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

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 —Rule (RBR)— for control, supervision decision support environmental systems. Design stage control tools systems tend to be somehow ad-hoc regarding nature processes involved. Hence, automated approach is proposed sake scalability different types configurations The hybrid scheme provides complementarity in set-point generation process controllers, increasing reliability Process Control System (IPCS), which core component IDS methodology. Furthermore, flexible dynamic enough able cope with evolution systems, learning from its relevant experienced situations. presented has been implemented real facility.