Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/7629
Title: Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression
Authors: Araromi, Dauda Olurotimi
Majekodunmi, Olukayode Titus
Adeniran, Jamiu Adetayo
Salawudeen, Taofeeq Olalekan
Majekodunmi, Olukayode Titus
Izmir Institute of Technology. Chemical Engineering
Keywords: Fuzzy exhaustive search
ANFIS
GLM regression
LASSO regularization
Predictive models
Wastewater treatment process
Issue Date: Sep-2018
Publisher: Springer Verlag
Source: Araromi, D. O., Majekodunmi, O. T., Adeniran, J. A., and Salawudeen, T. O. (2018). Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression. Environmental Monitoring and Assessment, 190(9). doi:10.1007/s10661-018-6878-x
Abstract: 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.
URI: https://doi.org/10.1007/s10661-018-6878-x
https://hdl.handle.net/11147/7629
ISSN: 0167-6369
0167-6369
Appears in Collections:Chemical Engineering / Kimya Mühendisliği
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Sürdürülebilir Yeşil Kampüs Koleksiyonu / Sustainable Green Campus Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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