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本文用总体物质特性描述了工业浮选中尾矿流量的摸拟原理。而总体物质特性又为每个单体物质的化学、矿相分析和离散分布所决定。离散分布可以是非连续的,抑或是连续的,这要由实实验所观察到的动力学确定。模型是根据表征空气界面和连续矿浆相间总体物质固有分布特性的充气速率与平衡效应的动力学作用而建立的。该模型可以用来求解近似的一阶速率常数。充气速率用β系数摸拟.泡沫稳定性参数。和特定浮选槽的充气速率Q_A的乘积与α系数成比例。在不影响物质离散分类的情况下,根据浮选网络摸拟原理,便很容易确定具体的β系数了。因此,这种摸拟可以适应随充气速率和泡沫稳定性改变而改变的操作变数。显然,β系数能很好地描述一系列实验室试验的充气速率变化效应。但是;这种模型不能很好地适应那种随时间变化而泡沫稳定性降低的情况。浮选过程的动力学模型,对设计和实际操作都是很重要的。它可以为复杂的工业网络摸拟提供依据。原矿性质的变化、处理量的变化都会严重地影响网络的操作。工厂操作的变动,不易在生产前就能估计到。因此,在特定条件下,根据整套已知的参数,要求摸拟程序具有确定参数和预测工厂操作的双重功能。参数的确定,既可按在线也可按离线分批试验获得。现存模型通常以处理量改变及不变参数的假定来预测随时间变化的操作变化。此外,也可望预测操作行为的局部效应。
In this paper, we describe the simulation principle of tailings flow in industrial flotation with the overall material properties. The overall material properties are determined by the chemical, mineralogical analysis and discrete distribution of each monomer material. Discrete distributions can be discontinuous or continuous, as determined by the kinetics observed in real experiments. The model is based on the dynamical effect of the inflation rate and the equilibrium effect, which characterizes the intrinsic distribution of the total mass of the air interface and the continuous slurry phase. The model can be used to solve approximate first-order rate constants. Inflation rate is modeled by β coefficient. Foam stability parameters. And the aeration rate Q_A of a particular flotation cell is proportional to the alpha coefficient. Without affecting the discrete classification of matter, the specific beta coefficient can be easily determined based on the flotation network simulation principle. Therefore, this simulation accommodates operating variables that vary with inflation rate and foam stability. Obviously, the beta coefficient can well describe the effect of changing rate of inflation on a series of laboratory tests. However, this model does not adapt well to the situation where foam stability decreases over time. The kinetic model of the flotation process is important for both design and practical operation. It provides the basis for complex industrial network simulations. The nature of the ore changes, changes in the amount of treatment will seriously affect the operation of the network. Changes in factory operations, not easy to estimate before production. Therefore, under certain conditions, according to the set of known parameters, the simulation program is required to have the dual function of determining parameters and predicting plant operation. The parameters can be determined either online or offline. Existing models often predict changes in operation over time based on the assumption of handling volume changes and invariant parameters. In addition, it is also expected to predict the local effects of operational behavior.