Modeling Carbon and Nitrogen Removal in an Industrial Wastewater Treatment Plant Using an Adaptive Network-Based Fuzzy Inference System

Wastewater treatment plant involves highly complex and uncertain processes, which are quite difficult to forecast. However, smooth and efficient operation of the treatment plant depends on an appropriate model capable of describing accurately the dynamic nature of the system. Most of the existing models were applied to industrial wastewater treatment plants. Therefore, this paper proposed an ANFIS model for carbon and nitrogen removal in the Bunus regional sewage wastewater treatment plant, Kuala Lumpur, Malaysia. For comparison, feed-forward neural network is used. Simulation results revealed that the ANFIS model demonstrated slightly better prediction capability in all the considered variables, chemical oxygen demand (COD), suspended solids (SS) and ammonium nitrogen (NH4-N) as compared to the FFNN model, thus proving that the proposed ANFIS model is reliable and useful to the wastewater treatment plant.

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Research on the enhancement of biological nitrogen removal at low temperatures from ammonium-rich wastewater by the bio-electrocoagulation technology in lab-scale systems, pilot-scale systems and a full-scale industrial wastewater treatment plant

Water Research ◽

10.1016/j.watres.2018.04.036 ◽

2018 ◽

Vol 140 ◽

pp. 77-89 ◽

Cited By ~ 16

Author(s):

Liang Li ◽

Guangsheng Qian ◽

Linlin Ye ◽

Xiaomin Hu ◽

Xin Yu ◽

...

Keyword(s):

Wastewater Treatment ◽

Nitrogen Removal ◽

Wastewater Treatment Plant ◽

Low Temperatures ◽

Industrial Wastewater ◽

Treatment Plant ◽

Pilot Scale ◽

Biological Nitrogen Removal ◽

Industrial Wastewater Treatment ◽

Biological Nitrogen

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Modelling of COD removal in a biological wastewater treatment plant using adaptive neuro-fuzzy inference system and artificial neural network

Water Science & Technology ◽

10.2166/wst.2009.482 ◽

2009 ◽

Vol 60 (6) ◽

pp. 1475-1487 ◽

Cited By ~ 16

Author(s):

G. Civelekoglu ◽

N. O. Yigit ◽

E. Diamadopoulos ◽

M. Kitis

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Wastewater Treatment ◽

Organic Carbon ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Treatment Plant ◽

Biological Wastewater Treatment ◽

Inference System ◽

Neuro Fuzzy

This work evaluated artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) modelling methods to estimate organic carbon removal using the correlation among the past information of influent and effluent parameters in a full-scale aerobic biological wastewater treatment plant. Model development focused on providing an adaptive, useful, practical and alternative methodology for modelling of organic carbon removal. For both models, measured and predicted effluent COD concentrations were strongly correlated with determination coefficients over 0.96. The errors associated with the prediction of effluent COD by the ANFIS modelling appeared to be within the error range of analytical measurements. The results overall indicated that the ANFIS modelling approach may be suitable to describe the relationship between wastewater quality parameters and may have application potential for performance prediction and control of aerobic biological processes in wastewater treatment plants.

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