Abstract: Methods and systems for determining a feed rate (unit mass/unit time) of metallic scrap material in real time being charged to an electric arc furnace (EAF) is provided, in which the methods and systems determine the speed of the metallic scrap material in real time and the volume of the metallic scrap material in real time. The methods and systems also classify the metallic scrap material via a machine learning model based on digital images of the metallic scrap material and assign a density to the metallic scrap material. The feed rate is determined based on the speed and volume of the metallic scrap material and the assigned density.

Abstract: Methods and systems for determining a respective mass associated with respective portions of the respective layers of metallic scrap material deposited into a charging-bucket associated with a batchwise-charged electric arc furnace (EAF) are provided, in which the methods and systems determine the respective masses associated with the respective portions of the respective layers of metallic scrap material based on (a) the respective volume of the respective portions of the respective layers of metallic scrap material and (b) the respective assigned densities assigned by a machine learning classification model based on digital images of the respective portions of the respective layers of metallic scrap material.

Abstract: A system and method for monitoring consumption of graphite electrodes during the operation of an electric arc furnace (EAF) uses machine vision cameras operatively communicating with a computer processor. The system can determine, track, manage, and optimize the consumption of the graphite electrodes in real time. Electrode consumption is determined for each EAF heat by measuring the length and tip diameter of the electrode. The length and tip diameter are used to determine the electrode consumption amount using a consumption model. Measured hydraulic pressure within the EAF correlating with a known electrode weight can also be used to determine electrode consumption and correlated with the model calculation. Butt loss can also be determined based on the machine vision measured lengths of the electrode and/or based on the hydraulic pressure. The calculated electrode consumption amounts are also stored in a database and correlated to other measured EAF parameters for multiple EAFs.