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Simulation of Drinking Water Infrastructures Through Artificial Intelligence-Based Modelling for Sustainability Improvement

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Simulation Tools and Techniques (SIMUtools 2023)

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Abstract

The development of control systems for critical infrastructures requires testing and validating the proposals before using them in real environments. This work proposes the development of a new control system with an approach based on sustainability, which uses multi-agent systems as a basis, and which breaks away from traditional proposals focused on optimising energy costs. This new approach requires a thorough validation before its possible deployment, as it is based on distributed components that make independent decisions to generate complex emergent behaviour. In order to test its viability, a simulator has also been developed alongside the control system, which allows the behaviour of each agent to be analysed by subjecting it to tests using real data from the scenario to be controlled. Through this tool it is possible to observe each agent in the fulfilment of its functions, validate its behaviour, and check that the control system guarantees the supply of drinking water to a city, using the data obtained from that city as input. Through the simulator it is possible to analyse and represent different configurations of the control system over an infrastructure, thus being able to select the best option for the environment.

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References

  1. Tikhamarine, Y., Souag-Gamane, D., Ahmed, A.N., Kisi, O., El-Shafie, A.: Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm. J. Hydrol. 582, 124435 (2020)

    Article  Google Scholar 

  2. Pan, Y., Zhang, L.: Roles of artificial intelligence in construction engineering and management: a critical review and future trends. Auto. Constr. 122, 103517 (2021)

    Article  Google Scholar 

  3. Abdallah, M., Talib, M.A., Feroz, S., Nasir, Q., Abdalla, H., Mahfood, B.: Artificial intelligence applications in solid waste management: a systematic research review. Waste Manag. 109, 231–246 (2020)

    Article  Google Scholar 

  4. Qiu, J., Shen, Z., Leng, G., Xie, H., Hou, X., Wei, G.: Impacts of climate change on watershed systems and potential adaptation through BMPs in a drinking water source area. J. Hydrol. 573, 123–135 (2019)

    Article  Google Scholar 

  5. Oikonomou, K., Parvania, M., Khatami, R.: Optimal demand response scheduling for water distribution systems. IEEE Trans. Ind. Inform. 14(11), 5112–5122 (2018)

    Article  Google Scholar 

  6. Shao, Y., Zhou, X., Yu, T., Zhang, T., Chu, S.: Pump scheduling optimization in water distribution system based on mixed integer linear programming. EJOR (2023)

    Google Scholar 

  7. Lowe, M., Qin, R., Mao, X.: A review on machine learning, artificial intelligence, and smart technology in water treatment and monitoring. Water 14(9), 1384 (2022)

    Article  Google Scholar 

  8. Moazeni, F., Khazaei, J.: Optimal operation of water-energy microgrids; a mixed integer linear programming formulation. J. Clean. Prod. 275, 122776 (2020)

    Article  Google Scholar 

  9. Gejl, R.N., Bjerg, P.L., Henriksen, H.J., Hauschild, M.Z., Rasmussen, J., Rygaard, M.: Integrating groundwater stress in life-cycle assessments–an evaluation of water abstraction. J. Environ. Manag. 222, 112–121 (2018)

    Article  Google Scholar 

  10. Trowsdale, S.A., Lerner, D.N.: Implications of flow patterns in the sandstone aquifer beneath the mature conurbation of Nottingham (UK) for source protection. Q. J. Eng. Geol. Hydrogeol. 36(3), 197–206 (2003)

    Article  Google Scholar 

  11. Tiwari, A.K., Kalamkar, V.R., Pande, R.R., Sharma, S.K., Sontake, V.C., Jha, A.: Effect of head and PV array configurations on solar water pumping system. Mater. Today Proc. 46, 5475–5481 (2021)

    Article  Google Scholar 

  12. Rossman, L.A., Grayman, W.M.: Scale-model studies of mixing in drinking water storage tanks. J. Environ. Eng. 125(8), 755–761 (1999)

    Article  Google Scholar 

  13. Wooldridge, M.: An Introduction to Multiagent Systems. Wiley, Hoboken (2009)

    Google Scholar 

  14. McArthur, S.D., et al.: Multi-agent systems for power engineering applications—Part I: concepts, approaches, and technical challenges. IEEE Trans. Power Syst. 22(4), 1743–1752 (2007)

