Intelligent multi-agent architecture for a supervisor of a water treatment plant

The rapid development of Information and Communication Technologies (ICT) and high-capacity hardware components make it necessary to achieve a strong integration of automatic systems based on new paradigms on intelligent distributed architectures, where require highly complex supervision and control...

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Autores:
Mendoza Merchán, Eduardo Vicente
Andramuño, Joselyne
Núñez Alvarez, José Ricardo
Córdova Rivadeneira, Luis
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
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Acceso en línea:
https://hdl.handle.net/11323/8970
https://repositorio.cuc.edu.co/
Palabra clave:
Multi-agent systems
Automation systems
Water treatment plant
UML
Petri net
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openAccess
License
CC0 1.0 Universal
id RCUC2_7a18ab14b56182f8078b31c505b31483
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repository_id_str
dc.title.spa.fl_str_mv Intelligent multi-agent architecture for a supervisor of a water treatment plant
title Intelligent multi-agent architecture for a supervisor of a water treatment plant
spellingShingle Intelligent multi-agent architecture for a supervisor of a water treatment plant
Multi-agent systems
Automation systems
Water treatment plant
UML
Petri net
title_short Intelligent multi-agent architecture for a supervisor of a water treatment plant
title_full Intelligent multi-agent architecture for a supervisor of a water treatment plant
title_fullStr Intelligent multi-agent architecture for a supervisor of a water treatment plant
title_full_unstemmed Intelligent multi-agent architecture for a supervisor of a water treatment plant
title_sort Intelligent multi-agent architecture for a supervisor of a water treatment plant
dc.creator.fl_str_mv Mendoza Merchán, Eduardo Vicente
Andramuño, Joselyne
Núñez Alvarez, José Ricardo
Córdova Rivadeneira, Luis
dc.contributor.author.spa.fl_str_mv Mendoza Merchán, Eduardo Vicente
Andramuño, Joselyne
Núñez Alvarez, José Ricardo
Córdova Rivadeneira, Luis
dc.subject.spa.fl_str_mv Multi-agent systems
Automation systems
Water treatment plant
UML
Petri net
topic Multi-agent systems
Automation systems
Water treatment plant
UML
Petri net
description The rapid development of Information and Communication Technologies (ICT) and high-capacity hardware components make it necessary to achieve a strong integration of automatic systems based on new paradigms on intelligent distributed architectures, where require highly complex supervision and control tasks, due to the generated requirements of the new production systems, the high number of variables to control and the advancement of technologies, especially in industries where continuous processes have been established. In the present work, a distributed hierarchical modular architecture is proposed for a supervision system, based on multi-agent systems (MAS), oriented to the management of processes in the filtration stage of a water purification plant, using a methodology to the implementation of intelligent agents that allow to project, design, verify and validate the system. This methodology is fundamentally based on the use of the Unified Modeling Language (UML) for its projection and Petri nets (PN) for the simulation and validation of properties, which allows to guarantee the modularity, flexibility, and robustness of the proposed system. The architectures of the intelligent agents in the different programmable devices are modeled and simulated to achieve an adequate interaction and collaboration, allowing to reduce the conflicts that may be generated between them. The evaluation of the distributed architecture focuses on the fulfillment of the functional requirements and evaluation metrics, which, through the analysis of the properties of the Petri net, allows to determine the correct operation of the system and its dynamic behavior in the face of unforeseen situations at different levels of automation.
publishDate 2021
dc.date.issued.none.fl_str_mv 2021
dc.date.accessioned.none.fl_str_mv 2022-01-11T21:26:15Z
dc.date.available.none.fl_str_mv 2022-01-11T21:26:15Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.doi.spa.fl_str_mv doi:10.1088/1742-6596/2090/1/012124
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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Corporación Universidad de la Costa
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url https://hdl.handle.net/11323/8970
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dc.relation.references.spa.fl_str_mv [1] Metzger, M., & Polakow, G. 2011. 2011. A survey on applications of agent technology in industrial process control. IEEE Transactions on Industrial Informatics, vol. 7, no. 4, pp. 570-581.
[2] Vilanova, R., Santín, I., & Pedret, C. 2017. Control en estaciones depuradoras de aguas residuales: Estado actual y perspectivas. Revista Iberoamericana de Automática e Informática Industrial RIAI, vol. 14, no. 4, pp. 329-345.
[3] Nuñez Alvarez, J. R., Zamora, Y. P., Pina, I. B., & Angarita, E. N. 2021. Demilitarized network to secure the data stored in industrial networks. International Journal of Electrical and Computer Engineering (IJECE), vol. 11, no. 1, pp. 611.
