Deliberative architecture for smart sensors in the filtering operation of a water purification plant
The increase of applications for industrial smart sensors is booming, mainly due to the use of distributed automation architectures, industrial evolution and recent technological advances, which guide the industry to a greater degree of automation, integration and globalization. In this research wor...
- Autores:
-
Mendoza, E.
Andramuño, J.
Núñez, J.
Benítez, I.
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/8310
- Acceso en línea:
- https://hdl.handle.net/11323/8310
https://doi.org/10.1088/1742-6596/1730/1/012088
https://repositorio.cuc.edu.co/
- Palabra clave:
- Deliberative architecture
smart sensors
water purification plant
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Deliberative architecture for smart sensors in the filtering operation of a water purification plant |
| title |
Deliberative architecture for smart sensors in the filtering operation of a water purification plant |
| spellingShingle |
Deliberative architecture for smart sensors in the filtering operation of a water purification plant Deliberative architecture smart sensors water purification plant |
| title_short |
Deliberative architecture for smart sensors in the filtering operation of a water purification plant |
| title_full |
Deliberative architecture for smart sensors in the filtering operation of a water purification plant |
| title_fullStr |
Deliberative architecture for smart sensors in the filtering operation of a water purification plant |
| title_full_unstemmed |
Deliberative architecture for smart sensors in the filtering operation of a water purification plant |
| title_sort |
Deliberative architecture for smart sensors in the filtering operation of a water purification plant |
| dc.creator.fl_str_mv |
Mendoza, E. Andramuño, J. Núñez, J. Benítez, I. |
| dc.contributor.author.spa.fl_str_mv |
Mendoza, E. Andramuño, J. Núñez, J. Benítez, I. |
| dc.subject.eng.fl_str_mv |
Deliberative architecture smart sensors water purification plant |
| topic |
Deliberative architecture smart sensors water purification plant |
| description |
The increase of applications for industrial smart sensors is booming, mainly due to the use of distributed automation architectures, industrial evolution and recent technological advances, which guide the industry to a greater degree of automation, integration and globalization. In this research work, an architecture for deliberative-type intelligent industrial sensors is proposed, based on the BDI (Belief Desire Intentions) model, adaptable to the measurement of different variables of the filtering process of a water purification plant. An intelligent sensor with functions of signal digitalization, self-calibration, alarm generation, communication with PLC, user interface for parameter adjustment, and analysis with data extrapolation have been arranged. For decision making, the use of fuzzy logic techniques has been considered, which allows imprecise parameters to be appropriately represented, simplifying decision problem solving in the industrial environment, generating stable and fast systems with low processing requirements. The proposed architecture has been modelled, simulated and validated using UML language in conjunction with Petri nets, which facilitate the representation of discrete system events, presenting them clearly and precisely. In the implementation and testing of the prototype, C/C ++ language has been used in an 8-bit microcontroller, experimentally corroborating the operation of the device, which allowed evaluating the behavior of a pseudo-intelligent agent based on the requirements of the water treatment plant, and also through comparisons with similar works developed by other researchers. |
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2021 |
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2021-06-01T00:28:36Z |
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2021 |
