Sintering Heat Treatment Process Modeling And Control Based On Data-drive

Sintering is important for iron-making industry. As an essential pre-process of blast furnace materials, sintering could minimize the fluctuations of the materials, and thus guarantee the steady operation of furnace. With the rising price of iron ore and public enhancing consciousness of energy saving and environment protection, it is imperative to improve the technology of iron making in terms of saving energy and reducing cost. In this technical innovation, the sintering process has attracted more and more attention as it plays an important role. Therefore, it is meaningful to study the iron ore sintering process.The thesis focuses mainly on temperature control of sintering ignition oven and modeling of sintering heating process. We adopt PIDNN (Proportional-Integral-Derivative Neural Network) control algorithm to control ignition oven temperature, based on the idea of establishing time-series data unit, fulfill the modeling of thermal state of sintering process with the help of fuzzy weitghted multi-model and then implement the control of process by fuzzy control algorithm.In detail, the major contributions of this thesis are summarized as follows:(1) PIDNN control algorithm is proposed to control ignition oven temperature. Regardless of the frequent fluctuations of pressure and calorific value of gas, with the help of neural network's self-learning behavior, the PIDNN control system turns out that the temperature fluctuates accurately within requirements.(2) An idea of establishing time-series data unit is employed. We divide the sintering process into data units using data-driven method, from the mixing of ingredients to the crushing of burned sinter. The sintering machine is divided into 16 data units. All the sintering parameters will be recorded for each data unit including ignition temperature, material thickness, pressure and temperature of box while each unit runs from start to end.(3) A fuzzy weighted multi-model method to build prediction model of sintering process is developed. We model the process by BPNN and GRNN respectively, and combine the results by fuzzy weighted method to build a new predict model.(4) We extract the recorded parameters of sintering process operated in ideal condition to build the ideal operation data set. With this data set we use fuzzy weighted multi-model algorithm to calculate the ideal setting of box temperature. After we get the goal setting of temperatures, we utilize fuzzy rules to control each box temperature by each box pressure.