State estimation for large-scale wastewater treatment plants

被引：56

作者：

Busch, Jan ^{[1
]}

Elixmann, David ^{[1
]}

Kuehl, Peter ^{[2
]}

Gerkens, Carine ^{[3
]}

Schloeder, Johannes P. ^{[2
]}

Bock, Hans G. ^{[2
]}

Marquardt, Wolfgang ^{[1
]}

机构：

[1] Rhein Westfal TH Aachen, AVT Proc Syst Engn, Aachen, Germany

[2] Heidelberg Univ, IWR, Heidelberg, Germany

[3] Univ Liege, LASSC, B-4000 Liege, Belgium

来源：

WATER RESEARCH | 2013年 / 47卷 / 13期

关键词：

State estimation; Moving horizon estimation; EKF; Wastewater treatment; ASM; BSM1; ACTIVATED-SLUDGE PROCESS; PARAMETER-ESTIMATION; KALMAN FILTER; SYSTEMS; IDENTIFICATION; COEFFICIENTS; MODEL;

D O I：

10.1016/j.watres.2013.04.007

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Many relevant process states in wastewater treatment are not measurable, or their measurements are subject to considerable uncertainty. This poses a serious problem for process monitoring and control. Model-based state estimation can provide estimates of the unknown states and increase the reliability of measurements. In this paper, an integrated approach is presented for the optimization-based sensor network design and the estimation problem. Using the ASM1 model in the reference scenario BSM1, a cost-optimal sensor network is designed and the prominent estimators EKF and MHE are evaluated. Very good estimation results for the system comprising 78 states are found requiring sensor networks of only moderate complexity. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：4774 / 4787

页数：14

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