    Article  Google Scholar 

  15. Shu, H., Tang, L., Dong, J.: A survey on application of multi-agent system in power system. Power Syst. Technol. 29(6), 27–31 (2005)

    Google Scholar 

  16. Ameur, C., Faquir, S., Yahyaouy, A.: Intelligent optimization and management system for renewable energy systems using multi-agent. IAES Int. J. Artif. Intell. 8(4), 352 (2019)

    Google Scholar 

  17. Basma, A., Benyounes, O.: A simulation energy management system of a multi-source renewable energy based on multi agent system. IAES Int. J. Artif. Intell. 10(1), 191 (2021)

    Google Scholar 

  18. Ahmadi, S.E., Sadeghi, D., Marzband, M., Abusorrah, A., Sedraoui, K.: Decentralized bi-level stochastic optimization approach for multi-agent multi-energy networked micro-grids with multi-energy storage technologies. Energy 245, 123223 (2022)

    Article  Google Scholar 

  19. Knorn, S., Chen, Z., Middleton, R.H.: Overview: collective control of multiagent systems. IEEE Trans. Control Netw. Syst. 3(4), 334–347 (2015)

    Article  MathSciNet  Google Scholar 

  20. Berna-Martinez, J.V., Marcia-Perez, F.: Robotic control systems based on bioinspired multi-agent systems. I. J. Adv. Eng. Sci. Technol. 8(1), 32–38 (2011)

    Google Scholar 

  21. Aydin, M.E., Keleş, R.: A multi agent-based approach for energy efficient water resource management. Comput. Ind. Eng. 151, 106679 (2021)

    Article  Google Scholar 

  22. Ferber, J., Weiss, G.: Multi-agent Systems: An Introduction to Distributed Artificial Intelligence, vol. 1. Addison-Wesley, Reading (1999)

    Google Scholar 

  23. Official web of ECharts. https://echarts.apache.org/. Accessed 18 Oct 2023

  24. WASUSI-MAS Repositori. https://github.com/jvberna/wasusi-mas. Accessed 18 Oct 2023

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Acknowledgments

This work was supported by the UAIND22-01B Project “Adaptive control of urban supply systems” by the Office of the Vice President of Research of the University of Alicante, and ICAR23-06 Project “EWAi. Elderly Wellness Artificial Intelligence” by ICAR Fundation - International Centre for Ageing Research.

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Authors and Affiliations

  1. Aguas de Valencia S.A., Avda. Marqués del Turia, 46005, València, Spain

    Carlos Calatayud Asensi & Vicente Javier Macián Cervera

  2. University of Alicante, Carretera San Vicente del Raspeig s/n, 03690, Alicante, Spain

    José Vicente Berná Martinez, Lucia Arnau Muñoz & Francisco Maciá Pérez

Authors
  1. Carlos Calatayud Asensi
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  2. José Vicente Berná Martinez
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  3. Lucia Arnau Muñoz
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  4. Vicente Javier Macián Cervera
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  5. Francisco Maciá Pérez
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Corresponding author

Correspondence to José Vicente Berná Martinez .

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Editors and Affiliations

  1. Universidad de Sevilla, Sevilla, Spain

    José-Luis Guisado-Lizar

  2. Universidad de Sevilla, Sevilla, Sevilla, Spain

    Agustín Riscos-Núñez

  3. Universidad de Sevilla, Sevilla, Spain

    María-José Morón-Fernández

  4. Carleton University, Ottawa, IL, USA

    Gabriel Wainer

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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Calatayud Asensi, C., Berná Martinez, J.V., Arnau Muñoz, L., Macián Cervera, V.J., Maciá Pérez, F. (2024). Simulation of Drinking Water Infrastructures Through Artificial Intelligence-Based Modelling for Sustainability Improvement. In: Guisado-Lizar, JL., Riscos-Núñez, A., Morón-Fernández, MJ., Wainer, G. (eds) Simulation Tools and Techniques. SIMUtools 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 519. Springer, Cham. https://doi.org/10.1007/978-3-031-57523-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-57523-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57522-8

  • Online ISBN: 978-3-031-57523-5

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