| Optimization of quantitatively dynamic feeding control system is an important part of the process control of ladle furnace (LF). To feed all materials accurately in time is the guarantee of the quality of product, and the important method to reduce cost. So the study of efficient and reliable quantitatively dynamic feeding control system has its special significance.Considering the structure of the system, the control of asynchronous motor and vibration feeding are analyzed to meet the demands of the quantitatively dynamic feeding control system. The dynamic model is established for this control system based on Transfer Function method.A method based on Iterative Learning Control (ILC) is used for the nonlinear, time-varying system with big inertia. By iterative learning, the control system can solve the problems of feeding speed and feeding accuracy. By simulation of this system, it is proved that this system is made for not only accurately quantitative control, but also meeting the demands of the most of feeding system.A linear programming model is established to calculate the optimum alloy additions in order to meet the control demand during the LF steelmaking process. But the accuracy of model depends on the element yield and the steel weight which are very difficult to be obtained in the process of LF steelmaking process. Depending on analysis the factors which influence the element yield and steel weight, an estimate model for element yield is established upon grey model in this thesis. By simulation, the method provides accurately control of the composition of the steel in the LF steelmaking process.In the end, on the basis of summary the whole research work, the trend for the future development of LF feeding system is proposed. |
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