This paper investigates an observable dynamic model of froth flotation circuits aimed at online state and parameter estimation and model-based control. The aim is to estimate the model states and parameters online from industrial data. However, in light of limitations in the plant data, additional model analysis is conducted. It is shown that without online compositional measurements, only the states and parameters of a reduced model can be estimated online. The reduced model lumps all recovery mechanisms into a single empirical equation. The reduced model is used to develop a moving horizon estimator (MHE) which is implemented on the industrial data. The state and parameter estimates from the MHE are used to evaluate the model prediction accuracy over a receding control horizon as would be done in model predictive control (MPC). Given the uncertainty of the available data, unmeasured disturbances and missing online measurements, the estimation and prediction results are reasonably accurate, at least in a qualitative sense. If accurate and reliable online measurements are available for estimation, the reduced model shows potential to be used for long-term model-based supervisory control of a flotation circuit.

中文翻译：

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基于工业数据的动态泡沫浮选模型的状态和参数估计


本文研究了泡沫浮选回路的可观察动态模型，旨在在线状态和参数估计以及基于模型的控制。目的是从工业数据中在线估计模型状态和参数。然而，鉴于被控对象数据的局限性，进行了额外的模型分析。结果表明，在没有在线成分测量的情况下，只能在线估计简化模型的状态和参数。简化模型将所有恢复机制归结为一个经验方程。简化模型用于开发在工业数据上实现的移动水平估计器 （MHE）。MHE 的状态和参数估计值用于评估模型预测在后退控制层位上的预测准确性，就像在模型预测控制 （MPC） 中所做的那样。考虑到可用数据的不确定性、未测量的干扰和缺失的在线测量，估计和预测结果是相当准确的，至少在定性意义上是这样。如果准确可靠的在线测量可用于估计，则简化的模型显示出用于浮选回路的长期基于模型的监督控制的潜力。