Enhance modelling predicting for pollution removal in wastewater treatment plants by using an adaptive neuro-fuzzy inference system

Abstract

Biological and physical treatment in wastewater treatment plants appears to be one of the most important variables in water quality management and planning. This crucial characteristic, on the other hand, is difficult to quantify and takes a long time to obtain precise results. Scientists have sought to devise several solutions to address these issues. Artificial intelligence models are one technique to monitor the pollutant parameters more consistently and economically at treatment plants and regulate these pollution elements during processing. This study proposes using an adaptive network-based fuzzy inference system (ANFIS) model to regulate primary and biological wastewater treatment and used it to model the nonlinear interactions between influent pollutant factors and effluent variables in a wastewater treatment facility. Models for the prediction of removal efficiency of biological oxygen demand (BOD), total nitrogen (TN), total phosphorus (TP), and total suspended solids (TSS) in a wastewater treatment plant were developed using ANFIS. Hydraulic retention time (HRT), temperature (T), and dissolved oxygen (DO) were input variables for BOD, TN, TP, and TSS models, as determined by linear correlation matrices between input and output variables. The findings reveal that the developed system is capable of accurately predicting and controlling outcomes. For BOD, TN, TP, and TSS, ANFIS was able to achieve minimum mean square errors of 0.1673, 0.0266, 0.0318, and 0.0523, respectively. The correlation coefficients for BOD, TN, TP, and TSS are all quite strong. In the wastewater treatment plant, ANFIS' prediction performance was satisfactory and the ANFIS model can be used to predict the efficiency of removing pollutants from wastewater.

Keywords

• ANFIS
• artificial neural networks
• biological oxygen demand
• biological treatment
• hydrolic retention time
• total suspended solids

References

1. [1] H. Y. H. Alnajjar and O. Üçüncü, “Using of a Fuzzy Logic as One of The Artificial Intelligence Models to Increase the Efficiency of The Biological Treatment Ponds in Wastewater Treatment Plants,” vol. 4, no. 2, pp. 85–94, 2021.
2. [2] T. Y. Pai et al., “Using fuzzy inference system to improve neural network for predicting hospital wastewater treatment plant effluent,” Comput. Chem. Eng., vol. 33, no. 7, pp. 1272–1278, 2009, doi: 10.1016/j.compchemeng.2009.02.004.
3. [3] M. S. Gaya, N. A. Wahab, Y. M. Sam, and S. I. Samsuddin, “ANFIS based effluent pH quality prediction model for an activated sludge process,” Adv. Mater. Res., vol. 845, pp. 538–542, 2014, doi: 10.4028/www.scientific.net/AMR.845.538.
4. [4] K. Yetilmezsoy, H. Ozgun, R. K. Dereli, M. E. Ersahin, and I. Ozturk, “Adaptive neuro-fuzzy inference-based modeling of a full-scale expanded granular sludge bed reactor treating corn processing wastewater,” J. Intell. Fuzzy Syst., vol. 28, no. 4, pp. 1601–1616, 2015, doi: 10.3233/IFS-141445.
5. [5] V. Nourani, P. Asghari, and E. Sharghi, “Artificial intelligence based ensemble modeling of wastewater treatment plant using jittered data,” J. Clean. Prod., vol. 291, p. 125772, 2021, doi: 10.1016/j.jclepro.2020.125772.
6. [6] D. O. Araromi, O. T. Majekodunmi, J. A. Adeniran, and T. O. Salawudeen, “Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression,” Environ. Monit. Assess., vol. 190, no. 9, 2018, doi: 10.1007/s10661-018-6878-x.
7. [7] M. S. Gaya, N. Abdul Wahab, Y. M. Sam, S. I. Samsudin, and I. W. Jamaludin, “ANFIS direct inverse control of substrate in an activated sludge wastewater treatment system,” Appl. Mech. Mater., vol. 554, pp. 246–250, 2014, doi: 10.4028/www.scientific.net/AMM.554.246.
8. [8] D. S. Manu and A. K. Thalla, “Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater,” Appl. Water Sci., vol. 7, no. 7, pp. 3783–3791, 2017, doi: 10.1007/s13201-017-0526-4.

9. [9] E. Hong, A. M. Yeneneh, T. K. Sen, H. M. Ang, and A. Kayaalp, “ANFIS based Modelling of dewatering performance and polymer dose optimization in a wastewater treatment plant,” J. Environ. Chem. Eng., vol. 6, no. 2, pp. 1957–1968, 2018, doi: 10.1016/j.jece.2018.02.041.
10. [10] M. S. Gaya, N. A. Wahab, Y. M. Sam, A. N. Anuar, and S. I. Samsuddin, “ANFIS modelling of carbon removal in domestic wastewater treatment plant,” Appl. Mech. Mater., vol. 372, pp. 597–601, 2013, doi: 10.4028/www.scientific.net/AMM.372.597.
11. [11] M. NEGNEVITSKY, Artificial Intelligence A Guide to Intelligent Systems, 2nd ed., vol. 123. London, 2005.
12. [12] S. Akkurt, G. Tayfur, and S. Can, “Fuzzy logic model for the prediction of cement compressive strength,” Cem. Concr. Res., vol. 34, no. 8, pp. 1429–1433, 2004, doi: 10.1016/j.cemconres.2004.01.020.
13. [13] F. I. Turkdogan-Aydinol and K. Yetilmezsoy, “A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater,” J. Hazard. Mater., vol. 182, no. 1–3, pp. 460–471, 2010, doi: 10.1016/j.jhazmat.2010.06.054.
14. [14] D. Erdirencelebi and S. Yalpir, “Adaptive network fuzzy inference system modeling for the input selection and prediction of anaerobic digestion effluent quality,” Appl. Math. Model., vol. 35, no. 8, pp. 3821–3832, 2011, doi: 10.1016/j.apm.2011.02.015.
15. [15] Z. Hu, Y. V. Bodyanskiy, and O. K. Tyshchenko, Self-Learning and Adaptive Algorithms for Business Applications, no. 2019. 2019.
16. [16] T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and Its Applications to Modeling and Control,” IEEE Trans. Syst. Man Cybern., vol. SMC-15, no. 1, pp. 116–132, 1985, doi: 10.1109/TSMC.1985.6313399.
17. [17] J. R. Jang, “ANFIS : Adap tive-Ne twork-Based Fuzzy Inference System,” vol. 23, no. 3, 1993.
18. [18] J. Wan et al., “Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system,” Appl. Soft Comput. J., vol. 11, no. 3, pp. 3238–3246, 2011, doi: 10.1016/j.asoc.2010.12.026.
19. [19] Y. M. Wang and T. M. S. Elhag, “An adaptive neuro-fuzzy inference system for bridge risk assessment,” Expert Syst. Appl., vol. 34, no. 4, pp. 3099–3106, 2008, doi: 10.1016/j.eswa.2007.06.026.