A dynamic flotation model incorporating fundamental and phenomenological relationships, information from froth images and steady-state models is described. Model outputs correspond with online measurements commonly available on flotation circuits, and the model parameters are estimated from industrial data. Simulation results are presented, highlighting important non-linearities that need to be taken into account for optimal flotation operation. Observability and controllability analyses are performed, proving that key flotation parameters can theoretically be estimated from online process measurements, and that the set of modelled inputs can control all the model outputs. This model can be used in advanced model-based control and optimisation applications. The ability to estimate key flotation parameters opens up opportunities for improved optimisation of operating variables such as aeration rates, froth depth and the reagent recipe.

描述了结合基本和现象学关系，泡沫图像信息和稳态模型的动态浮选模型。模型输出与浮选回路上通常可用的在线测量相对应，并且模型参数是根据工业数据估算的。给出了仿真结果，突出显示了最佳浮选操作需要考虑的重要非线性。进行了可观察性和可控性分析，证明了理论上可以从在线过程测量中估算关键浮选参数，并且建模输入集可以控制所有模型输出。该模型可用于基于模型的高级控制和优化应用程序。