Induction Furnace Virtual Temperature Sensor Based on Robust Adaptive Estimation

This paper focuses on the development of a virtual thermal sensor for estimating the metal charge temperature in an induction metal melting furnace. In foundry workshops, it is crucial to accurately measure the existing temperature of the metal charge. However, continuous temperature measuring instruments are typically unavailable for such harsh working operating conditions. The proposed method is used consistently to estimate the charge melting temperature using a single temperature sensor inserted in an easily accessible location in the furnace lining. The virtual sensor is created as a model-based dynamical observer, which means it employs a mathematical model to estimate the charge temperature based on the furnace's lining temperature measurement. The authors developed a Multiple Model Adaptive Estimator (MMAE) to assure the reliability of using the virtual sensor. This estimator is designed to account for uncertainties such as variations in initial conditions, power disruptions during the melting process, and variations in average charge temperature resulting from density differences. Therefore, this paper presents an efficient method for estimating the temperature of metal charges in an induction furnace using a virtual sensor, which can be a valuable tool in foundry workshops where continuous temperature measurements are not readily available.