[4] Aguilar, J., Bolivar, A.R., Hidrobo, F., & Cerrada, M. 2012. Sistemas MultiAgentes y sus Aplicaciones en Automatización Industrial. 2nd Edition, Universidad de Los Andes. Venezuela.
[5] Valdez, J., Pandolfi, D., & Villagra, A. 2018. Red de sensores inteligentes para adquisición de datos de una planta de desalinización de agua. Informes Científicos Técnicos-UNPA, vol. 10, no. 2, pp. 83-95.
[6] Nuñez-Alvarez, J. R., Benítez-Pina, I., & Llosas-Albuerne, Y. 2020. Communications in Flexible Supervisor for Laboratory Research in Renewable Energy. IOP Conf. Series: Materials Science and Engineering, vol. 844, pp. 012016 doi:10.1088/1757-899X/844/1/012016
[7] Michel, F., Ferber, J., & Drogoul, A. 2018. Multi-agent systems and simulation: A survey from the agent community’s perspective. Multi-Agent Systems, CRC Press, pp. 17-66.
[8] Oliveira, P., Pinto, T., Morais, H., & Vale, Z. 2012. MASGriP—a multi-agent smart grid simulation platform. IEEE Power and Energy Society General Meeting, 2012, pp. 1-8.
[9] Grignard A., Taillandier P., Gaudou B., Vo D.A., Huynh N.Q., & Drogoul A. 2013. GAMA 1.6: Advancing the Art of Complex Agent-Based Modeling and Simulation. Lecture Notes in Computer Science, vol 8291. Springer, Berlin. https://doi.org/10.1007/978-3-642-44927-7_9
[10] Leitão, P., Rodrigues, N., Turrin, C., & Pagani, A. 2015. Multiagent system integrating process and quality control in a factory producing laundry washing machines. IEEE Transactions on Industrial Informatics, vol. 11, no. 4, pp. 879-886.
[11] Dorri, A., Kanhere, S. S., & Jurdak, R. 2018. Multi-agent systems: A survey. IEEE Access, vol. 6, pp. 28573-28593.
[12] Jiang, Z., Khalgui, M., Mosbahi, O., & Jaouadi, A. 2014. A novel hierarchical multi-agent architecture for automatic restoration of smart grids. International Journal of Control and Automation, vol. 7, no. 1, pp. 153-170.
[13] Bravo, C., Aguilar Castro, J., Ríos, A., Aguilar Martin, J., & Rivas, F. 2011. Arquitectura basada en inteligencia artificial distribuida para la gerencia integrada de producción industrial. Revista iberoamericana de automática e informática industrial, vol. 8, no. 4, pp. 405-417.
[14] Cárdenas Torres, J. S., & Casas León, J. D. 2017. Diseño de una red hidráulica, automatizada, para la optimización del lavado de filtros de la planta de tratamiento de agua potable de Guasca, Cundinamarca. Thesis, Fundación Universidad de América, Colombia.
[15] Arenas Castaño, F. A., & Londoño Giraldo, W. H. 2017. Diseño de un sistema automatizado para una planta de tratamiento de agua potable. Thesis. Instituto Tecnológico Metropolitano. Colombia.
[16] Patriarca, H A., & Campana, A. 2013. Automatización de acueducto con tecnología GSM/GPRS: Uso de Herramientas Grafcet y GEMMA Aplicación sobre tecnología PLC-HMI-SCADA. Editorial Académica Española, pp. 160.
[17] Calderón Córdova, C., et al., Monitoring and automation of the water pumping, and storage process applied to a water treatment plant. 13th Iberian Conference on Information Systems and Technologies (CISTI), 2018, pp. 1-6. doi: 10.23919/CISTI.2018.8399292
[18] González-Salcedo, L. O., & García-Nuñez, J. B. 2020. Elaboración de un modelo neuronal artificial para la estimación de turbiedad y proposición de dosificaciones en el tratamiento de aguas residuales de la industria avícola. Informador Técnico, vol. 84, no. 1, pp. 3-17.
[19] Mendoza, E., Andramuño, J., Núñez, J., & Benítez, I. 2021. Deliberative architecture for smart sensors in the filtering operation of a water purification plant. J. Phys.: Conf. Ser., vol. 1730, no. 1, pp. 012088. doi: 10.1088/1742-6596/1730/1/012088.
[20] Bazyd\lo, G., Wojnakowski, M., & Wiśniewski, R. The use of UML and Petri net for graphic specification of the reconfigurable logic controllers. AIP Conference Proceedings, 2018, vol. 2040, no. 1, pp. 080004.
