Feedforward-feedback control of an activated sludge process: a simulation study

Vrečko, Darko; Hvala, Nadja; Carlsson, Bengt

doi:10.2166/wst.2003.0623

Cited by 43 publications

(23 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…3 and is given by µ (1) (Z) = min{µ(X1|A1), µ(X1|A1)} This membership function µ (1) (Z) can be explained as how big part we should take into consider for the fuzzy set C1. And similarly, we can reach the other membership function for the conclusion by rule (2). And as the present situation (X is X1 and Y is Y1) is affected by both rule (1) and rule (2), thus the final decision should be a combination of both membership funcion, as showed in Fig.3.…”

Section: Plant Performance Criteriamentioning

confidence: 77%

“…Similar to the original BSM1, the effluent quality index (EQI) for BSM1-P was included P, as calculated by Eqs. (1) and (2).…”

Section: Plant Performance Criteriamentioning

confidence: 99%

“…Different control algorithms for WWTPs have been introduced over the years. For instance, sufficient nitrification can be maintained by applying a constant aeration flow rate, by controlling the dissolved oxygen (DO) level at a pre-selected set-point or by using a variable DO set-point controller based on ammonia concentration in the last aerobic reactor of the plant [1,2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

PI and Fuzzy Control for P-removal in Wastewater Treatment Plant

Xu¹,

Vilanova²

2015

INT J COMPUT COMMUN

View full text Add to dashboard Cite

Due to the complex and non linear character, wastewater treatment process is difficult to be controlled. The demand for removing the pollutant, especially for nitrogen (N) and phosphorus (P), as well as reducing the cost of wastewater treatment plant is an important research theme recently. Thus, in this paper, the benchmark proposed default control strategy and 10 additional control strategies are applied on the combined biological P and N removal Benchmark Simulation Model No.1 (BSM1-P). In addition, according to the results of applying PI controllers, as usual, we also chose the group with the better performance, as well as the default control strategy, to replace the PI controllers with fuzzy controllers. In this way, it can be seen that in all cases the quality of effluent of the controlled process could be improved in some degree; and the fuzzy controllers get a better phosphorus removal.

show abstract

Section: Plant Performance Criteriamentioning

confidence: 77%

“…Similar to the original BSM1, the effluent quality index (EQI) for BSM1-P was included P, as calculated by Eqs. (1) and (2).…”

Section: Plant Performance Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

PI and Fuzzy Control for P-removal in Wastewater Treatment Plant

Xu¹,

Vilanova²

2015

INT J COMPUT COMMUN

View full text Add to dashboard Cite

show abstract

“…In the literature, there are several papers working on modeling of Wastewater Treatment Plants (WWTPs) [4][5][6][7]. In this One of the conclusions was that the literature demonstrates that to manage WWTPs in a sustainable way, the indicators, including operational costs, net energy and multiple environmental performance measures, including GHG, have to be considered.…”

Section: Introductionmentioning

confidence: 99%

Joint Environmental and Economical Analysis of Wastewater Treatment Plants Control Strategies: A Benchmark Scenario Analysis

2016

View full text Add to dashboard Cite

Abstract:In this paper, a joint environmental and economic analysis of different Wastewater Treatment Plant (WWTP) control strategies is carried out. The assessment is based on the application of the Life Cycle Assessment (LCA) as a method to evaluate the environmental impact and the Benchmark Simulation Model No. 1 (BSM1). The BSM1 is taken as the benchmark scenario used to implement the control strategies. The Effluent Quality Index (EQI) and the Overall Cost Index (OCI) are two indicators provided by BSM1 and used to evaluate the plant's performance from the effluent quality and the economic points of view, respectively. This work conducts a combined analysis and assessment of ten different control strategies defined to operate a wastewater treatment plant. This analysis includes the usual economic and performance indexes provided by BSM1 joined with the LCA analysis that determines the environmental impact linked to each one of the considered control strategies. It is shown how to get an overall evaluation of the environmental effects by using a normalized graphical representation that can be easily used to compare control strategies from the environmental impact point of view. The use of only the BSM1 indexes provides an assessment that leads to a clustering of control strategies according to the cost/quality tradeoff they show. Therefore, regarding the cost/quality tradeoff, all strategies in the same group are almost equal and do not provide an indication on how to proceed in order to select the appropriate one. It is therefore shown how the fact of adding a new, complementary, evaluation (LCA based) allows either to reinforce a decision that could be taken solely on the basis of the EQI/OCI tradeoff or to select one control strategy among the others.

show abstract

“…Further control algorithms are focused also on nitrate control [6], [7], [8] or extend the control strategy on whole plant and to the sewer system using heretical model predictive control [1]. The methods approached are also various: Proportional Integral and Derivative (PID) control, or feedforward integrated with feedback control [9], model predictive control (MPC) [4], [1], [10], [11], optimal control [13], fuzzy control [12] and nonlinear control. Experimental validation of those control strategies are few [14] due to the difficulty of applying those control algorithms on operational and running plants and also due to the lack of some parameters measurement.…”

Section: Introductionmentioning

confidence: 99%

EPSAC for wastewater treatment process (BSM1)

Crisan

Nașcu

Volcke

et al. 2015

2015 19th International Conference on System Theory, Control and Computing (ICSTCC)

View full text Add to dashboard Cite

Abstract-Predictive control is one of the most spread advanced control algorithms in industrial application field. Extended Prediction Self-Adaptive Control (EPSAC) is a part of this family of algorithms and is suitable for wastewater treatment plants control. The main goal of those industrial processes is to fulfil effluent water quality legal provisions with minimal energy consumption. In order to achieve this goal EPSAC control methodology has been applied to the wastewater treatment process. Benchmark Simulation Model No. 1 (BSM1) has been used to simulate the process dynamics. Two types of control strategies were implemented and tested: predictive control without taking into account measured disturbances and predictive control with feedforward. Feedforward control with two measured disturbances (the influent flow rate and ammonium concentration) has been tested.

show abstract

Feedforward-feedback control of an activated sludge process: a simulation study

Cited by 43 publications

References 13 publications

PI and Fuzzy Control for P-removal in Wastewater Treatment Plant

PI and Fuzzy Control for P-removal in Wastewater Treatment Plant

Joint Environmental and Economical Analysis of Wastewater Treatment Plants Control Strategies: A Benchmark Scenario Analysis

EPSAC for wastewater treatment process (BSM1)

Contact Info

Product

Resources

About