The Iterative Optimizationof Thepower-supply Curve For Electric Arc Furnace

The cost and efficiency of steel-making directly affect the economic profits of iron and steel enterprises.In steel-making process with Electric Arc Furnace(EAF), energy saving is very important. An effective power supply strategy will not only ensure smooth operation, but also help reduce power consumption, electrode loss, furnace wall corrosion and shorten tap-to-tap cycle, which will bring in great profits.On the basis of consulting large amount of literatures on relative subjects at home anda-broad, the thesis introduces the crafts, equipment history of electric arc furnace and empha-sizes onthe present state and perspective of control techniques of EAR With the electrical characteristics of EAF, its non-linear reactance model is regressed and analyzed according to field data. And by in-depth research on electrical and thermal characteristics and energy bal-ance of the smelting process of EAF, a power supply model of EAF is established, whose tar-get is to minimize power consumption, tap-to-tap cycle, electrode loss and consumption of refractory material, achieving power supply strategy optimization as a whole. In addition, this thesis puts forward the comprehensive economic evaluation index.The power supply model of EAF is a complicated multi-objective mixed integer nonli-near problem with constraints. Because parts of model parameters cannot be obtained, the model will not be directly used for optimization.In order to solve the problem of unknown parameters and optimize the model, this thesis puts forward Two-step iterative optimization algorithm.The first stage aims at determining the unknown model parameters, which will be iteratively updatedby using the model parameters adaptive method combined with EAF steelmaking history information.In the second stage, the target is to solve the updated power supply model. In contrast to the traditional optimization method and intelligent optimization algorithm for solving this kind of problem of the advantages and disadvantages, the thesis uses based-on adaptive grid and crowding distance of particle swarm algorithm (AGC-MOPSO) to solve the power supply model.The advantage of two-step iterative optimi- zation algorithm is to make power supply model more close to the actual situation of steel-making through updating the parameter iteratively. In addition, the optimization model can obtain the global optimal solution by applying intelligent algorithm. Therefore, power supply curve can be effectively applied to the actual field.Finally, taking a steel 40-ton EAF of some steelmaking plant for example, update un-known model parameters by the EAF steelmaking experience data. Then apply AGC-MOPSO algorithm to solve the EAF power supply model and obtain stage and the whole process of power supply strategy. By analyzing the simulation results, a reasonable power supply curve is established.