Nonlinear system identification of the dissolved oxygen to effluent ammonia dynamics in an activated sludge process

Chistiakova, Tatiana; Mattsson, Per; Carlsson, Bengt; Wigren, Torbjörn

doi:10.1016/j.ifacol.2017.08.365

Cited by 4 publications

(7 citation statements)

References 13 publications

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“…Empirical models have been used for performing the system identification of ASPs, where a pre-defined model order is assumed, see some examples in Vrečko et al (2004). System identification of simplified ASPs has been carried out by Chistiakova et al (2017), dealing with linear and non-linear models.…”

Section: Discussionmentioning

confidence: 99%

“…From the point of view of control, a system identification of the process is important, mainly because it will improve the control performance of the process, which is typically formed by PI controllers. Another reason is that the system identification can be used to carry out stability analysis of the closed-loop system (Chistiakova et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…The definition of such a model order is still empirical. Typically, a certain model order is assumed, see for example Chistiakova et al (2017) where an Output-Error Figure 1. 1 basic layout where the influent water first passes 2 anoxic tanks followed by 3 aerated tanks and then passes a settler before being released.…”

Section: Introductionmentioning

confidence: 99%

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

Wallin¹,

Zambrano²

2018

Linköping Electronic Conference Proceedings

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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-Single-Output (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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wallin¹,

Zambrano²

2018

Linköping Electronic Conference Proceedings

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“…On the other hand, the synthesis of an advanced control law of the DOC is required in order to not only provide the proper condition for the microorganisms' activities but also, for reducing the high consumption energy of blowers. In this context, our main objective is to control the process during the aerobic phase in order to maintain the DOC level less than 2g=m 3 (Chistiakova et al, 2017), and thereafter to ensure the respect of the standard weastwater reject norm. The estimated states and unknown parameter obtained from the HAO are utilized by a predictive output feedback controller which guarantees that the DOC is less than a predefined reference.…”

Section: Problem Statementmentioning

confidence: 99%

“…Thanks to its efficiency and its great effect on the energy consumption, controlling the DOC is crucial for the considered process. In this field, Chistiakova et al (2017) have developed an adaptive control law of DOC to settle the effluent ammonia in the AASP. In order to control the DOC flow a neural network controller based on an adaptive proportional-integral-derivative algorithm has been presented in Du et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Hybrid adaptive observer-based output feedback predictive control for the alternating activated sludge process

Boudagga

Dimassi

Said

et al. 2021

Transactions of the Institute of Measurement and Control

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In this paper, an adaptive observer-based predictive controller is designed for the alternating activated sludge process which represents a nonlinear hybrid system. Precisely, our objective is to control the dissolved oxygen concentration during the aerobic phase. First, a hybrid adaptive observer is designed to estimate conjointly the unmeasured state (the ammonia concentration) and the unknown parameter (the coefficient of performance of heterotrophic biomass). Then the estimated signals are used in the output feedback predictive control law. The convergence of the state estimation, parameter reconstruction and tracking control errors are established through a Lyapunov stability analysis. Numerical simulations are dedicated to highlight the good performances of the developed output feedback control approach.

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