Robust control design of an activated sludge process

Georgieva, Pétia; Azevedo, S. Feyo de

doi:10.1002/(sici)1099-1239(199911)9:13<949::aid-rnc445>3.0.co;2-g

Cited by 21 publications

(7 citation statements)

References 11 publications

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“…[23,12,[24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42]. In particular, the diurnal periodic load to wastewater treatment plants are of major concern, as well as the additional transient complications caused by, for example, storm events, see the numerical simulations, experimental data and discussions in, for example, [44-51, 43, 52].…”

Section: Introductionmentioning

confidence: 99%

Operating charts for continuous sedimentation IV: limitations for control of dynamic behaviour

Diehl

2007

J Eng Math

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Abstract. The nonlinear behaviour of a one-dimensional idealized model of continuous sedimentation has been investigated in this series of papers. The model is a scalar hyperbolic conservation law with space-discontinuous flux function and point source. Parameters in the equation are the two input variables (concentration and flux) and the control variable (a volume flow). The most desired type of solution contains a large concentration discontinuity and is referred to as 'optimal operation'. Operating charts (concentration-flux diagrams) have proved to be a means for classifying the nonlinear behaviour. In this paper, some fundamental results on the dynamic behaviour are presented, which give information on the limitations of the range of the control variable. When this is used together with the previously introduced optimal control strategies for step inputs, the process can be controlled.

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Section: Introductionmentioning

confidence: 99%

Operating charts for continuous sedimentation IV: limitations for control of dynamic behaviour

Diehl

2007

J Eng Math

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“…Controlling the concentration of biomass in recycle flow is the best and optimal method for removal of organic matter in the wastewater. 22 Banaei et. al., (2013) in his work, has presented the impact of biomass concentration on the performance of full-scale WWTP with varying aeration rate.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, these two parameters are directly governed by aeration rate (since it governs the DO level in wastewater that is been treated) and recycle sludge (concentration and flow rate). Controlling the concentration of biomass in recycle flow is the best and optimal method for removal of organic matter in the wastewater 22 . Banaei et.…”

Section: Introductionmentioning

confidence: 99%

Robust decentralized proportional–integral controller design for an activated sludge process

Anchan

Rao

2020

Asia-Pacific J Chem Eng

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This paper presents the design of a decentralized proportional-integral (PI) controller for a wastewater treatment plant (WWTP). The aeration rate and the return recycle sludge rate are manipulated inputs to the WWTP process, while substrate and biomass concentration are considered as the output variables. The study is divided into two segments: A decentralized controller is designed based on the best pairing in the first segment, and in the second segment, a decoupler is developed to reduce the interactions. Decouplers are generally used to create independent loops in the multivariable control loops. Each decoupled subsystem is converted to first order plus time delay (FOPTD) model using a system identification toolbox to design independent diagonal controllers. The numerical simulations have been performed to evaluate the effectiveness of the presented methods. Furthermore, a robustness study has also been carried out by taking into account multiplicative input and output uncertainties, and it is found that the controller designed based on minimizing the integral of absolute error (IAE) criteria shows more robust.

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“…Finally, BSM2 represents a significant development of the BSM1 model at the level of the treatment plant, taking into account both the water line and the sludge line. In the field of wastewater treatment process control, the specialists have approached various control algorithms ranging from conventional control structures (PI, PID) to advanced ones (robust [19][20][21][22], predictive [23,24], adaptive [25], sliding-mode, optimal [26,27] etc.) and artificial-intelligence-based ones (fuzzy, neural, expert systems) [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Optimal-Setpoint-Based Control Strategy of a Wastewater Treatment Process

et al. 2020

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This paper presents an optimal-setpoint-based control strategy of a wastewater treatment process (WWTP). The treatment plant serves the city of Galati, located in Eastern Romania, a city with a population of 250,000 inhabitants. As the treatment plant includes several control loops (based upon PI controllers), an efficient operation means the establishing of an optimal operating point regardless of the pluviometric regime (DRY, RAIN and STORM) or transitions between regimes. This optimal operating point is given by the optimal setpoint set (setpoints of the dissolved oxygen concentration in the aerated tanks, setpoint of the nitrate concentration, external recirculation flow, sludge flow extracted from the primary clarifier and excess sludge flow from the secondary clarifier) of the treatment plant control loops. The control algorithm has two distinct parts: the first part consists of computing the optimal aforementioned setpoints, based on the mathematical model of the treatment plant developed in SIMBA. For optimization (performed with genetic algorithms) an aggregate performance criterion that takes into consideration the quality of the effluent, the cost of the wastewater treatment as well as the percentage exceeding of the main parameters of the treated water was used; the second part consists of computing the optimal setpoint set which will be further applied directly in the process based on the membership to the current operating regime. The computation of the membership degrees to the current operating regime was performed with a fuzzification block, based on the information about the inflow rate in the biological treatment plant. For simulations, three data files of the influent were created, aiming at determining the optimal setpoints in each operating regime, and a fourth one containing an influent scenario able to globally test the system operation. The obtained results showed the efficiency of the biological treatment, the effluent quality index being about ten times lower than that of the influent. Furthermore, the genetic algorithm used in optimization determines accurately enough the minimum value of the performance criterion in the case of each pluviometric regime, the lowest value of the performance criterion being obtained in DRY operating regime and the highest values in RAIN and STORM regimes. This is mainly due to the increase of the treatment cost and to small exceeding of the limits of several quality parameters such as chemical oxygen demand and ammonium concentration in the two regimes mentioned above. The fuzzification block aims to achieve a smooth transition from one operating regime to another, thus determining easier operating regimes of the treatment plant actuators and contributing to the increase of their life cycle.

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Robust control design of an activated sludge process

Cited by 21 publications

References 11 publications

Operating charts for continuous sedimentation IV: limitations for control of dynamic behaviour

Operating charts for continuous sedimentation IV: limitations for control of dynamic behaviour

Robust decentralized proportional–integral controller design for an activated sludge process

Optimal-Setpoint-Based Control Strategy of a Wastewater Treatment Process

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