Fuzzy model-based predictive control of dissolved oxygen in activated sludge processes

Yang, Tao; Qiu, Wei; Ma, You; Chadli, Mohammed; Zhang, Lixian

doi:10.1016/j.neucom.2014.01.025

Cited by 80 publications

(29 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed PC-CR needs to be compared with baseline methods concerning above three metrics. We select several existing data-driven prediction methods for WTR: CNN, 46 LSTM, 47 FNN, 35 WFNN. 41 CNN and LSTM respectively denotes standard CNN model and standard LSTM model.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…In order to enforce ability of inference, some researchers also investigated coupling of neural network and fuzzy logic which is an effective mathematical reasoning tool. [35][36][37][38][39] For instance, Yang et al 35 proposed a fuzzy neural network-based predictive control mechanism for WTP and proved its superiority through simulation experiments. Ruan et al 36 introduced fuzzy neural network model into an anaerobic digestion system, and evaluated the performance of such model in predicting WTR.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Guo

Wang

et al. 2020

RSC Adv.

View full text Add to dashboard Cite

show abstract

Section: Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Guo

Wang

et al. 2020

RSC Adv.

View full text Add to dashboard Cite

show abstract

“…In other words, wastewater effluent quality depends greatly on the level of dissolved oxygen. Additionally, effective control of the dissolved oxygen level could reduce the operational cost of the wastewater treatment [10]. Therefore, the control of dissolved oxygen concentration is important, effective, and widely studied in WWTPS.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, all kinds of disturbances existing will degrade the performance of the closed-loop systems. In the last decades, various control algorithms have been proposed to control the dissolved oxygen, such as model predictive control (MPC) [7,10,15,16], fuzzy control [6,17], and neural network control [8,9,18,19]. The MPC has been widely applied in industrial processes, especially the fruitful linear MPC business software packages.…”

Section: Introductionmentioning

confidence: 99%

Linear Active Disturbance Rejection Control of Dissolved Oxygen Concentration Based on Benchmark Simulation Model Number 1

Wang

Wei

2015

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In wastewater treatment plants (WWTPs), the dissolved oxygen is the key variable to be controlled in bioreactors. In this paper, linear active disturbance rejection control (LADRC) is utilized to track the dissolved oxygen concentration based on benchmark simulation model number 1 (BSM1). Optimal LADRC parameters tuning approach for wastewater treatment processes is obtained by analyzing and simulations on BSM1. Moreover, by analyzing the estimation capacity of linear extended state observer (LESO) in the control of dissolved oxygen, the parameter range of LESO is acquired, which is a valuable guidance for parameter tuning in simulation and even in practice. The simulation results show that LADRC can overcome the disturbance existing in the control of wastewater and improve the tracking accuracy of dissolved oxygen. LADRC provides another practical solution to the control of WWTPs.

show abstract

“…Activated Sludge Models (ASMs) are widely used for the design, control and optimization of suspended biological treatment processes in municipal wastewater treatment plants (WWTPs) (Han et al 2014, Yang et al 2014, Busch et al 2013. Optimization of activated sludge processes can substantially reduce the capital and operational costs of WWTP both in design and operation stages, and to find out cost-effective improvement strategies for the existing plants.…”

Section: Introductionmentioning

confidence: 99%

Inverse Modeling of Nitrification-Denitrification Processes: A Case Study on the Blue Plains Wastewater Treatment Plant in Washington, DC.

Alikhani¹,

Takács²,

Omari³

et al. 2014

proc water environ fed

View full text Add to dashboard Cite

Evaluating the uncertainty associated with the predictions of Activated Sludge Models (ASMs) is essential in designing and optimization of biological wastewater treatment systems. The sources of ASM model prediction uncertainties can be classified into influent and environmental factor uncertainties, parameter uncertainty and epistemic uncertainty due to model abstraction. In this study, observed data obtained from nitrification-denitrification processes at the Blue Plains advanced wastewater treatment plant is used to quantifying the uncertainty associated with ASM parameter estimation. The posterior distributions obtained for parameters are then used in a Monte Carlo simulation to assess the uncertainties of predicted effluent constituents expressed as the 95% credible intervals of effluent concentrations. Using the outcome of the parameter estimation step, two scenarios of methanol loading scheme including one based on flow-rate (baseline) and the second one based on total nitrogen in the influent were evaluated. The treatment performance of the system was quantified based on the probability distributions of effluent violation with respect to certain water quality standards. The results show that methanol loading based on influent flow-rate is less effective compared to determining methanol loading based on total nitrogen loading in the influent.

show abstract

Fuzzy model-based predictive control of dissolved oxygen in activated sludge processes

Cited by 80 publications

References 31 publications

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Data-driven prediction and control of wastewater treatment process through the combination of convolutional neural network and recurrent neural network

Linear Active Disturbance Rejection Control of Dissolved Oxygen Concentration Based on Benchmark Simulation Model Number 1

Inverse Modeling of Nitrification-Denitrification Processes: A Case Study on the Blue Plains Wastewater Treatment Plant in Washington, DC.

Contact Info

Product

Resources

About