数据驱动的工业过程运行监控与自优化研究展望

With the development towards large-scale, continuous, and integrated modern industrial processes, it is essential to effectively monitor the plant-wide operations that cover decision, cooperative control, and base-level control. The operation monitoring has recently become an active area of research both in academia and industry. Firstly, the demanding work of operation monitoring and the current status in industrial area are analyzed in this paper. Secondly, the existing methods on model-based fault diagnosis and fault-tolerant control, and data driven abnormal situation diagnosis and self healing control are reviewed, while the opportunities are analyzed under cyber physical systems (CPS) circumstances. Finally, future research directions and recent progresses on the topic of operation monitoring and self-optimization of industrial processes are discussed, including: 1) data-driven multi-level comprehensive monitoring of decision, cooperative control, and base-level control; 2) multi-source dynamic information based abnormal situation diagnosis that combines first principles, process data, and expert knowledge; 3) cooperative self-healing control which combines expert knowledge and control strategy; 4) data driven dynamic performance analysis of process operation and self-optimization; and 5) technologies that implement operation monitoring and self-optimization system.