conditions include OG (Ore Grade), HQC (High-Quality Content), LQC (Low-Quality Content), MPV (Minimum Production Volume), and GR (Geological Reserves). Ore grade refers to the ratio of metal to total ore in the raw ore. In this study, it is the iron content in the mixed mineral product, which is a percentage. High-quality content refers to the proportion of material that is conducive to subsequent work in total ore. Low-quality content refers to the proportion of material that is detrimental to subsequent work in total ore. In this study, HQC is \(Fe_{2}O_{3}\) and \(Fe_{3}O_{4}\), LQC is \(FeCO_{3}\) and \(FeSiO_{3}\). The MPV refers to the minimum production volume at each ore mining site. The geological reserves refer to the maximum amount of each ore that can be produced. One blending indicators include the total amount of ore, the maximum and minimum values of mixed ore grades, the maximum and minimum values of high-quality material content, and the maximum and minimum values of total inferior ore content. These values are determined for the quality of the ore blending products, and their size is related to the use of the mixed ore. Therefore, the ore blending indicators need to be transferred to the production workshop when the production plan is determined. A useful ore blending product index can ensure to a certain extent that the quality of grinding products and beneficiation products meet the production standards, so it is also an essential condition that needs to be considered when ore blending to generate a plan. Under normal circumstances, the above are the restrictive conditions for ore blending production. The operator determines the appropriate ore blending plan according to the actual situation and stores the produced mixed ore in their respective warehouses, waiting for distribution. The open-pit targeted by this research is the own property of a concentrator. The ore mined can only be used as raw material for this concentrator. Therefore, while ensuring the quality of the blended ore products, it is also necessary to make the mixed ore suitable for the beneficiation plant's production. In order to achieve this goal, this research adds a condition to the ore blending scheme to ensure the quality of the beneficiation products. It is the difference between this ore blending method and the conventional ore blending method. The last condition on ore blending is the quality of the beneficiation products. Predict the quality of the beneficiation products according to the mixed ore's nature and guide the ore blending plan's adjustment to form a closed-loop control. There are many influencing factors in the production process, from ore blending to beneficiation. Every time the ore blending scheme is adjusted, it takes several hours to know the adjustment effect through laboratory tests. It can be said that this is a nonlinear, extensive lag system, so this research considers this to be a black box system, and a neural network is used to build a predictive model. Concentrate grade and concentrate recovery rate are the key indicators that determine the quality of beneficiation products. Not only that, they can also evaluate the metallurgical efficiency of the beneficiation process.