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International Workshop on Soft Computing Models in Industrial and Environmental Applications

SOCO 2020: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) pp 374–382Cite as

Demand Control Ventilation Strategy by Tracing the Radon Concentration in Smart Buildings

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

Abstract

Ensuring air quality should be a mandatory premise in every building, since if not, its occupants are on high risk. In fact, Radon pollutants are stated to be the second main cause among all lung cancer patients in the United States. Radon is a noble gas which seeps up through the ground and accumulates there, making it hard to be identified. A proper ventilation system needs to be installed on industrial plants so that the Radon exhaled from building materials is properly dispelled, ensuring fresh, quality air. In order to keep a proper air quality level in smart buildings, a control ventilation strategy should be defined so that the exhaled Radon is ensured to be dispelled keeping the indoor air quality high. In the proposed paper, the diffusion-advecntion method has been studied in order to propose a solution on Radon concentration tracing on smart buildings ventilation system. Diffusion-advecntion is a mathematical method that will determine whether Radon will propagate or not, based on the concentration of Radon, the diffusion constant and the advecntion velocity of the indoor air, which can lead to a recommendation for the smart building ventilation system to be activated or not, respectively. In this paper a new ventilation strategy for smart buildings based on the Diffusion-advecntion equation has been proposed to improve air quality. The results of this new ventilation strategy have been tested in a real case study in a smart building in the city of Salamanca. The main outcome of this new strategy is the improvement in response times of the current systems.

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Acknowledgements

This work was developed as part of “Virtual-Ledgers-Tecnologías DLT/Blockchain y Cripto-IOT sobre organizaciones virtuales de agentes ligeros y su aplicación en la eficiencia en el transporte de última milla”, ID SA267P18, project cofinanced by Junta Castilla y León, Consejería de Educación, and FEDER funds.

Author information

Authors and Affiliations

  1. BISITE Research Group, University of Salamanca, 37008, Salamanca, Spain

    Roberto Casado-Vara, David García-Retuerta, Alvaro Bartolomé & Juan M. Corchado

  2. Department of Industrial Engineering, University of A Coruña, Ferrol, A Coruña, Spain

    Esteban Jove & Jose Luis Calvo-Rolle

  3. Department of Applied Mathematics, University of Salamanca, Calle del Parque 2, 37008, Salamanca, Spain

    Angel Martin-del Rey

  4. Department of Applied Mathematics, Institute of Fundamental Physics and Mathematics, University of Salamanca, Calle del Parque 2, 37008, Salamanca, Spain

    Angel Martin-del Rey

  5. IoT Digital Innovation Hub (Spain), Air Institute, Calle Segunda 4, 37188, Salamanca, Spain

    Juan M. Corchado

Authors
  1. Roberto Casado-Vara
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  2. David García-Retuerta
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  3. Alvaro Bartolomé
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  4. Esteban Jove
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  5. Jose Luis Calvo-Rolle
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  6. Angel Martin-del Rey
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  7. Juan M. Corchado
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Corresponding author

Correspondence to Roberto Casado-Vara .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Casado-Vara, R. et al. (2021). Demand Control Ventilation Strategy by Tracing the Radon Concentration in Smart Buildings. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_36

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