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Parameter estimation procedure for complex non-linear systems: calibration of ASM No.1 for N-removal in a full-scale oxidation ditch

机译:复杂非线性系统的参数估计程序:ASM No.1的标定,用于满量程氧化沟中的脱氮

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When applied to large simulation models, the process of parameter estimation is also called calibration. Calibration of complex non-linear systems, such as activated sludge plants, is often not an easy task. On the one hand, manual calibration of such complex systems is usually time-consuming, and its results are often not reproducible. On the other hand, conventional automatic calibration methods are not always straightforward and often hampered by local minima problems. In this paper a new straightforward and automatic procedure, which is based on the response surface method (RSM) for selecting the best identifiable parameters, is proposed. In RSM, the process response (output) is related to the levels of the input variables in terms of a first- or second-order regression model. Usually, RSM is used to relate measured process output quantities to process conditions. However, in this paper RSM is used for selecting the dominant parameters, by evaluating parameters sensitivity in a predefined region. Good results obtained in calibration of A SM No.1 for N-removal in a full-scale oxidation ditch proved that the proposed procedure is successful and reliable. [References: 11]
机译:当应用于大型仿真模型时,参数估计的过程也称为校准。复杂的非线性系统(例如活性污泥厂)的校准通常并非易事。一方面,对这种复杂系统进行手动校准通常很耗时,而且其结果通常无法重现。另一方面,常规的自动校准方法并不总是那么简单,而且常常受到局部极小问题的困扰。在本文中,提出了一种基于响应面法(RSM)选择最佳可识别参数的简单,自动的新方法。在RSM中,根据一阶或二阶回归模型,过程响应(输出)与输入变量的级别有关。通常,RSM用于将测量的过程输出量与过程条件相关联。但是,在本文中,通过评估预定义区域中的参数灵敏度,RSM用于选择主导参数。在全规模氧化沟中对A SM No.1进行N去除的校准中获得的良好结果证明,该方法是成功且可靠的。 [参考:11]

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