ABSTRACT
This research proposes a new index to evaluate the stability of the melting process, in three-phase electric arc furnaces (EAFs), based on the acoustic signals generated during the different stages of the casting. The proposed stability index is obtained by characterizing the time and frequency domain of the acoustic signals. During EAF monitoring, acoustic signals were acquired using a microphone coupled to an NI USB-9234 acquisition system. To validate the results, the voltage and current signals were measured with the aid of a Circutor AR6 power analyzer for three-phase electrical networks. The results showed that the acoustic signal energy in the frequency range of 1 to 12 kHz can be used as an indicator of the process stability in the EAF. Finally, the validity of the proposed stability index is evaluated from the process characterization using the harmonic distortion analysis methods and the dynamic U-I characteristics of the arc voltage and current signals. The results obtained demonstrated the effectiveness of the proposal and constitute a starting point for advances in the automatic control of the process in the EAF, from the acoustic signals.
ABSTRACT
This article presents a sliding mode controller that uses a generalized predictive controller in the reaching mode. The proposed predictive sliding mode controller is developed from a first-order-plus-deadtime model that represents a good approximation to many chemical processes. The predictive sliding mode controller has six tuning parameters and the tuning rules are given in the paper. Four simulation examples show the features of the proposed controller, which overcomes some of the disadvantages of sliding mode control and generalized predictive control strategies.