Sewer Network Multi-objective Optimization using Genetic Algorithms
DOI:
https://doi.org/10.52846/stccj.2023.3.1.49Keywords:
wastewater, sewer network, multi-objective optimization, controlled elitist genetic algorithmAbstract
This paper focuses on the multi-objective optimization of a sewer network that serves a medium-sized Romanian city, with a population of 250,000 residents. The sewer network is modeled using BSMSewer software package. The obtained results are based on numerical simulations with the optimization algorithm considering two performance criteria: the volume of overflow and the quality of the overflowed wastewater. For optimization, two approaches that use a controlled elitist genetic algorithm were employed: a multi-objective optimization and a two-steps multi-objective optimization. Results analysis involved comparing them with a scenario where each performance criterion was separately minimized. Additionally, a comparison was made to the situation where the sewer network operated without a control system, meaning the valves were fully open and the pumps were running at maximum capacity.
References
M. Marinaki and M. Papageorgiou, Optimal Real-time Control of Sewer Networks. London: Springer-Verlag, 2005
R. Saagi, X. Flores-Alsina, G. Fu, D. Butler, K. V. Gernaey, and U. Jeppsson, “Catchment & sewer network simulation model to benchmark control strategies within urban wastewater systems,” Environ. Model. Softw., vol. 78, pp. 16–30, Apr. 2016 DOI: https://doi.org/10.1016/j.envsoft.2015.12.013
O. Bailey, T. C. Arnot, E. J. M. Blokker, Z. Kapelan, and J. a. M. H. Hofman, “Predicting impacts of water conservation with a stochastic sewer model,” Water Sci. Technol., vol. 80, no. 11, pp. 2148–2157, Dec. 2019 DOI: https://doi.org/10.2166/wst.2020.031
O. Bailey, L. Zlatanovic, J.P. van der Hoek, Z. Kapelan, M. Blokker, T. Arnot, and J. Hofman, “A Stochastic Model to Predict Flow, Nutrient and Temperature Changes in a Sewer under Water Conservation Scenarios,” Water, vol. 12, no. 4, p. 1187, Apr. 2020 DOI: https://doi.org/10.3390/w12041187
M. Mauricio-Iglesias, I. Montero-Castro, A. L. Mollerup, and G. Sin, “A generic methodology for the optimisation of sewer systems using stochastic programming and self-optimizing control,” J. Environ. Manage., vol. 155, pp. 193–203, May 2015 DOI: https://doi.org/10.1016/j.jenvman.2015.03.034
V. Minzu, M. Barbu, and C. Nichita, “A Binary Hybrid Topology Particle Swarm Optimization Algorithm for Sewer Network Discharge,” in 2015 19th International Conference on System Theory, Control and Computing (ICSTCC), Nov. 2015, pp. 627–634 DOI: https://doi.org/10.1109/ICSTCC.2015.7321363
V. Minzu and A. Serbencu, “Control structure for the optimal sewer network discharge,” in 2016 20th International Conference on System Theory, Control and Computing (ICSTCC), Oct. 2016, pp. 61–66 DOI: https://doi.org/10.1109/ICSTCC.2016.7790641
M. Barbu, R. Vilanova, and I. Santin, “Fuzzy control applied on a benchmark simulation model for sewer networks,” in 2016 20th International Conference on System Theory, Control and Computing (ICSTCC), Oct. 2016, pp. 180–185 DOI: https://doi.org/10.1109/ICSTCC.2016.7790662
S. R. Mounce, W. Shepherd, M. Abdel-Aal, A.N.A. Schellart, J.D. Shucksmith, and S.J. Tait, “Optimisation of a fuzzy logic-based local real-time control system for mitigation of sewer flooding using genetic algorithms,” J. Hydroinformatics, vol. 22, no. 2, pp. 281–295 DOI: https://doi.org/10.2166/hydro.2019.058
U. Rathnayake and A. H. M. Faisal Anwar, “Dynamic control of urban sewer systems to reduce combined sewer overflows and their adverse impacts,” J. Hydrol., vol. 579, p. 124150, Dec. 2019 DOI: https://doi.org/10.1016/j.jhydrol.2019.124150
I. Vasiliev, L. Luca, M. Barbu, R. Vilanova, and S. Caraman, “Sewer Network Model of a City with a MediumSized Population,” 23rd International Carpathian Control Conference, Sinaia, 2022 DOI: https://doi.org/10.1109/ICCC54292.2022.9805864
I. Vasiliev, L. Luca, M. Barbu, R. Vilanova, and S. Caraman, “Optimal Control Strategy of a Sewer Network,” Water, vol. 14, no. 7, p. 1062, Mar. 2022 DOI: https://doi.org/10.3390/w14071062
K. Deb and T. Goel, “Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence,” in Evolutionary Multi-Criterion Optimization, vol. 1993, Springer Berlin Heidelberg, 2001, pp. 67–81 DOI: https://doi.org/10.1007/3-540-44719-9_5
C. C. Aldea and C. Olariu, “Selecting the Optimal Software Solution under Conditions of Uncertainty,” Procedia - Social and Behavioral Sciences, vol. 109, pp. 333–337, Jan. 2014 DOI: https://doi.org/10.1016/j.sbspro.2013.12.467
H. S. Sánchez, A. Visioli, and R. Vilanova, “Optimal Nash tuning rules for robust PID controllers,” Journal of the Franklin Institute, vol. 354, no. 10, pp. 3945–3970, Jul. 2017 DOI: https://doi.org/10.1016/j.jfranklin.2017.03.012
G. Guariso and M. Sangiorgio, “Improving the Performance of Multiobjective Genetic Algorithms: An Elitism-Based Approach,” Information, vol. 11, no. 12, p. 587, Dec. 2020 DOI: https://doi.org/10.3390/info11120587
