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.
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Ahmadi, A., Fatemi, Z., & Nazari, S. (2018). Assessment of input data selection methods for BOD simulation using data-driven models: a case study. Environmental Monitoring and Assessment, 190, 239. https://doi.org/10.1007/s10661-018-6608-4.
Araromi, D. O., Sonibare, J. A., & Emuoyibofarhe, J. O. (2014). Fuzzy identification of reactive distillation for acetic acid recovery from wastewater. Journal of Environmental Chemical Engineering, 2, 1394–1403. https://doi.org/10.1016/j.jece.2014.05.008.
Belanche, L. A., Valde, J. J., Comas, J., Roda, I. R., & Poch, M. (1999). Towards a model of input–output behaviour of wastewater treatment plants using soft computing techniques. Environmental Modelling & Software, 14, 409–419. https://doi.org/10.1016/S1364-8152(98)00102-9.
Belanche, L., Valde, J. J., Comas, J., Roda, I. R., & Poch, M. (2000). Prediction of the bulking phenomenon in wastewater treatment plants. Artificial Intelligence in Engineering, 14, 307–317. https://doi.org/10.1016/S0954-1810(00)00012-1.
Cristea, V., Pop, C., & Agachi, P. S. (2009). Artificial neural networks modelling of PID and model predictive controlled wastewater treatment plant based on the benchmark simulation model no. 1. Computers & Chemical Engineering. https://doi.org/10.1016/S1570-7946(09)70197-X.
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.
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.
El-Din, A. G., & Smith, D. W. (2002). A neural network model to predict the wastewater inflow incorporating rainfall events. Water Research, 36, 1115–1126. https://doi.org/10.1016/S0043-1354(01)00287-1.
Gernaey, K. V., van Loosdrecht, M., Henze, M., Lind, M., & Jørgensen, S. B. (2004). Activated sludge wastewater treatment plant modelling and simulation: state of the art. Environmental Modelling & Software, 19, 763–783. https://doi.org/10.1016/j.envsoft.2003.03.005.
Gontarski, C. A., Rodrigues, P. R., Mori, M., & Prenem, L. F. (2000). Simulation of an industrial wastewater treatment plant using artificial neural networks. Computers & Chemical Engineering, 24, 1719–1723. https://doi.org/10.1016/S0098-1354(00)00449-X.
Guillaume, S. (2001). Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Transactions on Fuzzy Systems, 9, 426–443. https://doi.org/10.1109/91.928739.
Hamed, M. M., Khalafallah, M. G., & Hassanien, E. A. (2004). Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling & Software, 19, 919–928. https://doi.org/10.1016/j.envsoft.2003.10.005.
Hardin, J.W. & Hilbe, J.M. (2007). Generalized linear models and extensions. Texas: Stata Press.
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.
Jablonski, A., Barszcz, T., Bielecka, M., & Breuhaus, P. (2013). Modeling of probability distribution functions for automatic threshold calculation in condition monitoring systems. Measurement, 46, 727–738. https://doi.org/10.1016/j.measurement.2012.09.011.
Jang, J.-S. R. (1993). ANFIS—adaptive network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665–685. https://doi.org/10.1109/21.256541.
Jang, J.-S. R., & Sun, C.-T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83, 378–406. https://doi.org/10.1109/5.364486.
Karray, F. O., & De Silva, C. W. (2004). Soft computing and intelligent systems design: theory, tools and applications. England: Pearson Education.
McCullagh, P., & Nelder, J. A. (1989). Generalized linear models. New York: Chapman & Hall.
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.
Mjalli, F. S., Al-Asheh, S., & Alfadala, H. E. (2007). Use of artificial neural network black-box modeling for the prediction wastewater treatment plants performance. Journal of Environmental Management, 83, 329–338. https://doi.org/10.1016/j.jenvman.2006.03.004.
Moral, H., Aksoy, A., & Gokcay, C. F. (2008). Modeling of the activated sludge process by using artificial neural networks with automated architecture screening. Computers & Chemical Engineering, 32, 2471–2478. https://doi.org/10.1016/j.compchemeng.2008.01.008.
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.
Nasr, M. S., Moustafa, M. A., Seif, H. A., & El Kobrosy, G. (2012). Application of artificial neural network (ANN) for the prediction of El-Agamy wastewater treatment plant performance—Egypt. Alexandria Engineering Journal, 51, 37–43. https://doi.org/10.1016/j.aej.2012.07.005.
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.
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.
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.
Passino, K. M., & Yurkovich, S. (1998). Fuzzy control. California: Addison-Wesley Longman.
Pons, M. N., Spanjers, H., & Jeppsson, U. (1999). Towards a benchmark for evaluating control strategies in wastewater treatment plants by simulation. Computers & Chemical Engineering, 23, S403–S406. https://doi.org/10.1016/S0098-1354(99)80099-4.
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).
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.
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.
Tangirala, A. K. (2015). Principles of system identification: theory and practice. Florida: CRC Press.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.
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.
Yel, E., & Yalpir, S. (2011). Prediction of primary treatment effluent parameters by fuzzy inference system (FIS) approach. Procedia Computer Science, 3, 659–665. https://doi.org/10.1016/j.procs.2010.12.110.
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The authors would like to acknowledge the support of Seven-Up Bottling Company, Lagos, Nigeria for releasing the data used.
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Araromi, D.O., Majekodunmi, O.T., Adeniran, J.A. et al. Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression. Environ Monit Assess 190, 495 (2018). https://doi.org/10.1007/s10661-018-6878-x
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DOI: https://doi.org/10.1007/s10661-018-6878-x