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为准确评价工艺过程设计和优化所需要的数据并对生产指标进方准确预报，可以通过建立矿石性质以及工艺过程的模型并对它们进行模拟的方法来实现。地质统计方法已用于建立有价成分品位为变量的矿体模型，这些模型适用于储量估计、圈定矿体边界、开采工艺设计和模拟以及选厂入选原矿性质变化的评价。但矿体模型通常不能提供有关评价矿石性质的资料，而这些资料正是一个可靠的工艺模拟必不可少的。迄今为止，要想通过工艺模拟对生产指标做出预测需要从不同矿点采样以获得“代表性试样”。代表性试样的性质（如解离度、可选性、可磨性等）以及用这种矿样所做的工艺试验都视为“矿体的平均行为”。更为可靠的方法不仅需要从不同矿点采样，而且要通过试验从工艺角度确定试样的矿石类型，并考虑工艺特征参数。为建立工艺给矿模型，采用地质统计方法来确定各种参数的数值。因此可以根据矿体中可回收矿物的含量取代有用矿物的含量来预报生产指标；这样，工艺选择、设备选型、控制系统的设计以及对生产指标的预报更为准确可靠。
In order to accurately evaluate the data needed for the process design and optimization and accurately predict the progress of the production index, it can be achieved by establishing the model of ore properties and process and simulating them. Geostatistical methods have been used to establish orebody models with variable compositional variances as variables that are useful for estimating reserves, delimiting ore body boundaries, design and simulation of the mining process, and changes in the properties of the ore selected for processing. However, ore body models often do not provide information about the nature of the ore being evaluated, and such information is essential to a reliable process simulation. To date, predicting production metrics through process simulations requires sampling from different sites to obtain “representative samples.” The properties of the representative sample (eg, resolution, selectivity, grindability, etc.) and the process tests performed with this sample are considered “average ore body behavior.” A more reliable method requires not only sampling from different ore points, but also determining the ore type of the sample from a process perspective through experiments and taking into account the process characteristic parameters. To establish the process to mine model, the use of geological statistics to determine the value of various parameters. Therefore, the production index can be predicted according to the content of the recyclable mineral in the ore body instead of the content of the useful mineral. In this way, the selection of the process, the selection of the equipment, the design of the control system and the prediction of the production index are more accurate and reliable.