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

Abstract

One of the most essential variables in water quality management and planning appears to be biological treatment in wastewater treatment plants. This critical characteristic, however, is difficult to measure and takes a long time to produce accurate findings. Scientists have attempted to develop several strategies to solve these challenges. Artificial intelligence models are one such way it is feasible to monitor the treatment plants pollutant parameters and manage these pollution elements during processing more reliably and economically. The use of a fuzzy logic model to control biological wastewater treatment is proposed in this research. The objective of these software models is to predict future treatment problems, intervene in the facility quickly and effectively, reduce or eliminate environmental pollution, improve the ecosystem, and determine the treatment efficiency of the wastewater treatment plant by using fewer laboratory-scale reactors and pilot plants. This study aims to use artificial intelligence models (fuzzy logic model) to achieve the best biological treatment (BOD, TN and TP) while at the same time ensuring that the treated wastewater is within the standards. The model was generated using FL (MATLAB software was used to create the FL model), and the model inputs are HRT, pH, temperature, F/M and BOD load to assess to which degree each of these variables affects BOD, TN and TP. The model outputs were within the acceptable wastewater quality standards according to the Turkish water pollution control regulation for the receiving environment of treated wastewater.

Keywords

• Biological treatment
• artificial intelligence models
• fuzzy logic
• treated wastewater
• Biological Oxygen Demand
• total nitrogen and total phosphorus

References

1. Ak, M., Kentel, E., Kucukali, S., 2017. A fuzzy logic tool to evaluate low-head hydropower technologies at the outlet of wastewater treatment plants. Renew. Sustain. Energy Rev. 68, 727–737. https://doi.org/10.1016/j.rser.2016.10.010
2. Chiroşcǎ, A., Dumitraşcu, G., Barbu, M., Caraman, S., 2011. Fuzzy control of a wastewater treatment process. Smart Innov. Syst. Technol. 10 SIST, 155–163. https://doi.org/10.1007/978-3-642-22194-1_16
3. Dai, J.F., Wang, Y.C., Ma, X.H., Yang, E.Z., 1997. Modeling and simulation of OFDCS’s. Asia-Pacific Microw. Conf. Proceedings, APMC 2, 577–580. https://doi.org/10.1109/apmc.1997.654607
4. Fan, L., Boshnakov, K., 2010. Fuzzy logic based dissolved oxygen control for SBR wastewater treatment process. Proc. World Congr. Intell. Control Autom. 4142–4146. https://doi.org/10.1109/WCICA.2010.5553972
5. Hung T. Nguyen, Carol Walker, E.A.W., 2006. A first course in fuzzy logic, 4th ed, Paper Knowledge . Toward a Media History of Documents. Chapman and Hall/CRC, New York. https://doi.org/https://doi.org/10.1201/9780429505546 Kristensen, N.R., Madsen, H., Jørgensen, S.B., 2004. A method for systematic improvement of stochastic grey-box models. Comput. Chem. Eng. 28, 1431–1449. https://doi.org/10.1016/j.compchemeng.2003.10.003
6. Mamdani, E.H., Assilian, S., 1975. An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man. Mach. Stud. 7, 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2
7. Mohd Noor, S.B., Khor, W.C., Ya ’acob, M.E., 2004. Fuzzy logic control of a nonlinear ph-neutralisation in waste water treatment plant. Int. J. Eng. Technol. 1, 197–205. Nadiri, A.A., Shokri, S., Tsai, F.T.C., Asghari Moghaddam, A., 2018. Prediction of effluent quality parameters of a wastewater treatment plant using a supervised committee fuzzy logic model. J. Clean. Prod. 180, 539–549. https://doi.org/10.1016/j.jclepro.2018.01.139
8. NEGNEVITSKY, M., 2005. Artificial Intelligence A Guide to Intelligent Systems, 2nd ed, British Library Cataloguing. London. https://doi.org/10.1016/j.poly.2016.11.012 Raman Bai, V., Bouwmeester, R., Mohan, s., 2009. Fuzzy logic water quality index and importance of water quality parameters. Air, Soil Water Res. 2, 51–59. https://doi.org/10.4137/aswr.s2156

9. Vijayaraghavan, G., Jayalakshmi, M., 2015. A Quick Review on Applications of Fuzzy Logic in Wastewater Treatment. Int. J. Res. Appl. Sci. Eng. Technol. 3, 421–425.
10. Yao, L., Xu, Z., Lv, C., Hashim, M., 2020. Incomplete interval type-2 fuzzy preference relations based on a multi-criteria group decision-making model for the evaluation of wastewater treatment technologies. Meas. J. Int. Meas. Confed. 151, 107137. https://doi.org/10.1016/j.measurement.2019.107137