Modeling and control of an electric arc furnace

Model Based Predictive Control is a class of computer algorithms that explicitly use a process model to predict future plant outputs and compute an appropriate control action through on-line optimization of a cost objective over a future horizon, subject to various constraints. The cost function is defined in terms of the tracking error (the difference between the predicted output and set-point). Using this scheme, many different MBPC algorithms have been proposed in the literature. This paper presents an adaptive-predictive control algorithm, which uses on-line simulation and rule-based control. The algorithm is applied to an electrode position system of an electric arc furnace. Electric arc furnaces are commonly used in steelmaking and in smelting of nonferrous metals. To obtain the electric arc, usually there are used three graphite electrodes. The power level depends by the positions of the electrodes. As a result, the realization of a competitive control system is very important because it led to reduction of the energy consumption, pollution, and increases the safety of the process.