Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression
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.
KeywordsWastewater 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.
- Dupuit, E., Pouet, M. F., Thomas, O., & Bourgois, J. (2007). Decision support methodology using rule-based reasoning coupled to non-parametric measurement for industrial wastewater network management. Environmental Modelling & Software, 22, 1153–1163. https://doi.org/10.1016/j.envsoft.2006.05.025.CrossRefGoogle Scholar
- Dürrenmatt, D. J., & Gujer, W. (2012). Data-driven modeling approaches to support wastewater treatment plant operation. Environmental Modelling & Software. https://doi.org/10.1016/j.envsoft.2011.11.007.
- Hardin, J.W. & Hilbe, J.M. (2007). Generalized linear models and extensions. Texas: Stata Press.Google Scholar
- Heddam, S., Lamda, H., & Filali, S. (2016). Predicting effluent biochemical oxygen demand in a wastewater treatment plant using generalized regression neural network based approach: a comparative study. Environmental Processes, 3, 153–165. https://doi.org/10.1007/s40710-016-0129-3.CrossRefGoogle Scholar
- Karray, F. O., & De Silva, C. W. (2004). Soft computing and intelligent systems design: theory, tools and applications. England: Pearson Education.Google Scholar
- Mingzhi, H., Jinquan, W., Yongwen, M., Yan, W., Weijiang, L., & Xiaofei, S. (2009). Control rules of aeration in a submerged bio-film wastewater treatment process using fuzzy neural networks. Expert Systems with Applications, 36, 10428–10437. https://doi.org/10.1016/j.eswa.2009.01.035.CrossRefGoogle Scholar
- Nadiri, A. A., Shokri, S., Tsai, F. T. C., & Moghaddam, A. A. (2018). Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model. Journal of Cleaner Production, 180, 539–549. https://doi.org/10.1016/j.jclepro.2018.01.139.CrossRefGoogle Scholar
- Oke, E.O., Jimoda, L.A., & Araromi, D.O. (2017). Determination of biocoagulant dosage for water clarification using developed neuro-fuzzy network integrated with user interface based calculator. Water Science and Technology: Water Supply, ws2017241, https://doi.org/10.2166/ws.2017.241.
- Oliveira-Esquerre, K. P., Mori, M., & Bruns, R. E. (2002). Simulation of an industrial wastewater treatment plant using artificial neural networks and principal component analysis. Brazilian Journal of Chemical Engineering, 19, 365–370. https://doi.org/10.1590/S0104-66322002000400002.CrossRefGoogle Scholar
- Pai, T. Y., Wan, T. J., Hsu, S. T., Chang, T. C., Tsai, Y. P., Lin, C. Y., Su, H. C., & Yu, L. F. (2009). Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent. Computers & Chemical Engineering, 33, 1272–1278. https://doi.org/10.1016/j.compchemeng.2009.02.004.CrossRefGoogle Scholar
- Pai, T. Y., Yang, P. Y., Wang, S. C., Lo, M. H., Chiang, C. F., Kuo, J. L., Chu, H. H., Su, H. C., Yu, L. F., Hu, H. C., & Chang, Y. H. (2011). Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality. Applied Mathematical Modelling, 35, 3674–3684. https://doi.org/10.1016/j.apm.2011.01.019.CrossRefGoogle Scholar
- Passino, K. M., & Yurkovich, S. (1998). Fuzzy control. California: Addison-Wesley Longman.Google Scholar
- Rustum, R., & Adeloye, A. J. (2007). Replacing outliers and missing values from activated sludge data using Kohonen self-organizing map. Journal of Environmental Engineering, 133, 909–916. https://doi.org/10.1061/(ASCE)0733-9372(2007)133:9(909).CrossRefGoogle Scholar
- Singh, K. P., Basant, N., Malik, A., & Jain, G. (2010). Modeling the performance of “up-flow anaerobic sludge-blanket” reactor based wastewater treatment plant using linear and non-linear approaches—a case study. Analytica Chimica Acta, 658, 1–11. https://doi.org/10.1016/j.aca.2009.11.001.CrossRefGoogle Scholar
- Suh, C. W., Lee, J. W., Hong, Y. S. T., & Shin, H. S. (2009). Sequential modeling of fecal coliform removals in a full-scale activated-sludge wastewater treatment plant using an evolutionary process model induction system. Water Research, 43, 137–147. https://doi.org/10.1016/j.watres.2008.09.022.CrossRefGoogle Scholar
- Tangirala, A. K. (2015). Principles of system identification: theory and practice. Florida: CRC Press.Google Scholar
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.Google Scholar
- Tomić, A. Š., Antanasijević, D., Ristić, M., Perić-Grujić, A., & Pocajt, V. (2018). A linear and non-linear polynomial neural network modeling of dissolved oxygen content in surface water: inter- and extrapolation performance with inputs’ significance analysis. Science of the Total Environment, 610-611, 1038–1046. https://doi.org/10.1016/j.scitotenv.2017.08.192.CrossRefGoogle Scholar