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Artículo de revista |
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eng |
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L. H. Hernández, J. Pestana, D. C. Palomeque, P. Campoy, y J. L. Sanchez-Lopez. Identificación y control en cascada mediante inversión de no linealidades del cuatrirrotor para el Concurso de Ingeniería de Control CEA IFAC 2012. Revista Iberoamericana de Automática e Informática Industrial RIAI, 2013, vol. 10, n.o 3, pp. 356-367. M. Hermann, T. Pentek, y B. Otto. Design Principles for Industries 4.0 Scenarios. 49th Hawaii International Conference on System Sciences (HICSS), 2016, pp. 3928-3937, doi: 10.1109/HICSS.2016.488 H. Kagermann. Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0 Working Group acatech. National Academy of Science and Engineering, 2013. https://en.acatech.de/publication/recommendations-for-implementing-thestrategic-initiative-industrie-4-0-final-report-of-the-industrie-4-0-working-group/. A. C. Pereira y F. Romero. A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manufacturing, 2017 vol. 13, pp. 1206-1214 A. Cruz. Estructura de Control para Procesos de Producción desde el Paradigma de los Sistemas Holónicos de Manufactura. Universidad del Cauca, 2017. Chee-Yee Chong y S. P. Kumar. Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE, 2003, vol. 91, n.o 8, pp. 1247-1256, ago. 2003, doi: 10.1109/JPROC.2003.814918. C. Bravo, J. Aguilar-Castro, A. Ríos, J. Aguilar-Martin, y F. Rivas. Arquitectura Basada en Inteligencia Artificial Distribuida para la Gerencia Integrada de Producción Industrial. Revista Iberoamericana de Automática e Informática Industrial RIAI, 2011, vol. 8, n.o 4, pp. 405-417, oct. 2011, doi: 10.1016/j.riai.2011.09.013. J. Aguilar, LB 2013 Sistemas MultiAgentes y sus Aplicaciones en Automatización Industrial_1.pdf, 2.a ed. Venezuela: Universidad de los Andes, 2013. J. Andramuño, N. Vega, y P. Parra. Industry 4.0 Embedded Systems Network. IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2019, pp. 1-6, doi: 10.1109/CHILECON47746.2019.8987620. L. Quintero. Un modelo de control inteligente para sistemas de manufactura basado en los paradigmas holónico y multi-agente. Escuela de sistemas, 2009. E. Hernandez, J. Alvarez, F. J. Valdes, y H. Puebla. An Integral Formulation Approach for Numerical Solution of Tubular Reactors Models. International Journal of Chemical Reactor Engineering, 2011, vol. 9, n.o 1, doi: 10.2202/1542-6580.2444. Leitão, Rodrigues, Turrin, y Pagani. Multiagent System Integrating Process and Quality Control in a Factory Producing Laundry Washing Machines. IEEE Journals & Magazine, 2015. https://ieeexplore.ieee.org/document/7104129. J. Palanca, E. del Val, A. Garcia-Fornes, H. Billhardt, J. M. Corchado, y V. Julián. Designing a goal-oriented smart-home environment, Inf Syst Front, 2018, vol. 20, n.o 1, pp. 125-142, doi: 10.1007/s10796-016-9670-x. Sun, Yu, Kochurov, Hao, y Hu. A Multi-agent Based Intelligent Sensor and Actuator Network Design for Smart House and Home Automation. Journal of Sensor and Actuator Networks, 2013, 2(3), 557-588 Benitez-Pina, I. F., Lamar-Carbonell, S., Silva, R. M. D., Eigi-Miyagi, P., & Silva, J. R. Design of automatic control system based on unified timed hybrid Petri net. Dyna, 2017, 84(200), 80-89. J. R. Nuñez-Alvarez, I. F. Benítez-Pina, y Y. Llosas-Albuerne. Communications in Flexible Supervisor for Laboratory Research in Renewable Energy. In IOP Conference Series: Materials Science and Engineering, 2020, Vol. 844, No. 1, p. 012016. E. Mendoza, J. Andramuño, y L. Cordova. Intelligent flow and level sensors for a filtering system in a water treatment plant. 