[21] Vani, M., Kumari, M. C., Priya, M. H., & Harika, N. 2015. An effective language for objectoriented design-uml (unified modeling language). International Research Journal of Engineering and Technology (IRJET 2015), vol. 2, no. 5, pp. 1212-1218.
[22] Hendricks, D. W. 2006. Water treatment unit processes: physical and chemical. 1 st Edition. CRC press, pp. 1266.
[23] Faust, S. D., & Aly, O. M. 1996. Chemistry of water treatment. 1 st Edition. CRC press, pp. 600.
[24] Mendoza, E. V., Fuentes, P., Benítez, I., Reina, D., & Núñez, J. 2020. Red de sensores inalámbricos multisalto para sistemas domóticos de bajo costo y área extendida. Revista Iberoamericana de Automática e Informática industrial, vol. 17, no. 4, pp. 412-423. doi: 10.4995/riai.2020.12301.
[25] Andramuño, J., Mendoza, E., Núñez, J., & Liger, E. 2021. Intelligent distributed module for local control of lighting and electrical outlets in a home. J. Phys.: Conf. Ser., vol. 1730, no. 1, pp. 012001. doi: 10.1088/1742-6596/1730/1/012001.
[26] Nuñez, J. R., et al., 2019. Tools for the Implementation of a SCADA System in a Desalination Process. IEEE Latin America Transactions, vol. 17, no. 11, pp. 1858-1864, DOI: 10.1109/TLA.2019.8986424
[27] Mendoza, E., Andramuño, J., & Córdova, L. 2020. Intelligent flow and level sensors for a filtering system in a water treatment plant. Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, doi: 10.18687/LACCEI2020.1.1.424.
[28] Zarandi, M. F., & Azad, F. K. 2013. A type 2 fuzzy multi agent-based system for scheduling of steel production. 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 992-996.
[29] Fazel Zarandi, M. H., Kashani Azad, F., & Karimi Kashani, H. A. 2017. A Hybrid Modeling for Continuous Casting Scheduling Problem. AUT Journal of Modeling and Simulation, vol. 49, no. 2, pp. 173-180.
[30] Taboun, M. S., & Brennan, R. W. 2017. An embedded multi-agent system based industrial wireless sensor network. Sensors, vol. 17, no. 9, pp. 2112.
[31] Turcu, C., Turcu, C., & Gaitan, V. 2018. An Internet of Things oriented approach for water utility monitoring and control. Advances in Computer Science, arXiv:1811.12807, pp. 175-180.
[32] Legien, G., et al. 2017. Agent-based decision support system for technology recommendation. Procedia Computer Science, vol. 108, pp. 897-906.
[33] Zhao, Z., & Xu, Y. 2010. DPMAS: A Design Method for Multi-agent System Using Agent UML. 2010 Third International Conference on Information and Computing, pp. 137-140, doi: 10.1109/ICIC.2010.305.
[34] Silva, J. R., Benítez, I., Villafruela, L., Gomis, O., & Sudrià, A. 2008. Modeling extended Petri nets compatible with GHENeSys IEC61131 for industrial automation. The International Journal of Advanced Manufacturing Technology, vol. 36, no. 11-12, pp. 1180-1190.
[35] Girault, C., & Valk, R. 2013. Petri nets for systems engineering: a guide to modeling, verification, and applications. 3 rd Edition. Springer Science & Business Media.
[36] Stanimirović, P. S., & Petković, M. D. Gauss–Jordan elimination method for computing outer inverses. Applied Mathematics and Computation, vol. 219, no. 9, pp. 4667-4679.
