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A modified moving horizon estimation scheme for multi-timescale chemical processes

机译:多时程化学过程的改进的移动层估计方法

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Many chemical processes have timescale multiplicity arising from the coupling between different physico-chemical phenomena. A direct application of classical moving horizon estimation (MHE) to multi-timescale processes may require a long estimation horizon, leading to a high computational load. In this work, we explore a modified MHE scheme where output measurements are selectively chosen based on process dynamics to be used in the MHE optimization problem. This allows the MHE to cover measurements in the distant past up until the latest available measurements without significantly increasing the computational complexity. Simulation studies have shown that the proposed approach provides similar accuracy of state estimation compared to classical MHE with a significant reduction in computational time.
机译:许多化学过程由于不同的物理化学现象之间的耦合而具有时标多样性。将经典移动视界估计(MHE)直接应用于多时间尺度过程可能需要较长的估计视界,从而导致较高的计算负荷。在这项工作中,我们探索了一种改进的MHE方案,其中基于过程动力学有选择地选择输出测量,以用于MHE优化问题。这使得MHE可以覆盖遥远的测量值,直到最近的可用测量值,而不会显着增加计算复杂性。仿真研究表明,与经典的MHE相比,该方法可提供类似的状态估计精度,并显着减少了计算时间。

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