Prediction of activated sludge bulking based on a self-organizing RBF neural network

Han, Honggui; Qiao, Junfei

doi:10.1016/j.jprocont.2012.04.002

Cited by 63 publications

(38 citation statements)

References 55 publications

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“…However, activated sludge processes become complicated because of their changing physical characteristics and variability of the pollution load of raw wastewater [BARNETT et al 1995]. This clearly demonstrates the need for modelling and simulations, not only to describe the different phases and processes involved in the treatment process but also to make predictions under different management scenarios [HAN, QIAO 2012].…”

Section: Introductionmentioning

confidence: 99%

Dynamic simulation for wastewater treatment plants management: Case of Souk-Ahras region, north-eastern Algeria

Dairi

Djebbar

Hammar

et al. 2017

Journal of Water and Land Development

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Treatment performances of two wastewater treatment plants (WWTPs), located in North-Eastern Algeria (Souk-Ahras and Sedrata) were tested using ASM1 model.The model, to be considered as a decision tool for the appropriate management of activated sludge systems, served for the prediction of both WWTP behaviours under different operating conditions. In Sedrata WWTP the first management scenarios is based on an increase of inflow rate, taking into account a new transfer volume from a nearby zone. In a second scenerio, the ultimate flow of 40 000 m 3 ·d -1 is estimated. Regarding Souk-Ahras WWTP, three scenarios were tested. The first tested the impact of an increase of the extraction flow rate and yielded a reduction by 37% of sludge production. The second dealt with the management of the mass budget of substrata and biomass. Finally, the third application was devoted to the estimation of the plant ultimate capacity, estimated to be 60 000 m 3 ·d -1.

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Section: Introductionmentioning

confidence: 99%

Dynamic simulation for wastewater treatment plants management: Case of Souk-Ahras region, north-eastern Algeria

Dairi

Djebbar

Hammar

et al. 2017

Journal of Water and Land Development

View full text Add to dashboard Cite

show abstract

“…The review of the literature shows [10,18], that these analyzes are made by hybrid models which are usually a combination of self-organizing artificial neural network with another type of neural network (probabilistic, multi-layer perceptron, recurrent). However, the above-described approaches has its disadvantages, especially in case they have a small amount of input data or when the yields as a result of the classification is characterized by significant differences.…”

Section: Som (Self-organizing Neural Network)mentioning

confidence: 99%

“…In these models the first stage of calculating training set, using a suitable algorithm discriminatory type of method k -medium [8], c -medium [9], self-organizing neural networks [10], is divided into appropriate classes, within which they are made predictions based on independent models. In many cases [11,12], this approach can improve the ability of predictive mathematical model, but in a situation of extremely diverse number of data in each class or insufficient input data there may be problems at the stage of learning and testing statistical models.…”

Section: Introductionmentioning

confidence: 99%

Application of Selected Methods of Black Box for Modelling the Settleability Process in Wastewater Treatment Plant

Szeląg

Gawdzik

2017

Ecological Chemistry and Engineering S

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Abstract:The paper described how the results of measurements of inflow wastewater temperature in the chamber, a degree of external and internal recirculation in the biological-mechanical wastewater treatment plant (WWTP) in Cedzyna near Kielce, Poland, were used to make predictions of settleability of activated sludge. Three methods, namely: multivariate adaptive regression splines (MARS), random forests (RF) and modified random forests (RF + SOM) were employed to compute activated sludge settleability. The results of analysis indicate that modified random forests demonstrate the best predictive abilities.

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“…The activated sludge contains various micropopulation which can eliminate and defuse the harmful substances for the human and ecological environment. Generally, WWTP consists of four parts which are primary stage (physical treatment), secondary stage (biological treatment and secondary sedimentation tank), tertiary stage (advanced treatment) and sludge treatment, which are described in details as follows [9,38]:…”

Section: Wastewater Treatment Plant (Wwtp)mentioning

confidence: 99%

“…The biological process is mainly related to the activated sludge and it represents microbial growth, death, and nutrient consumption. These activated sludge models are used to approach enormous amount of biological processes which occurred in each bioreactor [38]. Overall, the modeling of the WWTP is still a complicated problem for the complex and changing process [16].…”

Section: Wastewater Treatment Plant (Wwtp)mentioning

confidence: 99%

Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

Qiao

2016

Water

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Abstract:The precision of soft measurement for biochemical oxygen demand (BOD) is always restricted due to various factors in the wastewater treatment plant (WWTP). To solve this problem, a new soft measurement modeling method based on chaos theory is proposed and is applied to BOD measurement in this paper. Phase space reconstruction (PSR) based on Takens embedding theorem is used to extract more information from the limited datasets of the chaotic system. The WWTP is first testified as a chaotic system by the correlation dimension (D), the largest Lyapunov exponents (λ 1 ), the Kolmogorov entropy (K) of the BOD and other water quality parameters time series. Multivariate chaotic time series modeling method with principal component analysis (PCA) and artificial neural network (ANN) is then adopted to estimate the value of the effluent BOD. Simulation results show that the proposed approach has higher accuracy and better prediction ability than the corresponding modeling approaches not based on chaos theory.

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Prediction of activated sludge bulking based on a self-organizing RBF neural network

Cited by 63 publications

References 55 publications

Dynamic simulation for wastewater treatment plants management: Case of Souk-Ahras region, north-eastern Algeria

Dynamic simulation for wastewater treatment plants management: Case of Souk-Ahras region, north-eastern Algeria

Application of Selected Methods of Black Box for Modelling the Settleability Process in Wastewater Treatment Plant

Soft Measurement Modeling Based on Chaos Theory for Biochemical Oxygen Demand (BOD)

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