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

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.