2017 Volume 57 Issue 1 Pages 131-138
In the steel works, direct observation of the internal states of many processes, such as the blast furnace, is difficult. Automation of such processes based on process visualization is an urgent issue. Because the number of sensors is limited, the state estimation utilizing partial sensor information is necessary. We developed a technique which visualizes the entire temperature distribution of a shaft furnace by means of the particle filter, which combines the sensor information and a nonlinear model calculation. This state estimation was incorporated in the heat pattern control logic based on future prediction, in which the estimated heat pattern is set as the initial condition. The control logic was implemented in a ferro-coke pilot plant. As a result, the control accuracy of 10°C was achieved. Furthermore, the operational condition was adjusted based on the correlation between the estimated heat pattern and the product strength. In consequence, the product strength improved by 0.5 points (Drum Index 150/15 mm, DI15015).
