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.

本文提出了一种智能决策支持（IDS）方法，该方法基于数​​据驱动技术-基于案例的推理（CBR）和模型驱动技术-基于规则的推理（RBR）的集成，用于控制，监督和决策支持。环境系统。考虑到所涉及过程的性质，针对环境系统的控制和决策支持工具的设计阶段往往是临时的。因此，为了可扩展到环境系统的不同类型和配置，提出了一种自动方法。所提出的混合方案在过程控制器的设定值生成中提供了互补性，从而提高了智能过程控制系统（IPCS）的可靠性，该系统是IDS方法论的核心组成部分。此外，IDS的方法具有足够的灵活性和动态性，能够从环境相关的经验中学习，以应对环境系统的动态演变。所介绍的方法已在实际工厂中实施。