磨矿分级过程的混合智能建模与仿真

Hybrid Intelligent Modeling and Simulation for Ore Grinding and Classification Process

针对具有非线性、多变量、参数时变、边界条件波动等综合复杂特性的磨矿分级过程,基于物料平衡理论建立球磨机与泵池的动态模型,结合机理模型与经验知识建立有用功率TSK(Takagi-Sugeno-Kang)模型来确定粒度选择函数,给出旋流器分级经验模型,并基于径向基网络(RBFN)计算溢流浓度和对溢流粒度分布误差进行补偿,研制了可进行磨矿分级过程动态仿真的混合智能模型.用某铁矿选矿厂二段磨矿闭路实际生产数据进行仿真实验,在旋流器给矿控制量和新给入矿浆流量、浓度、粒度波动下,模型的仿真结果与磨机有用功率实际值变化趋势相同.

Modeling the ore grinding and classification proces.s is very important to the optimization of production index. A hybrid intelligent model is thus developed for the simulation of ore grinding and classification process according to its compositive complexity including nonlinearity, multivariable, time-varying parameters and boundary condition fluctuation. This dynamic model is based on the population balance models for ball mill and sump, TSK （Takagi- Sugeno-Kang） model for net mill power draft, empirical cyclone model together with the RBF networks to compensate for the estimation error of overflow density and particle size distribution. With the actual process data from a grinding circuit of a concentration plant, the simulation results of this hybrid intelligent model have the same dynamic characteristics during the variation in fresh slurry feed velocity, density, particle size distribution and cyclone feed manipulating variables.