Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks

Luo, Fei; Yu, Rong; Xu, Yuge; Li, Yan

doi:10.1109/fskd.2009.494

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…e sharp fluctuation of TN and COD cannot be ignored for this issue. For example, effluent COD mostly fluctuated within 10 to 60 mg/L in the study of Luo et al [23]. In contrast, the range is magnified to 3-335 mg/L in this study.…”

Section: Advantages Limitations and Recommendation For Futurecontrasting

confidence: 65%

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Employing Artificial Neural Networks to Predict the Performance of Domestic Sewage Treatment Terminals in the Rural Region

Lin

Luo

Zhang

et al. 2021

Mathematical Problems in Engineering

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Domestic sewage in rural regions is mainly treated by small-scale treatment terminals in China. The large quantities and high dispersion of these terminals render the chemical measurement of effluent to be a time and energy intensive work and further hinder the efficient surveillance of terminals’ performance. After a thorough investigation of 136 operating terminals, this study successfully employs two artificial neural network (ANN) models to predict effluent total nitrogen (TN) and COD (R2 both higher than 0.8) by setting some easily detectable parameters, e.g., pH and conductivity, as inputs. To prevent ANN models getting stuck on local optima and enhance the model performance, genetic algorithm (GA) and particle swarm optimization (PSO) are introduced into ANN, respectively. By comparison, ANN-PSO excels in modelling both TN and COD. The root mean square error (RMSE) and R2 of ANN-PSO in modelling TN are 9.14 and 0.90, respectively, in the training stage, and 11.54 and 0.90, respectively, in the validation stage. The RMSE and R2 of ANN-PSO in modelling COD are 22.10 and 0.90, respectively, in the training stage, and 26.57 and 0.85, respectively, in the validation stage. This is the first study to provide performance prediction models that are available for different terminals. Two established ANN-PSO models show great practical significance in monitoring huge amounts of terminals despite the slight sacrifice of models’ accuracy caused by the great heterogeneity of different terminals.

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Section: Advantages Limitations and Recommendation For Futurecontrasting

confidence: 65%

“…(2) Inputs contained some parameters that are difficult or costly to be measured. In some cases, influent TN even served as inputs for effluent TN prediction [23].…”

Section: Limitations Of Current Cases Applied On Modelling Thementioning

confidence: 99%

Employing Artificial Neural Networks to Predict the Performance of Domestic Sewage Treatment Terminals in the Rural Region

Lin

Luo

Zhang

et al. 2021

Mathematical Problems in Engineering

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“…Guan et al and Wang et al proposed the soft-sensing method for predicting the quality parameters of wastewater treatment [23,24]. Luo et al developed a soft computing approach based on the back propagation neural networks and fuzzy-rough sets to predict effluent NH 3 -N, COD, and total nitrogen (TN) concentration of a real WWTP [25]. The results showed that the prediction by this approach was better than the other traditional modeling approaches.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

Zou

Wang

2019

Mathematical Problems in Engineering

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Wastewater treatment plant (WWTP) is the energy-intensive industries. Energy is consumed at every stage of wastewater treatment. It is the main contributor to the costs of WWTP. Analysis and forecasting of energy consumption are critical to energy-saving. Many factors influence energy consumption. The relationship between energy consumption and wastewater is complex and challenging to identify. This article employed the fuzzy clustering method to categorize the sample data of WWTP and analyzed the relationship between energy consumption and the influence factors in different categories. The study found that energy efficiency in various categories was changed and the same influence factors in different types had different influence intensity. The Radial Basis Function (RBF) neural network was used to forecast energy consumption. The data from the complete set and categories was adopted to train and test the model. The results show that the RBF model using the date from the subset has better performance than the multivariable linear regression (MLR) model. The results of this study provided an essential theoretical basis for energy-saving in WWTP.

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Wastewater Treatment Plant Performance Prediction with Support Vector Machines

Ribeiro

Sanfins²,

Belo

2013

Advances in Data Mining. Applications and Theoretical Aspects

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Effluent Quality Prediction of Wastewater Treatment Plant Based on Fuzzy-Rough Sets and Artificial Neural Networks

Cited by 5 publications

References 17 publications

Employing Artificial Neural Networks to Predict the Performance of Domestic Sewage Treatment Terminals in the Rural Region

Employing Artificial Neural Networks to Predict the Performance of Domestic Sewage Treatment Terminals in the Rural Region

Analysis and Forecasting of the Energy Consumption in Wastewater Treatment Plant

Wastewater Treatment Plant Performance Prediction with Support Vector Machines

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