18th LACCEI International Multi-Conference for Engineering, Education and Technology, 2020. H. Berger, Automating with SIMATIC S7-1200: configuring, programming and testing with STEP 7 Basic Visualization with HMI Basic. John Wiley & Sons, 2013. H. Zhang. Design of Control System for Concrete Mixing Station Based on PLC and Touch Screen. Advanced Materials Research. Trans Tech Publications Ltd, 2011. p. 2473-2476. J. Rochez, E. B. B. Vinuela, M. Koutli, y T. Petrou, «Opening the Floor to PLCs and IPCs: CODESYS in UNICOS», p. 4, 2014. E. V. M. Merchán, P. G. F. Espinoza, I. F. B. Pina, D. R. Tabares, y J. R. N. Alvarez. Red de sensores inalámbricos multisalto para sistemas domóticos de bajo costo y área extendida. Revista Iberoamericana de Automática e Informática industrial, 2020, vol. 0, n.o 0, Art. n.o 0, abr. 2020, doi: 10.4995/riai.2020.12301. R. Loarte, B. Quizhpe, y H. Paz. Desarrollo y simulación de un sistema multiagente para la comunicación de semáforos para encontrar la ruta óptima mediante grafos. Revista Tecnológica ESPOL, 2015, vol. 28, pp. 43-63 A. Cando. Prototipos de sensores inteligentes de caudal y nivel para la operación de un subsistema de filtrado en una planta potabilizadora de agua. http://repositorio.ucsg.edu.ec/handle/3317/14413. |
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Mendoza, E.Andramuño, J.Núñez, J.Benítez, I.2021-06-01T00:28:36Z2021-06-01T00:28:36Z20211742-6588, 1742-6596https://hdl.handle.net/11323/8310https://doi.org/10.1088/1742-6596/1730/1/012088Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The increase of applications for industrial smart sensors is booming, mainly due to the use of distributed automation architectures, industrial evolution and recent technological advances, which guide the industry to a greater degree of automation, integration and globalization. In this research work, an architecture for deliberative-type intelligent industrial sensors is proposed, based on the BDI (Belief Desire Intentions) model, adaptable to the measurement of different variables of the filtering process of a water purification plant. An intelligent sensor with functions of signal digitalization, self-calibration, alarm generation, communication with PLC, user interface for parameter adjustment, and analysis with data extrapolation have been arranged. For decision making, the use of fuzzy logic techniques has been considered, which allows imprecise parameters to be appropriately represented, simplifying decision problem solving in the industrial environment, generating stable and fast systems with low processing requirements. The proposed architecture has been modelled, simulated and validated using UML language in conjunction with Petri nets, which facilitate the representation of discrete system events, presenting them clearly and precisely. In the implementation and testing of the prototype, C/C ++ language has been used in an 8-bit microcontroller, experimentally corroborating the operation of the device, which allowed evaluating the behavior of a pseudo-intelligent agent based on the requirements of the water treatment plant, and also through comparisons with similar works developed by other researchers.Mendoza, E.Andramuño, J.Núñez, J.Benítez, I.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Journal of Physics: Conference Serieshttps://iopscience.iop.org/article/10.1088/1742-6596/1730/1/012088Deliberative architecturesmart sensorswater purification plantDeliberative architecture for smart sensors in the filtering operation of a water purification 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/acceptedVersionL. H. Hernández, J. Pestana, D. C. Palomeque, P. Campoy, y J. L. Sanchez-Lopez. Identificación y control en cascada mediante inversión de no linealidades del cuatrirrotor para el Concurso de Ingeniería de Control CEA IFAC 2012. Revista Iberoamericana de Automática e Informática Industrial RIAI, 2013, vol. 10, n.o 3, pp. 356-367.M. Hermann, T. Pentek, y B. Otto. Design Principles for Industries 4.0 Scenarios. 