Model-order selection of output-error models - BSM1 as case study

Christian Wallin, J. Zambrano
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Abstract

Output-Error (OE) System Identification is used to estimate the nonlinear behavior of an activated sludge process (ASP) in a Wastewater Treatment Plant (WWTP). The aim is to identify dynamic models to reproduce the effect of different plant dynamics. How the dissolved oxygen concentration of the aerobic tank affect the effluent ammonia concentration and how the internal recirculation affect the nitrate concentration of the anoxic tank is studied. The best fit of the model is estimated by varying the model order through a trial-and-error approach. Three different scenarios are investigated: one Single-Input-SingleOutput (SISO) and two Multiple-Input-Multiple-Output (MIMO) structures. In the SISO scenario only the oxygen to the effluent ammonia dynamics is investigated. Then for both the MIMO scenarios the internal recirculation to nitrate concentration dynamics in the anoxic tank is included and in the last scenario the influent flow rate is also included. The approach is evaluated using the Benchmark Simulation Model no.1 (BSM1).
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输出误差模型的模型阶数选择——以BSM1为例
输出误差(OE)系统辨识用于估计污水处理厂(WWTP)中活性污泥过程(ASP)的非线性行为。目的是确定动态模型来再现不同植物动态的影响。研究了好氧池溶解氧浓度对出水氨氮浓度的影响以及内循环对缺氧池硝酸盐浓度的影响。通过试错法,通过改变模型阶数来估计模型的最佳拟合。研究了三种不同的场景:一种单输入单输出(SISO)结构和两种多输入多输出(MIMO)结构。在SISO情景中,只研究了氧对出水氨的动力学。然后,对于两种MIMO方案都包括缺氧池中硝酸盐浓度的内部再循环动态,并且在最后一种方案中还包括进水流速。采用基准仿真模型1对该方法进行了评价(BSM1)。
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