Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression

  • Dauda Olurotimi Araromi
  • Olukayode Titus MajekodunmiEmail author
  • Jamiu Adetayo Adeniran
  • Taofeeq Olalekan Salawudeen


In this paper, nonlinear system identification of the activated sludge process in an industrial wastewater treatment plant was completed using adaptive neuro-fuzzy inference system (ANFIS) and generalized linear model (GLM) regression. Predictive models of the effluent chemical and 5-day biochemical oxygen demands were developed from measured past inputs and outputs. From a set of candidates, least absolute shrinkage and selection operator (LASSO), and a fuzzy brute-force search were utilized in selecting the best combination of regressors for the GLMs and ANFIS models respectively. Root mean square error (RMSE) and Pearson’s correlation coefficient (R-value) served as metrics in assessing the predicting performance of the models. Contrasted with the GLM predictions, the obtained modeling results show that the ANFIS models provide better predictions of the studied effluent variables. The results of the empirical search for the dominant regressors indicate the models have an enormous potential in the estimation of the time lag before a desired effluent quality can be realized, and preempting process disturbances. Hence, the models can be used in developing a software tool that will facilitate the effective management of the treatment operation.


Wastewater treatment process modeling Predictive models ANFIS Fuzzy exhaustive search GLM regression LASSO regularization 



The authors would like to acknowledge the support of Seven-Up Bottling Company, Lagos, Nigeria for releasing the data used.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Chemical EngineeringLadoke Akintola University of TechnologyOgbomosoNigeria
  2. 2.Department of Chemical EngineeringIzmir Institute of TechnologyIzmirTurkey
  3. 3.Department of Chemical EngineeringUniversity of IlorinIlorinNigeria

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