2026-04-17T02:50:01Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/3471742026-01-21T09:47:26Zcom_2072_1033col_2072_452950

Interoperating data-driven and model-driven techniques for the automated development of intelligent environmental decision support systems Pascual Pañach, Josep Cugueró Escofet, Miquel Àngel Sànchez-Marrè, Miquel Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic Decision support systems Sewage Case-based reasoning Data mining Rule-based reasoning Intelligent environmental decision support system Intelligent process control Wastewater treatment plant Sistemes d'ajuda a la decisió Aigües residuals Raonament basat en casos Mineria de dades 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. The authors acknowledge the partial support of this work by the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government. Peer Reviewed Postprint (author's final draft) 2021-06 Article https://www.sciencedirect.com/science/article/pii/S1364815221000645 info:eu-repo/grantAgreement/AGAUR/V PRI/2017 DI 006 info:eu-repo/grantAgreement/AGAUR/RIS3CAT/2017 SGR 574 © 2021 Elsevier https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en Open Access Attribution-NonCommercial-NoDerivatives 4.0 International Elsevier