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State estimation of wastewater treatment plants based on model approximation

机译:基于模型逼近的污水处理厂状态估计

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In this article, we consider state estimation of wastewater treatment plants based on model approximation. In particular, we consider a wastewater treatment plant described by the Benchmark Simulation Model No.1 which consists of a five-chamber reactor and a settler. We propose to use the proper orthogonal decomposition approach with re-identification of output equations to obtain a reduced-order model of the original system. Then, the reduced-order model is taken advantage of in state estimation. An approach on how to determine an appropriate minimum measurement set is also proposed based on degree of observability. A continuous-discrete extended Kalman filtering algorithm is used to design the estimator based on the reduced-order model. We show through extensive simulations under different weather conditions that the estimator based on the reduced-order model with re-identified output equations gives good state estimates of the actual process.
机译:在本文中,我们考虑基于模型逼近的污水处理厂状态估算。特别是,我们考虑了由基准模拟模型No.1描述的废水处理厂,该厂由一个五室反应器和一个沉降器组成。我们建议使用适当的正交分解方法对输出方程进行重新识别,以获得原始系统的降阶模型。然后,在状态估计中利用降阶模型。还基于可观察程度,提出了一种如何确定适当的最小测量集的方法。基于降阶模型,采用连续离散扩展卡尔曼滤波算法设计估计量。通过在不同天气条件下的大量模拟,我们表明,基于降阶模型的重新估计输出方程的估计量可以给出实际过程的良好状态估计。

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