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Interoperating data-driven and model-driven techniques for the automated development of intelligent environmental decision support systems

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JPascual_etal_InteroperatingData-ModelDrivenTechIPCSWWTPs-EMS-postprint.pdf (1,800Mb)
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10.1016/j.envsoft.2021.105021
 
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Pascual Pañach, JosepMés informació
Cugueró Escofet, Miquel ÀngelMés informacióMés informació
Sànchez-Marrè, MiquelMés informacióMés informacióMés informació
Document typeArticle
Defense date2021-06
PublisherElsevier
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 4.0 International
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 4.0 International
Abstract
This paper proposes an Intelligent Decision Support (IDS) methodology based on the integration of a data-driven technique —Case Based Reasoning (CBR)— and model-driven technique —Rule Based Reasoning (RBR)— for control, supervision and decision support on environmental systems. Design stage of control and decision support tools for environmental systems tend to be somehow ad-hoc regarding to the nature of the processes involved. Hence, an automated approach is proposed for the sake of scalability to different types and configurations of environmental systems. The proposed hybrid scheme provides complementarity in the set-point generation for the process controllers, increasing the reliability of the Intelligent Process Control System (IPCS), which is the core component of the IDS methodology. Furthermore, the IDS methodology is flexible and dynamic enough to be able to cope with the dynamic evolution of environmental systems, learning from its relevant experienced situations. The approach presented has been implemented in a real facility.
CitationPascual, J.; Cugueró, M.A.; Sànchez-Marrè, M. Interoperating data-driven and model-driven techniques for the automated development of intelligent environmental decision support systems. "Environmental modelling & software", Juny 2021, vol. 140, article 105021, p. 1-16. 
URIhttp://hdl.handle.net/2117/347174
DOI10.1016/j.envsoft.2021.105021
ISSN1364-8152
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S1364815221000645
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  • Doctorat en Intel·ligència Artificial - Articles de revista [26]
  • Departament de Ciències de la Computació - Articles de revista [954]
  • KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic - Articles de revista [112]
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