Chen Xuling

Real-time operation guide system for sintering process with artificial intelligence

In order to optimize the sintering process, a real-time operation guide system with artificial intelligence was developed, mainly including the data acquisition online subsystem, the sinter chemical composition controller, the sintering process state controller, and the abnormal conditions diagnosis subsystem. Knowledge base of the sintering process controlling was constructed, and inference engine of the system was established. Sinter chemical compositions were controlled by the strategies of self-adaptive prediction, internal optimization and center on basicity. And the state of sintering was stabilized centering on permeability. In order to meet the needs of process change and make the system clear, the system has learning ability and explanation function. The software of the system was developed in Visual C++ programming language. The application of the system shows that the hitting accuracy of sinter compositions and burning through point prediction are more than 85%; the first-grade rate of sinter chemical composition, stability rate of burning through point and stability rate of sintering process are increased by 3%, 9% and 4%, respectively.

Operation Guidance System for Iron Ore Sintering Process

An operation guidance system for iron ore sintering process was developed, according to the characters of sintering plant in China. The sintering process is divided into three stages that(which are the ) forming, moving and disappearing of reaction zone. The control strategy integrated soft measurement, prediction and fuzzy control technology was put forward on the basis of analysis to mechanism of sintering process. Change of permeability in first stage was judged using integrated fuzzy judge method, and TRP and BTP were soft-measured according to temperature of waste gas. Predict model of BTP was build by time-list method. The self-adaptive fuzzy control algorithm was achieved by auto-center, non-linear quantification, self-adjust rules and so on. Industrial application of this system shows that the fluctuation of main status parameters can be fell down 18%~29%.