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在选矿过程模拟中，综合运用下述两种方法，可以较可靠地预测生产指标，估算设计和生产过程优化所需的数据1）矿石特性的建模和模拟;2）选矿过程模型的建模和模拟。可以用地质统计方法描述矿体模型是众所周知的，其中研究的变量是有价组分的品位。这种模型运用于储量估算、地质统计划界、设计和模拟开采方法，此外，还适用于评价选矿厂人选原矿的变异性。但是，还没有标准化的方法可以把有些数据结合到矿体模型中去，以便评价矿石性质，及可靠地模拟选矿过程。到目前为止，为了预测选矿厂指标，所用的选矿过程模拟方法一般是从矿体的不同地点采取矿样，并制成所谓的“代表性矿样”。根据这种矿样，将矿石性质（如解离粒度、可选性、可磨性等）和选矿试验看成是“矿体的平均特性”，并视为常数。比较可靠的一种方法是从矿体不同地点采取矿样，进行试验，从选矿过程的角度确定矿样的特性（矿石模式），并使用综合参数（选矿过程特性）．这些参数值用地质统计学方法处理，以便建立一种模型，模拟人选矿石．这样，就可用回收的矿物量，而不用赋存的矿物量进行评价。此外，过程的选择、设备规格的确定、控制装置的设计以及指标的预测将更为精确可靠。
In the process of mineral processing simulation, the following two methods can be combined to predict the production index more reliably and estimate the data needed for the design and production process optimization. 1) Modeling and simulation of ore properties; 2) Modeling of the beneficiation process model And simulation. It is well-known that geological models can be used to describe ore body models, where the variables studied are the grades of the value components. This model is used for reserve estimation, geostatistical demarcation, design and simulation of mining methods, and is also suitable for assessing the variability of concentrates of beneficiation candidates. However, there is no standardized way to incorporate some data into the orebody model in order to evaluate the nature of the ore and to reliably model the beneficiation process. So far, in order to predict the beneficiation process, the beneficiation process simulation method used is to take ore samples from different locations of the ore body and make the so-called “representative ore samples”. Based on this mineral sample, the nature of the ore (eg particle size, selectivity, grindability, etc.) and dressing tests are considered “average characteristics of the ore body” and are treated as constants. One more reliable method is to take ore samples from different locations of the orebody and conduct tests to determine the characteristics of the ore sample (ore pattern) from the perspective of the beneficiation process and to use the integrated parameters (beneficiation process characteristics). These parameter values are processed geostatically to create a model that simulates the candidate ore. In this way, the amount of recovered minerals can be used instead of the amount of minerals present. In addition, the choice of process, equipment specifications, control device design, and forecasting of indicators will be more accurate and reliable.