[37] García, D. R., Simari, G. R., & García. A. J. 2004. Planificación de agentes BDI. VI Workshop de Investigadores en Ciencias de la Computación, 2004, pp. 418-423. http://sedici.unlp.edu.ar/handle/10915/21240
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spelling Mendoza Merchán, Eduardo Vicente82a314d47eefa500caa9f32236626f39Andramuño, Joselynec0998e1803e346dad1d6ebfa8be587a1Núñez Alvarez, José Ricardo0cc3252b61f921503a4a2c5f9e03c67cCórdova Rivadeneira, Luis45ce9f08d44f051a5cd0513de837e4302022-01-11T21:26:15Z2022-01-11T21:26:15Z20211742-65881742-6596https://hdl.handle.net/11323/8970doi:10.1088/1742-6596/2090/1/012124Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The rapid development of Information and Communication Technologies (ICT) and high-capacity hardware components make it necessary to achieve a strong integration of automatic systems based on new paradigms on intelligent distributed architectures, where require highly complex supervision and control tasks, due to the generated requirements of the new production systems, the high number of variables to control and the advancement of technologies, especially in industries where continuous processes have been established. In the present work, a distributed hierarchical modular architecture is proposed for a supervision system, based on multi-agent systems (MAS), oriented to the management of processes in the filtration stage of a water purification plant, using a methodology to the implementation of intelligent agents that allow to project, design, verify and validate the system. This methodology is fundamentally based on the use of the Unified Modeling Language (UML) for its projection and Petri nets (PN) for the simulation and validation of properties, which allows to guarantee the modularity, flexibility, and robustness of the proposed system. The architectures of the intelligent agents in the different programmable devices are modeled and simulated to achieve an adequate interaction and collaboration, allowing to reduce the conflicts that may be generated between them. The evaluation of the distributed architecture focuses on the fulfillment of the functional requirements and evaluation metrics, which, through the analysis of the properties of the Petri net, allows to determine the correct operation of the system and its dynamic behavior in the face of unforeseen situations at different levels of automation.application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference SeriesMulti-agent systemsAutomation systemsWater treatment plantUMLPetri netIntelligent multi-agent architecture for a supervisor of a water treatment plantArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Metzger, M., & Polakow, G. 2011. 2011. A survey on applications of agent technology in industrial process control. IEEE Transactions on Industrial Informatics, vol. 7, no. 4, pp. 570-581.[2] Vilanova, R., Santín, I., & Pedret, C. 2017. Control en estaciones depuradoras de aguas residuales: Estado actual y perspectivas. Revista Iberoamericana de Automática e Informática Industrial RIAI, vol. 14, no. 4, pp. 329-345.[3] Nuñez Alvarez, J. R., Zamora, Y. P., Pina, I. B., & Angarita, E. N. 2021. Demilitarized network to secure the data stored in industrial networks. International Journal of Electrical and Computer Engineering (IJECE), vol. 11, no. 1, pp. 611.[4] Aguilar, J., Bolivar, A.R., Hidrobo, F., & Cerrada, M. 2012. Sistemas MultiAgentes y sus Aplicaciones en Automatización Industrial. 2nd Edition, Universidad de Los Andes. Venezuela.[5] Valdez, J., Pandolfi, D., & Villagra, A. 2018. Red de sensores inteligentes para adquisición de datos de una planta de desalinización de agua. Informes Científicos Técnicos-UNPA, vol. 10, no. 2, pp. 83-95.[6] Nuñez-Alvarez, J. R., Benítez-Pina, I., & Llosas-Albuerne, Y. 2020. Communications in Flexible Supervisor for Laboratory Research in Renewable Energy. IOP Conf. Series: Materials Science and Engineering, vol. 844, pp. 012016 doi:10.1088/1757-899X/844/1/012016[7] Michel, F., Ferber, J., & Drogoul, A. 2018. Multi-agent systems and simulation: A survey from the agent community’s perspective. Multi-Agent Systems, CRC Press, pp. 17-66.[8] Oliveira, P., Pinto, T., Morais, H., & Vale, Z. 2012. MASGriP—a multi-agent smart grid simulation platform. IEEE Power and Energy Society General Meeting, 2012, pp. 1-8.[9] Grignard A., Taillandier P., Gaudou B., Vo D.A., Huynh N.Q., & Drogoul A. 2013. GAMA 1.6: Advancing the Art of Complex Agent-Based Modeling and Simulation. Lecture Notes in Computer Science, vol 8291. Springer, Berlin. https://doi.org/10.1007/978-3-642-44927-7_9[10] Leitão, P., Rodrigues, N., Turrin, C., & Pagani, A. 2015. Multiagent system integrating process and quality control in a factory producing laundry washing machines. IEEE Transactions on Industrial Informatics, vol. 11, no. 4, pp. 879-886.