Abstracts

In this article, a selection procedure for identifying the most influential parameters of a wastewater treatment system with respect to maximizing economic profit is presented. Optimal wastewater treatment configurations require the adjustment of numerous system parameters to obtain economically efficient solutions. Such optimal solutions are commonly defined in terms of maximizing economic performance, typically expressed through minimizing the total cost of the wastewater treatment system. However, applying classical analytical optimization methods to this problem is time-consuming and computationally demanding due to the system’s high degree of nonlinearity. Accordingly, the primary objective of this study was to determine which parameters of the wastewater treatment system exert the greatest influence on overall economic profit. To support this selection process, an Adaptive Neuro-Fuzzy Inference System (ANFIS) was employed, as this method is well-suited for handling nonlinear relationships and redundant datasets. The results indicate that system size is the dominant factor affecting economic outcomes. The obtained findings have practical significance, as they provide guidance for selecting the most economically favorable configuration for a given wastewater treatment system.

Keywords

Wastewater, Economic profit, Optimal solution, ANFIS.

References

1. 1. Abdel-Fatah, M.A. (2018). Nanofiltration systems and applications in wastewater treatment: Review article. Ain Shams Engineering Journal, 9(4), 3077-3092.
2. 2. Ansari, F.A., Ravindran, B., Gupta, S.K., Nasr, M., Rawat, I., Bux, F. (2019). Techno-economic estimation of wastewater phycoremediation and environmental benefits using Scenedesmus obliquus microalgae. Journal of Environmental Management, 240, 293-302.
3. 3. Deneva, H., Teirumenieka, E., Lazov, L., Nikolić, V., Milovančević, M. (2017). Analysis of the influence of auxiliary gas pressure on cut characteristics of laser cut steel. Lasers in Engineering, 40(4-6), 253-263, doi: 10.1016/j.infrared.2018.04.007.
4. 4. Elreedy, A., Fujii, M., Tawfik, A. (2019). Psychrophilic hydrogen production from petrochemical wastewater via anaerobic sequencing batch reactor: techno-economic assessment and kinetic modelling. International Journal of Hydrogen Energy, 44(11), 5189-5202.
5. 5. Jokanović, B., Lalić, B., Milovančević, M., Simeunović, N., Marković, D. (2017). Economic development evaluation based on science and patents. Physica A: Statistical Mechanics and its Applications, 481, 141-145, doi: 10.1016/j.physa.2017.04.015.
6. 6. Kothari, R., Kumar, V., Pathak, V.V., Tyagi, V.V. (2017). Sequential hydrogen and methane production with simultaneous treatment of dairy industry wastewater: bioenergy profit approach. International Journal of Hydrogen Energy, 42(8), 4870-4879.
7. 7. Lyng, K.A., Stensgård, A.E., Hanssen, O.J., Modahl, I.S. (2018). Relation between greenhouse gas emissions and economic profit for different configurations of biogas value chains: A case study on different levels of sector integration. Journal of Cleaner Production, 182, 737-745.
8. 8. Mladenovic, I., Marković, D., Milovančević, M., Nikolic, M. (2016). Extreme learning approach with wavelet transform function for forecasting wind turbine wake effect to improve wind farm efficiency. Advances in Engineering Software, 96, 91-95. 10.1016/j.advengsoft.2016.02.011.
9. 9. Murashko, K., Nikku, M., Sermyagina, E., Vauterin, J.J., Hyppänen, T., Vakkilainen, E., Pyrhönen, J. (2018). Techno-economic analysis of a decentralized wastewater treatment plant operating in closed-loop. A Finnish case study. Journal of Water Process Engineering, 25, 278-294.
10. 10. Novković-Cvetković, B., Gligorijević, M., Petković, D., Jović, S., Milovančević, M., Nikolić, V. (2019). Evaluation of information and communication technology sector in the teaching process and strategic collaboration between universities and industry. Computer Applications in Engineering Education, 27(3), 653-662, doi: 10.1002/cae.22105.
11. 11. Nikolić, V., Kaljević, J., Jović, S., Petković, D., Milovančević, M., Dimitrov, L., Dachkinov, P. (2018). Survey of quality models of e-learning systems. Physica A: Statistical Mechanics and its Applications, 511, 324-330, doi: 10.1016/j.physa.2018.07.05 Najafi, A., Jafarian, A., Darand, J. (2019). Thermo-economic evaluation of a hybrid solar- conventional energy supply in a zero liquid discharge wastewater treatment plant. Energy Conversion and Management, 188, 276-295.
12. 12. Nikolić, V., Milovančević, M., Petković, D., Jocić, D., Savić, M. (2018). Parameters forecasting of laser welding by the artificial intelligence techniques. Facta Universitatis, Series: Mechanical Engineering, 16 (2), 193-201, doi: 10.22190/FUME180526025N Padilla-Rivera, A., Güereca, L.P. (2019). A proposal metric for sustainability evaluations of wastewater treatment systems (SEWATS). Ecological Indicators, 103, 22-33.
13. 13. Zheng, B., Huang, G., Liu, L., Zhai, M., Guan, Y. (2019). Metabolism of urban wastewater: Ecological network analysis for Guangdong Province, China. Journal of Cleaner Production, 217, 510-519.