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Electrical and thermal analysis for the copper removal process in an electric furnace

Zybała Radosław, Golak Sławomir, Sak Tomasz, Madej Piotr

Computer Methods in Materials Science

2023

Vol. 23, No. 4

31--44

The article presents an electrical model of a resistance furnace with two electrodes encompassing the generation of Joule heat. The characteristic feature of this model was the consideration of contact resistance between the electrodes and the slag. A series of analyses were conducted based on this model. Firstly, the impact of contact resistance on current flow and Joule heat generation in the furnace was assessed, demonstrating its significant importance. A separate group of analyses focused on the spatial configuration of the furnace and its interaction with the aforementioned phenomena. The impact of symmetric and asymmetric electrodes immersion was analysed. In addition to the impact on current flow, the study also demonstrated the influence on the natural convection mechanism described by the proposed measures of the spatial non-uniformity of heat generation. The research showed that symmetric electrode immersion allows for the generation of more heat in the system at a constant voltage. Asymmetric electrodes immersion causes an increase in the non-uniformity of heat generation, which translates into a higher intensity of natural convection.

Application of adaptive controller neural network based on RBF NN for temperature control electrical resistance furnace

Kada Benyekhlef, ElKebir Abdelkader, Negadi Karim, Belhadj Hafida, Chaouch Djamel Eddine

Przegląd Elektrotechniczny

2020

R. 96, nr 6

33--38

In order to solve the problem for temperature electrical resistance furnace. Characterized by their large inertia, nonlinear, long time delay and time-varying property it is rather difficult to obtain satisfactory control results with Performances of conventional PI control cannot achieve good control effect. In this paper a neural network-based adaptive control approach (ACNN) for electrical furnace is developed .using RBF NN to estimate the unknown functions by neural networks and from good choice of the law of adaptation. Based on the resolution of the lyapunov equation. Taking account of all possible parameter variations the adaptive control is designed so that it has the ability to improve the performance of the closed loop system, producing the control signal by using the information from the system. In this case we use a coping mechanism that observes the signal to control and adjust the synaptic weights of neural networks when system parameters change over time. Result shows that the proposed algorithm (ACNN) performs very well when furnace parameter varies the latter allow the neural model to be identified online and, if necessary its parameters to be stabilized and it is very easy to program it online.

Piec elektryczny charakteryzuje się nieliniowością, dużym czasem opóźnienia co utrudnia sterowanie nime. W pracy zaproponowano system sterowania piecem z wykorzystaniem sieci neuronowej System jest zaprojektowany tak, że uwzględnia zmiany parametrów.