[11] Dorri, A., Kanhere, S. S., & Jurdak, R. 2018. Multi-agent systems: A survey. IEEE Access, vol. 6, pp. 28573-28593.[12] Jiang, Z., Khalgui, M., Mosbahi, O., & Jaouadi, A. 2014. A novel hierarchical multi-agent architecture for automatic restoration of smart grids. International Journal of Control and Automation, vol. 7, no. 1, pp. 153-170.[13] Bravo, C., Aguilar Castro, J., Ríos, A., Aguilar Martin, J., & Rivas, F. 2011. Arquitectura basada en inteligencia artificial distribuida para la gerencia integrada de producción industrial. Revista iberoamericana de automática e informática industrial, vol. 8, no. 4, pp. 405-417.[14] Cárdenas Torres, J. S., & Casas León, J. D. 2017. Diseño de una red hidráulica, automatizada, para la optimización del lavado de filtros de la planta de tratamiento de agua potable de Guasca, Cundinamarca. Thesis, Fundación Universidad de América, Colombia.[15] Arenas Castaño, F. A., & Londoño Giraldo, W. H. 2017. Diseño de un sistema automatizado para una planta de tratamiento de agua potable. Thesis. Instituto Tecnológico Metropolitano. Colombia.[16] Patriarca, H A., & Campana, A. 2013. Automatización de acueducto con tecnología GSM/GPRS: Uso de Herramientas Grafcet y GEMMA Aplicación sobre tecnología PLC-HMI-SCADA. Editorial Académica Española, pp. 160.[17] Calderón Córdova, C., et al., Monitoring and automation of the water pumping, and storage process applied to a water treatment plant. 13th Iberian Conference on Information Systems and Technologies (CISTI), 2018, pp. 1-6. doi: 10.23919/CISTI.2018.8399292[18] González-Salcedo, L. O., & García-Nuñez, J. B. 2020. Elaboración de un modelo neuronal artificial para la estimación de turbiedad y proposición de dosificaciones en el tratamiento de aguas residuales de la industria avícola. Informador Técnico, vol. 84, no. 1, pp. 3-17.[19] Mendoza, E., Andramuño, J., Núñez, J., & Benítez, I. 2021. Deliberative architecture for smart sensors in the filtering operation of a water purification plant. J. Phys.: Conf. Ser., vol. 1730, no. 1, pp. 012088. doi: 10.1088/1742-6596/1730/1/012088.[20] Bazyd\lo, G., Wojnakowski, M., & Wiśniewski, R. The use of UML and Petri net for graphic specification of the reconfigurable logic controllers. AIP Conference Proceedings, 2018, vol. 2040, no. 1, pp. 080004.[21] Vani, M., Kumari, M. C., Priya, M. H., & Harika, N. 2015. An effective language for objectoriented design-uml (unified modeling language). International Research Journal of Engineering and Technology (IRJET 2015), vol. 2, no. 5, pp. 1212-1218.[22] Hendricks, D. W. 2006. Water treatment unit processes: physical and chemical. 1 st Edition. CRC press, pp. 1266.[23] Faust, S. D., & Aly, O. M. 1996. Chemistry of water treatment. 1 st Edition. CRC press, pp. 600.[24] Mendoza, E. V., Fuentes, P., Benítez, I., Reina, D., & Núñez, J. 2020. Red de sensores inalámbricos multisalto para sistemas domóticos de bajo costo y área extendida. Revista Iberoamericana de Automática e Informática industrial, vol. 17, no. 4, pp. 412-423. doi: 10.4995/riai.2020.12301.[25] Andramuño, J., Mendoza, E., Núñez, J., & Liger, E. 2021. Intelligent distributed module for local control of lighting and electrical outlets in a home. J. Phys.: Conf. Ser., vol. 1730, no. 1, pp. 012001. doi: 10.1088/1742-6596/1730/1/012001.[26] Nuñez, J. R., et al., 2019. Tools for the Implementation of a SCADA System in a Desalination Process. IEEE Latin America Transactions, vol. 17, no. 11, pp. 1858-1864, DOI: 10.1109/TLA.2019.8986424[27] Mendoza, E., Andramuño, J., & Córdova, L. 2020. Intelligent flow and level sensors for a filtering system in a water treatment plant. Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, doi: 10.18687/LACCEI2020.1.1.424.[28] Zarandi, M. F., & Azad, F. K. 2013. A type 2 fuzzy multi agent-based system for scheduling of steel production. 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), pp. 992-996.[29] Fazel Zarandi, M. H., Kashani Azad, F., & Karimi Kashani, H. A. 2017. A Hybrid Modeling for Continuous Casting Scheduling Problem. AUT Journal of Modeling and Simulation, vol. 49, no. 2, pp. 173-180.[30] Taboun, M. S., & Brennan, R. W. 2017. An embedded multi-agent system based industrial wireless sensor network. Sensors, vol. 17, no. 9, pp. 2112.[31] Turcu, C., Turcu, C., & Gaitan, V. 2018. An Internet of Things oriented approach for water utility monitoring and control. Advances in Computer Science, arXiv:1811.12807, pp. 175-180.[32] Legien, G., et al. 2017. 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Planificación de agentes BDI. 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