49th Hawaii International Conference on System Sciences (HICSS), 2016, pp. 3928-3937, doi: 10.1109/HICSS.2016.488H. Kagermann. Recommendations for implementing the strategic initiative INDUSTRIE 4.0. Final report of the Industrie 4.0 Working Group acatech. National Academy of Science and Engineering, 2013. https://en.acatech.de/publication/recommendations-for-implementing-thestrategic-initiative-industrie-4-0-final-report-of-the-industrie-4-0-working-group/.A. C. Pereira y F. Romero. A review of the meanings and the implications of the Industry 4.0 concept. Procedia Manufacturing, 2017 vol. 13, pp. 1206-1214A. Cruz. Estructura de Control para Procesos de Producción desde el Paradigma de los Sistemas Holónicos de Manufactura. Universidad del Cauca, 2017.Chee-Yee Chong y S. P. Kumar. Sensor networks: evolution, opportunities, and challenges. Proceedings of the IEEE, 2003, vol. 91, n.o 8, pp. 1247-1256, ago. 2003, doi: 10.1109/JPROC.2003.814918.C. Bravo, J. Aguilar-Castro, A. Ríos, J. Aguilar-Martin, y F. Rivas. Arquitectura Basada en Inteligencia Artificial Distribuida para la Gerencia Integrada de Producción Industrial. Revista Iberoamericana de Automática e Informática Industrial RIAI, 2011, vol. 8, n.o 4, pp. 405-417, oct. 2011, doi: 10.1016/j.riai.2011.09.013.J. Aguilar, LB 2013 Sistemas MultiAgentes y sus Aplicaciones en Automatización Industrial_1.pdf, 2.a ed. Venezuela: Universidad de los Andes, 2013.J. Andramuño, N. Vega, y P. Parra. Industry 4.0 Embedded Systems Network. IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2019, pp. 1-6, doi: 10.1109/CHILECON47746.2019.8987620.L. Quintero. Un modelo de control inteligente para sistemas de manufactura basado en los paradigmas holónico y multi-agente. Escuela de sistemas, 2009.E. Hernandez, J. Alvarez, F. J. Valdes, y H. Puebla. An Integral Formulation Approach for Numerical Solution of Tubular Reactors Models. International Journal of Chemical Reactor Engineering, 2011, vol. 9, n.o 1, doi: 10.2202/1542-6580.2444.Leitão, Rodrigues, Turrin, y Pagani. Multiagent System Integrating Process and Quality Control in a Factory Producing Laundry Washing Machines. IEEE Journals & Magazine, 2015. https://ieeexplore.ieee.org/document/7104129.J. Palanca, E. del Val, A. Garcia-Fornes, H. Billhardt, J. M. Corchado, y V. Julián. Designing a goal-oriented smart-home environment, Inf Syst Front, 2018, vol. 20, n.o 1, pp. 125-142, doi: 10.1007/s10796-016-9670-x.Sun, Yu, Kochurov, Hao, y Hu. A Multi-agent Based Intelligent Sensor and Actuator Network Design for Smart House and Home Automation. Journal of Sensor and Actuator Networks, 2013, 2(3), 557-588Benitez-Pina, I. F., Lamar-Carbonell, S., Silva, R. M. D., Eigi-Miyagi, P., & Silva, J. R. Design of automatic control system based on unified timed hybrid Petri net. Dyna, 2017, 84(200), 80-89.J. R. Nuñez-Alvarez, I. F. Benítez-Pina, y Y. Llosas-Albuerne. Communications in Flexible Supervisor for Laboratory Research in Renewable Energy. In IOP Conference Series: Materials Science and Engineering, 2020, Vol. 844, No. 1, p. 012016.E. Mendoza, J. Andramuño, y L. Cordova. Intelligent flow and level sensors for a filtering system in a water treatment plant. 18th LACCEI International Multi-Conference for Engineering, Education and Technology, 2020.H. Berger, Automating with SIMATIC S7-1200: configuring, programming and testing with STEP 7 Basic Visualization with HMI Basic. John Wiley & Sons, 2013.H. Zhang. Design of Control System for Concrete Mixing Station Based on PLC and Touch Screen. Advanced Materials Research. Trans Tech Publications Ltd, 2011. p. 2473-2476.J. Rochez, E. B. B. Vinuela, M. Koutli, y T. Petrou, «Opening the Floor to PLCs and IPCs: CODESYS in UNICOS», p. 4, 2014.E. V. M. Merchán, P. G. F. Espinoza, I. F. B. Pina, D. R. Tabares, y J. R. N. Alvarez. Red de sensores inalámbricos multisalto para sistemas domóticos de bajo costo y área extendida. Revista Iberoamericana de Automática e Informática industrial, 2020, vol. 0, n.o 0, Art. n.o 0, abr. 2020, doi: 10.4995/riai.2020.12301.R. Loarte, B. Quizhpe, y H. Paz. Desarrollo y simulación de un sistema multiagente para la comunicación de semáforos para encontrar la ruta óptima mediante grafos. Revista Tecnológica ESPOL, 2015, vol. 28, pp. 43-63A. Cando. 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